But despite all the innovation, one thing hasn’t changed: the need for human empathy.

This blog explores the increasingly critical balance between artificial intelligence and emotional intelligence in data visualization. Because while machines can speed up delivery, only people can deliver meaning.

Where AI fits in

AI has unlocked enormous value in the world of data storytelling. It can handle vast datasets, spot trends faster than any human, and eliminate hours of manual chart-making. For insight teams under pressure to deliver more with less, it’s a game-changer.

Some of AI’s greatest strengths include:

• Pattern detection: AI can surface interesting correlations, anomalies, and outliers that would take humans hours (or days) to find manually
• Automating the repetitive stuff: From chart creation to labeling and tagging, AI handles the boring bits so humans can focus on strategy
• Smart suggestions: Many tools now recommend charts, layouts, or visualizations based on the type of data being used
• Scalable personalization: AI can help tailor dashboard content to different users, learning preferences, and behaviors over time

These strengths are particularly powerful when paired with real-time data environments or high-volume datasets – areas where automation is not just helpful, but essential. In these scenarios, AI acts as an accelerant, driving efficiency and freeing up human capacity.

Find out more: Sign up for our latest demo, Meet your research agents, on Insight Platforms.

The limits of AI in storytelling

But for all that AI can do, there are meaningful gaps it simply can’t bridge.

• It lacks human context: AI can help you get closer to the ‘so what’ of the data provided, but it doesn’t understand the wider business context that humans will be immersed in
• It can’t sense tone or timing: AI can’t know when an insight is potentially sensitive, or when a team might not be ready to hear a tough truth
• It can’t detect bias: AI systems are only as unbiased as the data they’re provided. Without careful design, dashboards can end up replicating – or even amplifying – biases in the data that may not be relevant
• It misses the emotional resonance: AI can pick out the most important bits of information but can’t craft a narrative that will capture the emotions of those consuming the data

In short, AI is brilliant at the mechanics of storytelling – but it struggles with the next level of meaning.

Empathy is the human advantage

And that’s where we come in.

Human-centered dashboards are built with curiosity, lived experience, and ethical awareness. They’re shaped by the kinds of questions only people ask:

• What does this data really mean?
• Why should anyone care about this?
• What action do we want to inspire?

Empathy is more than just a soft skill. It helps us understand how different people will interpret the same chart, choose colors or language that feel accessible and respectful, and even sense when an insight could cause confusion, fear, or resistance.

In our Art & Science of Data Visualization ebook, we talk about how the best dashboards create emotional connections. That’s not something you can automate. It’s something you design for – with intent, compassion, and a deep understanding of your audience.

Read more: Art & Science of Data Visualization

AI and empathy = the dream team

The good news? This doesn’t have to be an either/or situation. In fact, it absolutely shouldn’t be.

AI and empathy aren’t rivals. They’re collaborators. And when you join the speed of AI and empathy built from years of human experience, your dashboards become exponentially more powerful. As our ebook, The Art and Science of Data Visualization, puts it:

“Machines will accelerate analysis, automate repetitive tasks, and surface patterns, but humans will continue to shape the narrative, provide context, and ensure that insights remain trustworthy, relevant, and empathetic.”

Here’s what that looks like in practice:

• AI finds the pattern; human decides whether it’s relevant, useful, or actionable
• AI recommends the layout; human adjusts based on stakeholder knowledge and emotional tone
• AI and automation translates data to visuals; human checks for bias, clarity, and resonance

It’s a bit like cooking with a sous-chef: AI can prep the ingredients, but you still need a human to taste, season, and plate the final dish.

• Read more: Incredible dashboard design principles that make data land
• Read more: Transform data visualization: Speak the language of leadership
• Read more: Boost data impact: Simplifying your visualizations

Keep the human in the loop

As AI gets more advanced, it’s easy to be dazzled by what it can do. But the best insight stories – the ones that shift strategy, spark ideas, and bring people together – are still shaped by people who understand people.

So yes, lean into AI. Use it to do the heavy lifting. Let it surface the interesting stuff. But don’t forget the empathy, context, and storytelling magic that only humans can bring.

Because in the end, data doesn’t drive decisions. People do.

Discover more about what it means to use AI for visualizations by exploring our solutions.

What market research teams do need are tools that fit the reality of the work: The deadlines, the complexity, and the constant pressure to do more with less. That may sound obvious, but in practice, plenty of platforms still expect researchers to bend to the software, rather than creating platforms that meet researchers’ needs straight off the bat.

So, what does it really take to build for market research?

We spoke to Debi Hart, VP Product Manager at Forsta, about the thinking behind Research HX, how development decisions get made, and why staying close to real research workflows matters so much.

What ‘built for market research’ really means

When Forsta says it’s ‘built for market research’, what does that look like in practice?

At its core, it comes down to understanding the work itself.

All companies rely on customer feedback, but we take it a step further.  Forsta’s solutions are specifically designed to support agencies and in-house insights teams, shaped by a deep understanding of the workflows behind how research gets done. That level of domain expertise doesn’t sit in one team either.

Our client support team, tech support team, and even account managers have over 10 years of experience in Market Research – many of them coming from the client side. Our platform engineers also have years of experience with our products and MR. The result is a platform that isn’t trying to retrofit research into a generic system, but one that’s built for market research around the realities of the job.

Where do ideas come from, and how do they move from suggestion to something that’s built?

Ideas come from everywhere: Clients, internal market research teams, product owners, and engineering. But the most valuable ideas tend to come from collaboration – taking those inputs and refining them into something that can deliver real value.

Once ideas are shaped, they’re prioritised in partnership with market research leadership. Roadmaps are developed and then shared with key clients to sense-check direction: What resonates, what’s missing, and what would make the biggest difference in practice.

There’s also a more structured way of capturing input. Client-facing teams gather feedback continuously, and those suggestions are fed into a crowdsourcing tool where the wider organisation can vote on what matters most. The highest-priority ideas are then pulled into future roadmaps.

Not everything can get built, but the goal isn’t to meet every individual request; it’s to create the most value for the largest number of researchers.

A lot of companies talk about feedback loops. What does that look like in practice?

Feedback is only useful if it leads to something actionable. At Forsta, that means combining external input from clients with internal expertise from teams who understand market research deeply. Ideas are validated internally, then put in front of clients as early as possible to test how they perform in real-world scenarios.

That iterative loop – build, test, refine – continues until a product is not just technically complete, but genuinely useful in practice. In other words, when we get to ‘done-done’. It might sound like a subtle distinction, but it’s the difference between simply releasing a feature and knowing it actually works in the context it was designed for.

Many platforms expect researchers to adapt their workflows. Forsta has taken a different approach – why?

Because the work is already complex enough. Research teams are juggling multiple projects, stakeholders, and timelines. Asking them to fundamentally change how they operate just to fit a tool creates more friction than it removes.

Instead, the focus is on understanding how clients already work and then building solutions that make those workflows more efficient. That understanding comes from staying close to the industry.

Our teams regularly engage with clients, attend conferences and webinars, and follow emerging trends to ensure the platform continues to reflect how research is evolving. The aim isn’t to redesign the way researchers work. It’s to make the existing workflow smoother, faster, and more intuitive.

Forsta has a lot of market research expertise embedded in its teams. Does that genuinely change the outcome?

Absolutely! Having people involved in product development who’ve worked on the operational side of market research brings a different level of understanding. They know what good looks like, where things typically go wrong, and where efficiencies can be gained without sacrificing control or methodological rigor. That balance is important.

It’s not just about making processes faster. It’s about making them more efficient while still giving researchers the flexibility and precision they need to do their jobs properly. It’s hard to do that without understanding the realities of research.

There’s an ongoing debate in software between tech-first and product-first. Where does Forsta sit?

Forsta is firmly product-first. That means technology is used as an enabler. New capabilities are only developed if there’s a clear benefit for the end user.

We’re not interested in building cool technology for its own sake. If it doesn’t fit into our product strategy or make a researcher’s jobs easier, it doesn’t get built.

This approach is particularly important as new technologies – including AI – continue to evolve. Our focus will always remain on how those capabilities can be applied in a way that adds value within real workflows, rather than chasing innovation for the sake of it.

You’ve worked across different organisations. What stands out about how Forsta builds?

Two things really stand out. The first is focus. Having leadership support to stay aligned on priorities makes a big difference. In many organisations, shifting priorities can make it difficult to deliver anything meaningful. At Forsta, there’s a clear direction, which allows teams to follow through on what they set out to do.

The second is collaboration. There’s a strong sense of shared ownership across teams, with people working together to improve the product rather than operating in silos. It’s less about individual ownership and more about shared goals. We leave egos at the door, we have fun, and we deliver meaningful value for our customers.

Why this matters for research teams

As research teams face increasing pressure to move faster, handle more data, and deliver clearer insights, the tools they rely on are under more scrutiny than ever.

The difference isn’t just in what platforms can do, but in how they’re built – and whether they genuinely reflect the realities of the work researchers do every day. The most effective research environments reflect this – connecting data, automation, and human expertise in workflows that keep researchers firmly in the loop.

Building for market research isn’t about technology alone; it’s about understanding the work well enough to make it better.

Debi Hart, VP Product Manager

Debi Hart leads the development of Research HX at Forsta, a seamless, integrated platform designed specifically for market researchers. With over 20 years of experience in technology, product development, and market research, Debi has been at the forefront of incorporating AI into cutting-edge tools that empower researchers to unlock deeper insights and drive meaningful action.

Discover more about what it means to use tech built for market research by exploring our solutions.

In fact, studies show that the human brain can process images as quickly as 13 milliseconds. So you want them to get the right first impression. Clutter kills comprehension. Clarity creates conviction.

Why less is more

Clarity seems obvious until you try to design it.

Most dashboards don’t set out to overwhelm, but in the name of ‘just in case’, it’s easy to cram every possible chart, metric and filter into a single screen. After all, if the data’s there, someone might need it. Right?

Maybe. But maybe not all at once.

One of the most important principles of dashboard design is deceptively simple: make it easy. That means guiding the user’s eye. Prioritizing what matters most. Giving insight room to breathe. Not dumping a whole load of data and calling it a day.

Because when your dashboard feels easy to use, users feel more confident. More engaged. More likely to act. And that’s the whole point.

And the stakes are real: Gartner reports that poor data literacy is one of the top challenges inhibiting data asset success.

The principles of simplifying your visualizations

So, what does clarity actually look like?

1. Visual hierarchy: Use layout, size, and color to guide attention. What’s the story? What’s the takeaway? Put those front and center – not buried in tab 12
2. Intentional whitespace: Don’t cram every pixel with content. Space helps your most important insights to stand out, while giving the user’s brain a breather
3. Progressive disclosure: You don’t have to show everything at once. Reveal complexity only when it’s needed. Start with the headline insight, then let users dig into the detail if they want it
4. Reduce chart junk: Drop the shadows. Lose the 3D pie charts. Get rid of anything that doesn’t help the user understand the story. Clean visuals beat flashy ones every time
5. Design for scannability: Use bold headers, grouped sections, and consistent formatting so users can find what they need at a glance. The goal is insight in seconds – not minutes of decoding

Together, these techniques turn a cluttered dashboard into a clean, compelling one – the kind that people actually want to use.

This is exactly where tools like Research Agent come into play. Instead of relying on manual reviews or subjective opinions, teams can now pressure-test their dashboards in seconds, ensuring every visual earns its place and every insight is crystal clear.

Clarity = empathy (and that means emotional impact)

Data visualization is about emotion as much as information. People don’t just read dashboards; they react to them. A cluttered dashboard can make users feel anxious, confused, or like they’re failing at understanding something they’re ‘supposed’ to get.

A clear dashboard, on the other hand, says you’ve got this. It builds confidence, trust, and momentum.

This is empathy in action, and simplifying your visualizations is how you deliver it. By removing noise and focusing attention, you’re showing respect for your users’ time, energy and cognitive load. You’re saying: “I see you. I know what you need. Let me make that easier.”

And that emotional response – that feeling of clarity – is what helps insight land harder.

But I need all this data… right?

Totally fair. Sometimes your audience really does need detail.

But simplifying your visualizations doesn’t mean stripping your dashboard bare. It means designing for focus first, and flexibility second.

If you’re scratching your head, here are some easy ways to balance completeness with clarity:

• Smart defaults: Show the most common or important view by default, with filters to explore more
• Tabs or sections: Break up content logically, instead of stacking everything on one screen
• Drill-down paths: Let users click into detail when they want it, but don’t force everyone to start there

These aren’t hacks; they’re simply part of progressive disclosure – helping users to navigate complexity on their own terms. And when done right, they make everyone’s life easier.

Research Agent reinforces this approach by acting as a built-in reviewer. It assesses dashboards based on clarity, credibility, and decision-readiness, highlighting where too much detail muddies the message and where simplification strengthens the story.

Instead of second-guessing what to cut or keep, teams get instant, research-backed recommendations on layout, narrative strength, and visual design. The result: fewer review cycles, faster delivery, and insights that actually stick.

Cut the noise, increase the impact

If you take one thing from this blog, let it be this: simplicity isn’t basic. It’s brave.

Creating a clear, focused dashboard takes more than design skill. It takes empathy. It takes restraint. And it takes the confidence to leave out what doesn’t serve the user – even if it looks pretty darn cool.

But when you effectively nail simplifying your visualizations, you’ll start seeing something else:

• Users who understand your insights the first-time round
• Teams who feel empowered, not overwhelmed
• Decisions made faster, and with more conviction

So next time you’re building a dashboard, remember: clarity = impact. And impact is what makes all of the data wrangling worth it.

Ready to turn clutter into clarity?

If your dashboards are doing more confusing than convincing, it’s time to rethink the way you design, review, and refine your insights.

Research Agent helps you bridge the gap between data and decision by ensuring every dashboard is clear, credible, and ready for action. No guesswork. No bottlenecks. Just better insights, faster.

Find out how Research Agent can transform your reporting and visualization workflows here.

In the world of market research, a beautifully designed data visualizations means nothing if nobody understands it – or worse, nobody uses it. And yet, so many teams fall into the trap of designing for data, not people.

If you want your data to have an impact, you need to start by knowing who it’s for. In other words, data visualization design isn’t just about what you want to show – it’s about what your users need to see.

This blog blends classic data design principles with modern thinking from Forsta’s ebook, The Art and Science of Data Visualization, to give you a fresh, human-centered take on usability.

Read more: The art and science of data visualization: Turning numbers into narratives

Start with the person, not the platform

Before you touch a chart or drag in a dataset, you need to define who you’re designing for. Are you building a snapshot for a busy CMO? A detailed view for a data analyst? A quick-glance summary for a field sales team? Each of these roles will have wildly different needs, levels of data literacy, and time constraints.

The trick is to treat your audience like personas – because once you understand their goals, motivations, and limitations, you can design with empathy. It’s a reminder that how people feel when using your data visualizations – overwhelmed, frustrated, confident, curious – will influence how (and whether) they act on the insights.

Why data visualization falls flat

When dashboards or presentations fail, it’s rarely because of bad data. It’s because they weren’t built for the people who actually need to use them.

Too often, data visualizations are designed around what’s technically possible, not what’s practically helpful. The result? Beautiful charts that no one looks at. Cluttered screens no one understands. Reports that leave stakeholders with more questions than answers.

We’ve all seen dashboards or presentations that try to be everything at once: loaded with KPIs, jammed with filters, riddled with competing charts. They might look impressive at first glance, but they don’t help anyone actually do anything.

Some common missteps:

• Designing for data completeness rather than clarity: Trying to include every datapoint often leads to visual overload and cognitive fatigue
• Prioritizing visual flair over functional flow: When design dazzles but distracts, insight gets lost in the noise
• Cramming too many metrics into one screen: Even well-labeled charts become white noise if there are too many to digest
• Failing to consider user context or environment: A beautiful dashboard that’s unreadable on a tablet or unworkable for a remote team huddle helps no one
• Ignoring role-specific needs or data literacy levels: The same data presented to a CFO and a customer service agent should look and feel very different
• Assuming interactivity equals usability: Just because a dashboard has filters and toggles doesn’t mean users will know how to use them

These traps are easy to fall into, especially when there’s pressure to show everything. But more data doesn’t mean more insight. In fact, it often means more noise.

The fix? Start with your core audience. Ask what they need to know in order to act. Then remove anything that doesn’t serve that need.

Less isn’t just more. It’s more usable, more helpful, and more likely to land.

Read more: Incredible dashboard design principles that make data land

Design for different minds

Let’s say your data visualizations will be shared across departments – from execs to researchers to frontline teams. You can’t assume a one-size-fits-all view will work.

The solution is audience segmentation. Not just for your research participants, but for your dashboard users too. Here’s how:

• Executives: Want high-level summaries, trends, and red flags. Think headlines, callouts, and one-click access to detail (if they ever need it)
• Analysts: Want depth. They’ll benefit from drill-downs, raw data access, and customizable filters
• Operational teams: Want relevance. Give them what affects their patch, product, or customer group

Democratization doesn’t mean dumbing down. It means making data accessible in the way that makes most sense to the user. That means providing:

• Tailored entry points based on role
• Role-based permissions (so users aren’t overwhelmed)
• Consistent design language to reduce cognitive friction

The goal is to create a dashboard that adapts to the person using it – not the other way around.

Make insights findable and usable

Even the most gorgeous dashboard can fail if people don’t know where to look or what to do next. Navigation matters. Hierarchy matters. Defaults matter.

Here are a few user-first design moves to consider:

• Progressive disclosure: Show the most important insights first, with the option to explore more. This reduces overload and guides the user naturally
• Guided pathways: Design flows that help users reach specific business questions or decisions
• Smart defaults: Pre-set views that reflect what most users want to see first, based on role or common behavior

This kind of frictionless experience builds trust. And trust builds usage.

A well-designed dashboard makes the complex feel simple; not because the data is simpler, but because the interface is smarter.

Research Agent: The colleague that knows it all

Research Agent helps turn all of this guidance into something teams can actually execute, not just aspire to. Embedded directly in Visualizations, it acts as an always-on reviewer, analyzing dashboards and reports at the visual level to assess clarity, narrative strength, and decision-readiness based on what stakeholders really see. It flags clutter, weak “so what” statements, and confusing layouts, then suggests how to refine them so insights are clearer, more relevant, and easier to act on. In practice, that means researchers spend less time second-guessing design choices and more time delivering dashboards that land with every audience, from execs to end users, without adding complexity or extra tools.

Human-first visualizations drive real decisions

Designing for humans means more than just clean lines and tidy charts. It’s about recognizing that every data point has a human on the other side of it – and every user has a decision to make.

A human-first dashboard:

• Reflects the needs and context of its users
• Balances clarity with depth
• Makes exploration intuitive and rewarding
• Encourages curiosity without causing confusion
• Translates insight into action

Read more: Human-centered design for market research

Ultimately, the best dashboards feel like they were designed just for you. They speak your language. They fit your workflow. They don’t just show you data – they help you understand it.

Because when people feel empowered, they use the insight. And when they use it, things change.

Now, we’ve used dashboards as an example throughout this, but the same principles apply to all types of data visualization. If you’d like to learn more about how you can make sharing your data quicker, easier and more appealing visit our Visualizations webpage.

It didn’t spark curiosity.
It didn’t feel simple or clear.
It didn’t make the story obvious.

That’s why step one in any good dashboard strategy isn’t about KPIs or chart types. It’s about how the experience feels to the people you’re trying to reach. Before you think about what goes into your dashboard, you need to consider how your audience will connect with it.

First impressions shape engagement

Designing a dashboard is a lot like writing a story. If the first few seconds don’t grab your reader (or in this case, your stakeholders), you risk losing them for good.

That’s not about being flashy. It’s about visual clarity and emotional ease – making the data feel approachable, relevant, and purposeful from the very first glance.

A dashboard that looks easy to read is far more likely to be explored. A dashboard that looks confusing is often ignored (regardless of how brilliant the insights might be).

That’s why dashboard design matters. A lot.

Increase the data-ink ratio (without removing the soul)

One of the original golden rules of dashboard design is to increase the data-ink ratio – in other words, minimize unnecessary visual fluff so that every element on the screen is carrying its weight.

Less chart junk = more cognitive breathing space.

But in 2026, we also need to be careful not to go too far. If we strip away too much in the pursuit of minimalism, we can end up with dashboards that feel clinical, cold, or just plain boring.

Modern data storytelling is about striking a balance:

• Remove noise, but keep personality
• Cut clutter, but keep context
• And don’t just reduce – refine

Think about what will help the user feel the meaning, not just see it.

Dashboard designs that look easy to read

This one’s deceptively simple – but critical.

In a world overloaded with infographics, animations, widgets, and toggles, stakeholders are tired. Their attention is fragmented. So, if your dashboard looks hard to use, they won’t even try.

This is how you make it look easy:

• Use consistent, intuitive layouts: Users need to know where to look
• Reduce unnecessary color noise: Use accent colors to guide attention
• Align visual hierarchy with business hierarchy: Put what matters most at the top
• Use plain language: Avoid jargon, labels that confuse, or metrics without meaning

It’s not about dumbing down. It’s about designing with empathy to create emotion.

Research Agent speeds time to dashboard design

If your team does not have the time, headspace, or hands-on bandwidth to constantly fine-tune dashboards for this balance, Research Agent can take care of these rounds of feedback for you.

It helps teams move faster from raw results to clear, compelling stories by surfacing what matters, cutting through clutter, and shaping insights into outputs people can actually use. That means less time wrestling with layout, structure, and signal-to-noise, and more time focusing on what the data is saying and what to do next.

In practice, Research Agent helps you create dashboards and deliverables that are cleaner without feeling cold, sharper without losing nuance, and efficient without stripping out the human touch that makes insights stick.

Find out more: See Research Agent in action

Emotion is the shortcut to action

When we talk about emotional impact in dashboards, we don’t mean making people cry (although we’re not against it). We mean creating a sense of connection. A feeling that ‘this matters’.

Great dashboards don’t just explain what’s happening. They help people feel the urgency of a problem, the potential of a solution, or the significance of a shift – often in a matter of seconds. That emotional response is what moves stakeholders from passive readers to active decision-makers.

To build emotional impact into your dashboard design:

• Focus on visual clarity that builds trust and confidence
• Use language that emphasizes the human context behind the data
• Choose framing elements (colors, icons, layout) that reinforce the tone of the insight
• Bring the data closer to real-world outcomes or individual experiences, wherever possible

Emotional resonance makes insight more memorable, more persuasive, and more likely to spark action.

Decorative framing isn’t fluff – it’s emotional UX

There’s a fine line between decoration and distraction. But used well, decorative framing can significantly boost engagement and memorability.

This could mean:

• A branded header that makes the dashboard feel familiar
• Iconography that reinforces meaning
• A cover page or intro screen that sets the tone
• Light framing visuals that reinforce the purpose of the data

As long as it’s not interfering with clarity, these emotional cues can anchor your audience and create a sense of narrative continuity.

Done right, this kind of visual framing actually makes data feel more human.

Make your dashboard designs worth reading

Put all of these principles into practice, and step one looks something like this:

• Remove visual noise (but not personality)
• Make your dashboard look instantly scannable and simple
• Use emotional cues like framing and iconography to guide attention
• Lead with storytelling, not just stats
• Always design dashboards for human connection, not just data logic

The dashboards that stick aren’t the most complex – they’re the ones that make meaning feel intuitive. And that starts from the first glance.

Now, we’ve used dashboards as an example for data design throughout this, but the same principles apply to all kinds of data visualization. If you’d like to learn more about how you can make sharing your data quicker, easier and more appealing visit our Visualizations webpage.

They don’t see the careful sampling or the hours you spent cleaning verbatims. They see the headline, judge at one chart, and make a sweeping generalization at one color palette. Perhaps not the most helpful at a time when the demand for useful data is at an all-time high. But hey, we’re visual creatures, so you can’t totally blame the non-researchers. 

Can AI help with data visualization? 

In MRII’s global study, 62% of market researchers said they or their team are already using AI tools, up from 39% the year before. Now, so far, most researchers have been using AI to speed up steps in the research process. We’ve seen this, with Research HX using AI and integration to cut research time in half¹.  

But what if we can apply AI to visual storytelling too? Taking a step beyond just speeding things up and moving towards improving how we can communicate data.  

Visualizations: The high-stakes step 

We have enough data, we now have the speed to process it, we have the human research expertise to know what data points make important insights for the audience. This is no longer the bottleneck, understanding is. 

We know we only have a few seconds to capture our audience’s attention. Attention spans have been shrinking since the dawn of the smartphone, and with publications like TIME reporting this means our focus is now worse than that of a goldfish, it dramatically highlights another problem when presenting to non-researchers. Data literacy. Because that common 9-second goldfish stat (while incidentally proving the point) is entirely made up, they can remember things for months.  

Data literacy = how well someone can read, comprehend, analyze and communicate data. Studies show that 88% of our audiences may struggle with this. 

This is why you’ve been tasked with the research, because this is a skill you can bring to the table that your stakeholders have recognized they need. So how do you communicate well with someone with the attention span of a fly, who can’t understand the data you’ve collected, and who probably wants their own opinions reinforced by you? You already know the answer… 

This is why data visualization is such an essential step; getting it right can make or break all the work you’ve already put into reaching that point. Because when a stakeholder says, “I don’t get this chart,” they’re not being difficult. They’re human. 

Why this matters for market research visualizations: If your chart requires a mini lecture, you’ve already lost. A visualization isn’t ‘supporting evidence.’ It’s the whole case, all by itself. 

Read more: Seeing is believing: How to display your data story 

That’s exactly why Research Agent exists: It’s built to make visualizations clearer, conclusions stronger, and reporting faster, by reviewing the actual slides/infographics/tables stakeholders see and coaching you in the moment to make adjustments that will help your insights land. 

What are ‘research agents’? 

Research agents are born from the latest AI developments and the research industry’s gradual move towards agentic. To simplify, they can appear as chatbots that live in your existing workflows to help you to improve the creation, analysis and the display of data.  

Read more about AI agents: Agentic AI: Your personal research assistant

For example, within Forsta’s Visualizations, Metadata Agent cleans and standardizes data before reporting even begins to acceplerate set up. And Research Agent’s job is to evaluate whether an insight within a report is clear, credible, and decision-worthy, based on what’s actually on the slide: 

• Research Agent strengthens the “so what” by identifying weak or unclear takeaways and helping refine them into confident, decision-ready conclusions, with an exec-ready focus. 
• It also gives design and layout guidance to reduce rework caused by cluttered slides. 
• Acts as a first-pass reviewer to reduce iteration cycles and speed delivery.
• Supports iterative conversation, so insight development becomes exploratory instead of linear and fragile. 
• Provides design and layout guidance to reduce rework and reliance on specialist designers. 
• It’s embedded directly in Visualizations, keeping insight work in flow with fewer tools and less friction.

This bundle of functionalities tackles the real problem: Slide quality is a messy mix of analysis, design, and storytelling, and most teams are trying to do it all with fewer people and less time, and in many cases, without a data design background. 

The bottom line 

Research AI agents help you to deliver what execs actually want from insight teams: Clarity, confidence, and all this at speed. It assists you turning technically correct charts into insights that can and will guide decisions, surfacing the “so what” instantly so leaders can move with full confidence in you.

Lean teams can produce senior-level outputs without ballooning headcount, while research-native intelligence keeps conclusions credible, methodologically sound, and fit for high-stakes decisions. Faster insights, fewer bottlenecks, and no compromise on rigor. 

So go ahead and ask your AI agents.

Just make sure it’s an agent that understands research, respects rigor, and improves the story on the slide where decisions actually get made.

Find out more about Forsta and research agents on our dedicated webpage.

1. Forsta research: A side by side comparison producing a project where questionnaire is 30 questions, sample 5000 respondents, 100 slides in ppt and tables where demographics are crossed by all questions is the deliverable, fieldwork is 10 days. 

But here’s the thing: Market research is so much more than ‘just another business function’. 

It has its own needs. Its own pace. Its own complexity. And when research teams try to wrangle generic BI tools into doing what they need, it usually ends in frustration, bottlenecks, or a pile of dashboards that no one actually looks at. 

That’s why more and more insight teams are switching to tools built specifically for market research reporting. Tools like Visualizations. 

Market research reporting reality: Speed, complexity and constant change 

Market research is often fast, messy and multi-dimensional. You’re dealing with: 

• Multiple waves of data 
• Complex sampling and weighting 
• Changing stakeholder priorities 
• Segmentations, brand trackers, and open-ended responses 
• Short lead times and high expectations 

Generic BI can visualise data. That does not mean it accelerates analysis. Too often, teams spend more time wrangling the tool than exploring the “so what,” or they rely on developers to tweak filters and rebuild views. Momentum dies. Insights drift. 

Designed for research, not retrofitted 

Market research reporting-specific tools like Visualizations are purpose-built for the job. 

A dedicated platform goes beyond plugging into market research workflows; it enhances them, making your life easier. With built-in support for methods like Net Promoter Score (NPS), significance analysis, segment comparison, and custom benchmarking, you can create visuals that reflect the realities of your data without complicated workarounds. 

You can also apply filters and weights in a way that aligns with your projects. The data feels native, not forced. And you don’t need a background in coding or BI design to make it all work. 

Empowering researchers; engaging stakeholders 

BI tools certainly have their place, but one of the biggest advantages of deploying market research-specific tools is the intuitive way they support both the insight team and the wider business. 

For researchers, it means more autonomy. You can: 

• Build dashboards without technical support 
• Iterate and adapt reports as research evolves 
• Customize outputs for different audiences 
• Work in real-time with live data integrations 

And for stakeholders? They get what they need: Accessible, easy-to-navigate dashboards that tell the story behind the data. No more static charts or PDFs buried in inboxes. Just clean, clickable visuals that spark action. 

Democratizing data 

When people can explore the data in a way that makes sense to them, they’re far more likely to use it. That means your insights land harder and go further, continuing to cement you as indispensable.

This goes hand in hand with working market research reporting into stakeholders existing workflows. Instead of forcing stakeholders to learn yet another portal, meet them in their natural habitat with links that open the exact view they need, prefiltered by market, wave, segment etc. Pair that with scheduled email digests and a “View full dashboard” link for those who want to dive deeper, for full immersion.

Keeping IT happy

It’s not just research and business teams who benefit from market research-specific tools. IT teams are often relieved when tools like Visualizations take some of the pressure off. 

Because Visualizations is secure, scalable, and designed to operate without constant support, it reduces the demand on internal tech teams. There’s no need for researchers to file tickets just to tweak a filter or rebrand a dashboard. Plus, if you’re already using another Forsta product, the integration means fewer legal and compliance headaches as it’s an extension of what you’re already working with.  

At the same time, the platform still offers the governance, control, and security your IT teams care about. It fits alongside existing BI tools without disruption, meaning you don’t have to choose between research-focused functionality and enterprise-wide oversight. 

Three reasons you should use a market research-specific solution 

In a sea of visualization tools, we get lost choosing the best solutions to meet our needs. If you’re still undecided about whether market research-specific tools are right for you, there are three very good reasons to make the switch from BI tools: 

1. Save time and resources: A large portion of market research projects require efficiency and effective report creation in order to serve the needs of clients and departments after data. 
2. Fulfilling market research-specific requirements: Market research data analysis is complex and calls for a specialized set of features to get the desired results, and share them appropriately. 
3. Responding to change: Market research teams are constantly adapting to new requirements and custom projects. 

BI tools can achieve a great deal, but when it comes to market research, they’re never going to go the extra mile. And the extra mile is precisely what insight teams need.  

Case study: GlobeScan cuts market research reporting time by 40% 

Global insights consultancy GlobeScan needed a faster, more efficient way to deliver high volumes of client reports. With hundreds of dashboards and PowerPoint slides to create, their reporting process had become too manual and time-consuming – leading to delays and a reliance on outsourcing. 

The solution? Visualizations. 

By automating much of the process through Visualizations, GlobeScan gained full control over quality, eliminated outsourcing, and drastically reduced editing time. 

“Today, we’re far more confident of the quality and can more nimbly address client requests.” – Terri Newman, Director of Graphics and Technical Training, GlobeScan 

The result: 

• 40% reduction in report production time 
• Easier edits and updates 
• Full visibility and consistency across outputs 

Read the full case study.

Case study: Ad Hoc delivers custom reports faster 

Ad Hoc Research is known for bespoke work across concept testing, EX, CX and brand measurement. Off-the-shelf tools could not keep pace with their promise of personalization, and limited resources capped client capacity. A bilingual market added complexity when platforms lacked true French and English support. 

The solution? Visualizations. 

Ad Hoc leaned into Visualizations for flexible buildouts, close collaboration, and speed. The team uses features like My Stories to auto-assemble PowerPoint decks and let clients self-filter by province or age, rather than rebuilding slides by hand. The interface can be toggled to French or English in one click, which keeps Quebec public-sector work simple.  

“Forsta has been a flexible tool that allows us to answer our clients’ needs in a tailor-made way.” – Erica Horn, Ad Hoc.  

The result: 

• Market research reporting delivered 1 to 2 weeks quicker than manual workflows.  
• More capacity without headcount by automating repeat builds; stronger client “stickiness” from customized, on-brand exports.  
• Bilingual portals in a click for French and English audiences.  
• New SMB offer, Radar HR, with an 8-minute standardized survey and a growing benchmark to compare by industry or geography.  

Read the case study.

Case study: Harris Poll modernizes dashboards and delivery 

Harris Poll blends custom and syndicated research at scale. After acquiring a visualization platform, they hit limits around dynamic filtering, norms and visual appeal, which created inefficiencies and fell short of client expectations. 

The solution? Visualizations. 

Harris shifted to Forsta’s Research HX stack, integrating Decipher and Visualizations so new waves flow straight into client-ready dashboards without manual rebasing. The upgrade improved deliverables, efficiency and satisfaction, and it put modern visuals front and center.  

The result: 

• Near-instant portal updates as survey data lands, replacing slow handoffs to data processing. 
• Dashboards that clients describe as a step above competing platforms.  
• Self-service features, like cross tables, that reduce ad hoc requests and boost engagement.  
• Rebuilt legacy projects, streamlined workflows and tighter delivery timelines with ongoing expert support from Forsta.  

Read the case study. 

Make the switch for smarter insight 

If your team is still relying on generic BI tools to communicate research, it might be time to rethink your market research reporting setup. 

The right tech doesn’t just display data. It helps you tell a story. It brings clarity, confidence and connection to your work. And it puts the power back where it belongs: In the hands of researchers. 

With Visualizations, you get: 

• Research-ready dashboards from day one 
• Faster project turnaround 
• Stronger stakeholder engagement 
• Better business impact 

See Visualizations in action. Level up your market research reporting.

The four key chapters of data storytelling

Context: Start by setting the scene. What’s the problem, question, or situation your data is responding to? Without context, your audience won’t know why the data matters.  

Insight: Highlight the ‘aha!’ moment. What does the data reveal that wasn’t obvious before? This is the core of your story, the thing you want people to remember.  

Narrative: Bring structure and flow. Connect the dots in a way that’s logical, human, and easy to follow. This might mean using comparisons, timelines, or themes to frame your message.  

Action: End with a purpose. What should your audience do, think, or change because of this insight? A great data story always points towards the next step.  

Together, these four parts turn static charts into compelling stories that spark decisions.  

The three Cs of visualization 

The three Cs of data visualization are clear, concise, and compelling. Let’s dive in:   

Clear: Your visuals should be instantly understandable. Strip away clutter and avoid misleading design choices so the audience can grasp the meaning at once.  

Concise: Show only what matters. Focus on the key message or takeaway, using data points that support your narrative without overwhelming the viewer.  

Compelling: Go beyond just presenting data; tell a story. Use design, context, and structure to make your insights engaging, persuasive, and memorable. 

The three Cs act as a simple litmus test: If your visual isn’t clear, concise, or compelling, it’s probably not ready to share.  

Now it’s time to take a closer look at the five stages of creating visualizations for market research data storytelling. 

The five stages of visualization 

Whether you’re visualizing customer segmentation data or campaign performance metrics, these five steps provide a reassuringly reliable framework:  

Step one: Define your objective  

Start with the end in mind. What decision are you trying to influence? What action do you want stakeholders to take? Your objective determines everything else about your visualization approach.  

Questions to ask:  

• What is the key insight I need to communicate?  
• Who is my audience and what do they care about?  
• What action should they take based on this data?  
• How does this fit into the broader business context? 

Step two: Select your data  

Choose the specific data points that best support your objective. This often means leaving out interesting but irrelevant information that might distract from your main message.  

Selection criteria:  

• Relevance to your key message  
• Data quality and reliability  
• Audience familiarity and interest  
• Available visualization options  

Step three: Choose your visual format  

Match your chart type to your data type and communication goal. Different visualization types excel at different tasks.  

Format considerations:  

• Data type (categorical, continuous, time-series)  
• Relationship you want to show  
• Audience preferences and familiarity  
• Presentation context (executive summary vs. detailed analysis)  

Step four: Design for impact  

Polish your visualization to maximize clarity and persuasiveness. This involves everything from color choices to annotation strategies.  

Design elements:  

• Clear, insight-focused titles  
• Strategic use of color and emphasis  
• Appropriate context and comparisons  
• Clean, professional formatting  

Step five: Refine and validate your visual  

Before sharing your visualization with stakeholders, take time to review and refine it. This isn’t just about aesthetics; it’s about making sure the message is clear, the interpretation is accurate, and the audience walks away with the right takeaway.  

Validation steps:  

• Check for visual clarity and alignment with your original objective  
• Test with a colleague or non-expert: Can they interpret it easily?  
• Look for unintended visual bias or clutter  
• Adjust layout, labels, or color to improve flow and focus 

The four keys for market research data storytelling

If you’re searching for the four keys to great data visualization, here’s a practical framework where the goal isn’t just to visualize data, but to influence decisions.  

Clarity: Every element of your visual should help the viewer instantly understand the message. Labels, colors, and chart types must all work together to reduce cognitive load. 

Relevance: Focus on the data that supports your key insight. Extra numbers might be interesting, but they dilute the story. Always ask: Does this support my objective?  

Context: Don’t just show the ‘what’ – show the ‘why’. Use comparisons, benchmarks, or historical trends to give your audience a meaningful frame of reference.  

Storytelling: The best visualizations lead your audience to a ‘so what?’ Make sure your visual builds towards a clear conclusion or call to action.  

These four key elements work together to turn static visuals into decision-making tools, helping your audience move from insight to impact.  

See it for yourself 

Get your insights the makeover they deserve. Request a demo today to see how Forsta can help you visualize your way to better business decisions. 

Here’s the thing: Raw data doesn’t sell ideas. Stories do. And the bridge between your brilliant research and actionable business decisions? The art and science of Market research visualization.

Whatever you’re presenting, how you visualize your market research data can make or break your impact. Let’s dive into everything you need to know about transforming those spreadsheets into compelling visual narratives.

What is data visualization?

In the context of market research, data visualization is the art and science of transforming research findings into snazzy visual formats that communicate insights clearly and compellingly. But it’s more nuanced than just making charts.

Market research visualization serves three critical functions:

• Discovery: Visualizations help researchers identify patterns, outliers, and relationships within their data that might not be obvious in spreadsheet form
• Communication: They translate complex findings into formats that stakeholders can quickly understand and act on
• Persuasion: Well-crafted visualizations don’t just inform; they inspire action by making the implications of research findings impossible to ignore

Ultimately, market researchers create visualizations to drive business decisions.

Where did visualizations come from?

Before we get into the modern world of market research visualization, let’s take a quick trip back in time. Understanding where data visualization came from helps us appreciate just how far we’ve come – and where we’re heading.

What is the oldest visualization tool?

If we take Andy Kirk’s widely accepted definition – “the representation and presentation of data to facilitate understanding” then visualization predates bar charts by tens of thousands of years.

Some historians point to Paleolithic cave paintings, like those in Carricola and Altamira, as early examples. Over 40,000 years old, they may have depicted wildlife and hunting patterns. It’s a reminder that visualization is about clear thinking, not fancy software. The best market research visuals still start as simple sketches that clarify the story.

What is the oldest visual data representation?

Ancient cave paintings and early maps top the list. In market research, early perceptual maps and demographic charts were foundational. They proved that complex customer behavior could be made clear through visuals.

What was the first data visualization in history?

The oldest known data visualization dates back to 1785-6, when William Playfair created the first statistical charts in his work “The Commercial and Political Atlas.” His bar charts showing Scotland’s trade transformed abstract data into something instantly understandable.

Playfair’s motivation was exactly the same as ours today: Make data accessible to people. Fast-forward to 2026, and his legacy lives on in every interactive dashboard, PowerPoint presentation, heat map, and AI-generated chart we use today.

What are the 4 main visualization types?

What are the four types of data visualization, we hear you ask? Which is a smart question! Understanding the four fundamental types will help you choose the right approach for your specific research objectives:

Comparison visualizations

These show relationships between different data points or groups. Think customer satisfaction scores across demographics, brand preference by region, or product performance over time. 

Best for: Competitive analysis, segmentation studies, A/B testing results, benchmark comparisons

Common formats: Bar charts, column charts, radar charts, parallel coordinates

Composition visualizations

These reveal how individual parts contribute to a whole. Perfect for showing market share, budget allocation, or the breakdown of customer feedback themes.

Best for: Market share analysis, budget allocation, survey response breakdowns, demographic compositions

Common formats: Pie charts, stacked bar charts, treemaps, waterfall charts

Distribution visualizations

These display how data points are spread across a range of values. Essential for understanding behavior patterns, price sensitivity, or demographic distributions.

Best for: Customer behavior analysis, price sensitivity studies, demographic research, satisfaction score distributions

Common formats: Histograms, box plots, scatter plots, heat maps

Relationship visualizations

These explore connections and correlations between different variables. Crucial for understanding how different factors influence behavior or business outcomes.

Best for: Customer journey mapping, correlation analysis, factor analysis, predictive modeling results

Common formats: Scatter plots, bubble charts, network diagrams, correlation matrices

The magic happens when you combine these types strategically. A comprehensive market research presentation might start with composition charts showing market breakdown, move to comparison charts highlighting competitive positioning, and conclude with relationship visualizations that reveal key drivers of human behavior.

But that’s not all you need to know about market research visualizations. The 3 Cs (coming soon!) are just as important!

Emerging trends in market research visualization for 2026

In 2026, AI-driven visualization tools will be moving towards necessity. Researchers are leveraging predictive dashboards, real-time storytelling, and mobile-optimized visuals to keep pace with data-hungry decision-makers.

Some trends to look out for include:

• Interactive dashboards that allow stakeholders to explore data themselves, moving beyond static presentations to dynamic exploration tools.
• Real-time visualization enabling continuous monitoring of customer sentiment, campaign performance, and market trends.
• Mobile-first design to ensure visualizations work effectively on all devices, recognizing that many stakeholders consume research insights on mobile devices.
• AI-assisted visualization tools suggesting optimal chart types and identify patterns automatically, though human judgment remains crucial for interpretation and storytelling.

Related reading:

1. The art and science of data visualization: Turning numbers into narratives
2. Seeing is believing: How to display your data story
3. The role of integration in enhancing data visualization
4. Expert insights into the future of visualizations

Transform your market research with powerful visualization

Ready to elevate your market research visualization game? Forsta’s comprehensive market research platform provides everything you need to transform complex data into compelling visual stories that drive business decisions.

Our integrated visualization tools work seamlessly with our survey design, data collection, and analysis capabilities, giving you a complete solution for modern market research. From real-time dashboards that track campaign performance to sophisticated segmentation visualizations that reveal hidden customer insights, Forsta makes it easy to create professional, impactful research presentations.

Don’t let great insights get lost in spreadsheets. Request a demo today to see how Forsta can help you visualize your way to better business decisions.

Welcome to the era of data democratization. In this blog, we’re giving you the full lowdown on why data democratization the key to making research more impactful, accessible, and action-driven than ever before. 

The problem: When data is trapped in silos 

Data is powerful, but only if the right people can access it, understand it, and do something with it. Too often, insights are locked in static datasets or siloed in systems that only a select few can navigate. Sharing them can become a game of pass-the-data-points where no one is getting the insights they need. 

Stakeholders want insights that speak to them. Instantly. Visually. Meaningfully. If it takes a data scientist to explain what’s going on to every single stakeholder, something’s broken. 

The shift from data to insight

Data democratization is about flipping the script. Instead of hoarding insights behind locked doors, it means making them available to the people who need them, when they need them, and in the format that makes sense to them. 

It’s not about removing expertise; it’s about enabling action. When customer success, marketing, and product teams can access and interpret insights directly, business moves faster. Smarter. More in sync with what customers actually need. 

That requires more than a mindset shift. It needs a platform built for the job. 

The how: An integrated approach 

With a dedicated Research HX platform, data doesn’t just sit in a silo waiting to be analyzed. It flows. Seamlessly. From collection to visualization, from qualitative comments to quantitative dashboards. 

You can combine market research, CX, EX and VoC data in one place. That means fewer tools, fewer handoffs, and fewer barriers between your team and the insight that drives action. 

Research HX makes it easy to: 

• Unite qual and quant without forcing them into unnatural boxes 
• Build insight workflows that actually reflect how teams think and work 
• Deliver findings that land with impact across the business 
• Filter dashboards to be audience-appropriate, not one-size-fits-none 
• Streamline reporting so insight teams can focus more on outcomes, less on formatting 

But democratizing insights isn’t just about making dashboards shareable. It’s about removing friction at every stage of the process. Think of all the blockers that keep insight from being acted on: logins people forget, tools they don’t understand, dashboards that need an analyst to interpret. 

The best democratized systems remove those barriers, because when access becomes second nature, insight becomes second nature too. 

Engagement amplified: Hooking your audience

Even with all the right data in all the right hands, if it doesn’t grab attention, it won’t go far. Why? Because the way you present insights can make or break their impact. Data democratization isn’t just about visibility: It’s about engagement. Here’s why visual storytelling is so powerful: 

• Visual appeal: Infographics are inherently more eye-catching than tables of numbers 
• Concise information: Key findings are presented succinctly 
• Storytelling: Infographics weave data points into a narrative, making the information more relatable and memorable 
• Shareability: Visually appealing and easily digestible content is more likely to be shared 

And that’s where the power of visualization comes in… 

Unlock deeper value through visualization 

The true power of data democratization lies in how you communicate insights. Infographics and visual storytelling unlock: 

• Improved comprehension: Visuals facilitate understanding complex relationships and trends within the data 
• Enhanced recall: Memorable visuals lead to better retention of key findings 
• Facilitated discussion: Infographics provide a clear and concise focal point for conversations and decision-making 
• Stronger impact: Presenting data visually makes your research more persuasive and influential 

To do this well, here are a few key considerations: 

• Know your audience: Tailor the design and complexity to their understanding and interests 
• Focus on key insights: Don’t overload the infographic with too much information. Highlight the most important findings 
• Choose the right visuals: Select charts, graphs, and icons that accurately and effectively represent the data 
• Maintain clarity and simplicity: Ensure the design is clean, uncluttered, and easy to follow 
• Tell a story: Structure the infographic logically to guide the viewer through the data narrative 
• Use color and typography strategically: Create visual hierarchy and enhance readability 
• Ensure data accuracy: Double-check all data points to maintain credibility 

But why is this so important? 

Why visualization matters 

Great research deserves great design. That’s why our Research HX platform’s Visualizations brings storytelling to life with visuals that don’t just look good; they clarify, persuade, and inspire action. 

From interactive dashboards to heatmaps and animated trend lines, visualization transforms static numbers into dynamic narratives. Stakeholders don’t want raw data; they want meaning. This is data democratization in action, so show your teams how visualization can be used to: 

• Track sentiment shifts in real time 
• Spot customer friction points at a glance 
• Build story-led dashboards for different departments 
• Highlight impact over information 
• Drive emotional connection with insight, not just intellectual buy-in 

These aren’t just pretty pictures, they’re accelerants for understanding. The right chart, in the right place, can be the difference between confusion and clarity. 

Need to compare performance across regions? A quadrant map does the trick. Want to understand respondent emotion at a glance? That’s where a word cloud or emoji sentiment map shines. Trying to map a respondent’s path through a purchase decision? A journey visualization lays it out beautifully. 

And gone are the days of having to do all the manual grunt work yourself. In these blessed days, we have AI… 

The role of AI in bringing qual and quant together 

AI is changing the game. Not by replacing researchers, but by helping them to scale their thinking. 

Forsta’s AI-powered tools help to: 

• Summarize and cluster verbatims for faster analysis 
• Surface emerging themes from open-ended responses 
• Turn unstructured text into structured insight 

This lets researchers move from grunt work to great work. Less time on manual coding, more time on strategic thinking. And crucially, it means qualitative data becomes just as scalable as quant without losing its human nuance. 

The power of role-based views

Here’s where data democratization gets really good. Because not everyone needs the same story. With Forsta, you can build role-based dashboards that tailor the message for each audience: 

• The CMO sees brand impact and campaign ROI 
• The UX team gets user friction points and feature feedback 
• Customer service leaders view top complaint drivers and praise points 

No need to rewrite the research ten times, once you’ve got your dashboards you can filter them to display the ideal information for each stakeholder. That means decisions happen faster, and they’re better informed. 

What it means for business

Data democratization isn’t just good for researchers. It’s good for your business, and your clients. 

• Teams act quicker because they have the data 
• Insights spark ideas across departments 

• The end result is a better experience, because clients know where to focus
• Clients trust you, rely on you and love what you bring to the table

By making insight more digestible and visible across functions, companies create a culture of curiosity and accountability. It’s not about data for data’s sake; it’s about creating a shared language that drives results. 

In short: Everyone wins. 

Want to find out more? Book a free demo to see what our industry-leading platform is all about. 

You can have the best research in the world, beautifully designed, meticulously analyzed, filled with smart observations and it still might not make an impact. Now, that’s not because your insights aren’t valuable; it’s because too often, they get lost in translation.  

The visualizations don’t connect. The dashboard’s too dense. The story gets buried under layers of charts, filters, and data points. And in a world where attention is a scarce commodity, that’s a dealbreaker. 

In this blog, we’ll fix all of that with insight storytelling, and more. 

Why great insights fall flat 

You know the feeling. You’ve nailed the research brief, delivered a killer study, but by the time you’re presenting to stakeholders, something’s gone a bit… stale. The data’s there, but the impact? Not so much. And it’s rarely down to lack of effort. The real issue usually lies in the tools and processes behind the scenes. 

Clunky tech stacks mean your data is stuck in one system, your analysis in another, and your final visuals manually cobbled together in a PowerPoint presentation. Teams are siloed, timelines are tight, and storytelling becomes an afterthought. And when insight delivery doesn’t match the pace or preferences of the audience, it gets lost in translation. 

Worse still, leadership might not realize what’s being missed. Because the story was never told in a way they could connect with. The thing is, CMOs and insight leads aren’t short on raw data. What they’re short on is clarity. They need research that speaks to them (not just in numbers, but in meaning). This is where you come in, to add some fizz back into data reporting. 

Research HX: Integration meets impact 

Integrated research tech (like Forsta’s Research HX solution) changes the game. When your tools talk to each other – i.e. when survey responses feed directly into visual dashboards, and qual and quant data sit side by side – you don’t just get prettier charts. You get stories that land. Faster delivery. Greater accuracy. Role-based dashboards that show people exactly what they care about, and nothing they don’t. 

It’s about more than combining tools: It’s about uniting research workflows to deliver stories that are sharp, swift, and built for the real world. 

With a fully integrated platform, the insight story process becomes seamless. You’re no longer hopping between systems or translating formats. Data flows smoothly from collection to visualization, and storytelling becomes a key part of the research, rather than an afterthought. 

That means: 

• Faster workflows with less hassle and fewer handoffs. 
• Cleaner, clearer outputs designed for specific audiences. 
• Stronger collaboration between research, design, and strategy teams. 
• Research teams or agencies that are valued and have a seat at the table. 

Because when everything talks to everything else, magic happens. 

Instead of wrestling exports and reformatting, your team can spend more time digging into the why. The narrative arc becomes clearer. The insight story gets sharper. The final output is designed not just to inform, but to persuade. And with better control over your data and visuals, you can tailor outputs to suit each stakeholder. 

Dashboards that speak to CMOs (and everyone else) 

Let’s get specific. A CMO doesn’t want a 94-page deck. They want a clear view of how a brand is performing across markets. A product lead wants to know what users hate (and love). An insights lead wants pain points and quick wins. 

An integrated approach to insight storytelling helps you meet each of those needs because you’re not scrambling to repurpose charts. With role-based filters, you’re not just sending out one-size-fits-all reports. You’re curating insights that are relevant to each decision-maker: 

• The CMO gets a high-level narrative 
• The Head of Insight sees the granular patterns 
• The Product team digs into feature feedback, unmet needs, and VoC themes 

All from the same dataset, just filtered and visualized based on what each person actually cares about, all updated in real-time. 

Now, let’s talk about those all-important visuals. 

Visual storytelling that gets noticed 

Let’s be honest: Many research outputs could use a glow-up. 

But when visuals are clean, focused, and interactive, people pay attention. In a business landscape where time is tight and attention spans are even tighter; your insights need to work harder and look sharper.  

Integrated platforms make it easy to build visuals that don’t just display data but communicate it. Think heatmaps that bring customer journeys to life. Word clouds that turn verbatim feedback into a snapshot of sentiment. Animated trend lines that show change over time in a way that sticks. Interactive dashboards that let every stakeholder explore the bits that matter most to them.  

These aren’t just aesthetic choices; they become strategic tools. Because visual storytelling isn’t just about making data prettier, it’s about making it memorable. And when insights are presented in a way that sparks interest and invites interaction, they don’t just land. They stick. 

Recap: Common insight story pitfalls and how to avoid them 

Here are a few classic reasons why insight stories flop, and how to flip the script: 

• Too much data, not enough story: Dumping a spreadsheet into a dashboard doesn’t make it a story. Lead with what matters. 
• One-size-fits-none reporting: Give different audiences different views. The CEO doesn’t need the same level of detail as a brand manager. 
• Clunky workflows: If you’re juggling five platforms and three teams just to get a chart out, something’s broken. 
• Pretty, but pointless: Don’t fall for eye-candy charts that don’t actually help decision-making. Substance comes first. 

In other words, if your stories aren’t sticking, it might be time to look at the systems behind them. 

The most powerful insight teams aren’t just great at research: They’re brilliant at communication. And that means ditching the silos, integrating the stack, and building stories that hit home. Forsta’s Research HX is built to help you do just that with Visualizations. One platform, one workflow, one version of the truth tailored to each audience, and powerful enough to drive real change. 

So, if your story’s getting lost, maybe it’s time to tell it differently. 

Data doesn’t always speak for itself. It needs a translator. A conductor. A storyteller. And that’s exactly what Visualizations in Research HX is built to be.

In the age of infinite data, visualizations aren’t just nice-to-have. They’re the deal-sealer. They turn overwhelming information into elegant stories. They spark action. They show the what, the why, and the so what.

With Research HX, visualizations don’t just sit at the end of the process like a cherry on top. They’re baked in from the start, feeding off live data, powered by AI, and tailored for the humans who need to make fast, confident decisions.

Beyond data dashboards

Let’s be clear: This isn’t marketing fluff or surface-level dashboards.

Visualizations in Research HX is built for professionals who need detail, not decoration. It’s fully customizable. You can tweak hundreds of settings to fit your brand, your client, or your analyst’s wildest dreams. Free-form design means you’re not stuck with rigid layouts or clunky widget templates. This isn’t PowerPoint by paint-by-numbers. It’s your canvas.

And speaking of PowerPoint…

Role-based dashboards

You don’t need to reinvent the wheel. Research HX Visualizations lets you export native dashboards straight into editable PowerPoint or Excel. Need a presentation for the CMO, a deep dive for your stats team, and a topline for your client lead? Done, done, and done.

Dashboards update automatically as new data flows in. And with role-based access, each stakeholder sees only what matters to them in the same dashboard, with a different lens. You stay in control, and your story stays on point.

Visualization integration: One platform to rule them all

Here’s where the magic happens.

Your survey data, whether it’s a long-term tracker or a fast-turn dipstick, flows directly into Visualizations. No exporting. No formatting. No manual, migraine-inducing Excel formulas. Just seamless, automatic syncing. Think of it as a data highway with no traffic and every lane wide open.

This means even your smallest studies can use high-impact visual reporting. What used to be reserved for enterprise-level trackers? Now available for scrappy ad hoc studies too.

AI data visualization: Smart and strategic

Our AI isn’t here to replace researchers. It’s here to elevate them.

With AI-enhanced Visualizations, respondent verbatims are analyzed on the fly, providing open-end enrichment. Summarized, themed, sentiment-tagged, and translated across languages. Want to flag PII before it sneaks into reports? AI’s got it. Need to create topics from unstructured text? No problem.

AI Compute gives you real-time reads on what matters most.

Conjoint. Crosstabs. The whole toolkit

Conjoint analysis? Already wired in. Raw data gets analyzed and visualized automatically, so no post-processing or separate tools are required.

Crosstabs? You can keep it simple or go full data-surgeon. Either way, export to Excel with ease and let your analysts work their magic. Researchers get the power to go deep without wasting time on manual formatting.

Visualizations built for the pros

We’ve said it before, and we’ll say it again. Research HX is made for professionals. You bring the expertise. We give you the tools to unleash it.

Most of our clients are fully DIY. But if you need a hand getting the most out of your dashboards, we offer expert services, training, and build support to help you fly solo—or scale up.

Research HX Visualizations

• Dynamic dashboards you can fully customize.
• Exportable PowerPoints and Excel files for seamless storytelling.
• Role-based dashboards for secure, personalized views.
• Instant integration from data collection to visualization.
• AI-powered analysis of open ends, themes, sentiment, and security flags.
• Native support for conjoint, crosstabs, and more.
• Services and support, but only when you need it.

Your data has something to say. Research HX helps it speak volumes.

Ready to go from clutter to clarity? Book a demo.

In this blog, we’ll talk you through everything you need to know about the wonders of data visualization – from what it is, why it’s important, and how it can bring your data to life, to the different types of data visualization, the pros and cons of data visualization, and the relationship it holds with big data.

You’re in for a treat!

What is data visualization?

Data visualization is the representation of information and data through visual elements – such as maps, graphs, and charts. The graphical context makes data much easier to understand and allows an audience to extract insights and spot trends with minimum confusion. Data visualization is also great for communicating information to a non-techy audience, who may get lost in reams of written data.

Also known as statistical graphics, information graphics, and information visualization, the practice of data visualization is part of the data science process, which holds firm that data must be visualized before any real conclusions can be drawn. For that reason, data visualization is frequently used within organizations to convey key information to stakeholders, and within teams.

It’s also common practice for data scientists when dealing in advanced analytics: translating complex numerical outputs into graphic visualizations makes for far easier reading – allowing results and outputs to be monitored through accurate visualizations. In the context of market research, representing customer feedback and buying trends visually is great for storytelling!

Why is data visualization important?

Put simply, data visualization is important because it increases understanding. Used across industries, sectors and disciplines, visualizations help people to process and interact with information, irrespective of their experience or expertise.

No matter what your business, data will form an essential part of it: from reporting on company performance, to monitoring the success of a marketing campaign, every organization is constantly dealing in data. But beyond the data lies something even more important: people; people who need to be able to interpret the story you’re telling them.

Data visualizations bring those stories to life – making them one of the most valuable business tools there is. They make the information human, by showing the stories behind the statistics, and the patterns behind the person.

They can help you to make informed decisions, easily spot outliers in amongst your trends, effortlessly showcase key statistics, and visually pave a clear way when you’re not sure which path to follow. Displaying data visually can also serve as an effective way to distribute information, meaning that insights can be easily shared, while making it possible to act on those insights quickly because there’s a clear path ahead.

In short, data visualizations serve to marry the creative with the technical; the storytelling with the analytic – and it’s a match made in heaven.

Why use data visualization to present your data? 8 reasons why

Now that we know why data visualization is so important, let’s look at why you should use it to present your all-important info – in whatever context that might be.

1. It’s universal: graphic representation of your data acts as a fast, efficient, and wonderfully universal way to convey information.
2. It’s insightful: being able to view all your data as a visual affords you a great many insights, as it’s possible to instantly spot where improvements need to be made, what’s performing particularly well, or how your customers are behaving.
3. It’s memorable: one of the greatest things about presenting information through data visualizations is that it becomes more memorable, more distinctive, and more note worthy.
4. It’s clear: data visualizations help your audience to absorb the info you’re relaying to them far more quickly than they would through more traditional, laborious methods (if they absorbed it at all).
5. It’s informative: if you want to make well-informed decisions, and you want to make them fast, presenting your data visually is the way forward.
6. It’s democratic: using data visualizations can help to negate the need for data scientists, as the data becomes far more accessible this way.
7. It’s captivating: grabbing and maintaining the attention of your audience is no mean feat when you’re dealing in data, but visualizations make it possible as people will be able to follow the story you’re telling.
8. It’s instructive: by helping you to predict sales, understand how to market products, and monitor consumer behaviour, data visualization can lead you to the most logical next steps for your organization.

Types of data visualization

We’ve come quite a long way since the days of pie charts and bar graphs. Effective though they undoubtedly were, data visualization has advanced somewhat. With a whole plethora of visualization methods out there to pick from, it’s important to make sure you’re choosing the right medium through which to share your data; otherwise, its beauty is lost.

So, what options are out there?

• Line charts: Basic. Common. Effective. Line charts are great for showing trends, and how variables change over time.
• Area charts: A cousin of the line chart, area charts can either be used to show multiple variables in a time series, or a sequence of data from consecutive and equally spaced times.
• Treemaps: If you’re after something that lets you compare different parts of one whole, with multiple categories at play, treemaps are where it’s at.
• Scatter plots: Need to show the relationship between two variables? The X and Y axis of a scatter plot shows your data points through dots!
•  Population pyramids: If you’re trying to visually represent the distribution of a population, population pyramids use a stacked bar graph to tell a story.
• Tables: Well, we don’t need to say much here, do we? If you need a simple way to lay out your figures, look no further than the rows and columns of the good old table.
•  Infographics: Whether a chart or a diagram, infographics combine visuals and words that represent data. Great for using on your socials!
• Dashboards: Ah, the analyst’s friend. Dashboards are a great way to display collections of data and visualizations in one place – helping you to present and analyse data with ease.

Data visualization and big data

Big data is a big deal. No two ways about it.

With the dawning of big data, organizations needed ways of dealing with the masses of data they were accumulating daily – and visualization tools were the perfect fit.

Making sense of data is critical for telling a story and getting other people to understand that story. Data visualization comes into its own where big data is concerned, because it effortlessly cuts through the noise and curates complex data into something that’s easily accessible – spotting trends and highlighting outliers as it goes.

Data visualization is also extremely valuable for its ability to speed up the interpretation process. With big data culminating in huge swathes of information that would ordinarily take an age to understand and explain, visualizations provide a graphic representation that makes the whole thing simple. And the best part? Visualizations give you a straightforward, savvy way to present key info to your stakeholders (in a way they’ll understand).

Advantages of data visualization

Right then, aside from easy stakeholder sharing, simplified interpretation, and speedy analysis, what advantages are there to data visualization?

You probably work with a lot of very clever people. So why do you need to make things easy for them? Can’t you just present info the old-fashioned way and trust their noggins to fathom it out? Well, you could – but there’s a whole science behind why data visualizations work so well.

As innately visual creatures, us humans are drawn to art. We like shapes and colors, patterns, and pictures. And we find it much easier to spot yellow from red than we do to spy differences in numbered sequences.

Data visualization is particularly effective because it commands attention and manages to keep it. When we’re presented with charts and graphs, it’s easy to spot patterns and trends; it also makes it much easier to tell a story, which helps us to internalize what we’re seeing. Viewing data in a spreadsheet just doesn’t have the same appeal.

When it comes to market research, data visualizations are perfect for helping you to interpret every rich piece of information – from customer feedback to buying trends. And because it’s so easy to share these graphics, you can bring everyone up to speed in no time at all – leading to uncapped opportunities for improvement!

But are there any downsides? 

Disadvantages of data visualization

Data visualization has a lot going for it, but there are certain drawbacks you need to consider before setting your cap at this way of working.

We’ve already given you examples of some of the different types of data visualization, but what you might not realize is that by using the wrong style, you open data up to misinterpretation or misrepresentation. Poor design can also make visualizations confusing, while visualizations carrying multiple datapoints can lead to inaccurate interpretation.

It’s also important to keep in mind that data can sometimes be biased or inaccurate, and that correlation doesn’t necessary equate to causation. Not only that, but when transferring information to graphical form, key messages risk being lost in translation.

Aside from risk of error, there’s also the effort and expense to consider. If you really want to use data visualizations to full effect, you’ll need to hire a visualization specialist (that, or enlist the help of an expert data partner). A data specialist will need to be able to optimize data by selecting the right visualization method for the data in question.

You might also need the involvement of your IT department if you’re dealing in Big Data visualization, as this often requires advanced storage and effective (and powerful) hardware.

Examples of data visualization in use

It’s all well and good talking about data visualization, but nothing quite brings the message home like seeing it in action (reinforcing the point that we’re all about the visual!) So, let’s look at some recent examples of data visualization in use today.

How eggs get their shape

Some super clever scientists have only gone and figured out why different birds lay differently shaped eggs. You could say they’re ‘cracked it’ (we’ll be here all week). The study involved data from nearly 50,000 bird eggs, collected over the course of the past 100 years, and mapped egg dimensions to an impressive 1,400 species! The chart is a beautiful illustration of the relationship between asymmetry and ellipticity.

How Americans eat

Ever wondered how much protein your average American gets through, or where it comes from? You have? Well wonder no more! This visualization utilises data from the USDA on food availability, to show how Americans eat nationally. The chart focuses on how many pounds of key sources of protein have been eaten between 1970 and 2019.

How the pandemic increased poverty

The Covid-19 pandemic led to a great many hardships for a great many people – including a rise in poverty amongst the world’s poorest countries. In this data visualization, color and placement are used to tell a sombre story, to compelling effect. The colored bars demonstrate the rising poverty rates for countries following the pandemic.

How can Forsta help?

We’re pretty big on data visualizations here at Forsta. And it’s because we frequently bring our customers’ data to life in the form of unmissable infographic storytelling.

Forsta’s infographic storytelling takes your boring old black and white data and transforms it into vibrant technicolor (or whatever’s most on-brand for you). We’ll help you to create infographics that explain, inspire, and convince people to act – all while helping you to map out steps in a customer journey, spotlight the scale of an issue, or reveal trends trapped in your data.

Telling visual stories is the best way to inject audience-inspiring pizazz into your information, and we’ve got all the tools you need to get there. Our Infographic Storytelling is incredibly fast (boasting drag-and-drop tools, design libraries, and no-snags export) – and you can use it on its own, or incorporate it into PowerPoints, PDFs, and online dashboards.

Arrange a free demo to see what it’s all about!

Getting to grips with the future of data

Data visualization is undoubtedly here to stay. With the growing popularity of Big Data, we’re not sure where organizations would be without it! But remember – it’s not without its drawbacks. You need to use the right tools at the right times, for the right data. And if you’re not sure how, it really is time to enlist an expert.

Perhaps in some quarters. But certainly not in the world of business, where the two meld together in perfect unison. Because we believe in treating each customer, or audience member as a human being – storytelling becomes even more important. It’s key to unlocking the authentic human experience of each person you connect with. 

In fact, data storytelling is an absolute treasure trove: unlocking the hidden messages and meaning behind the data, and presenting it in a language your stakeholders can relate to. So let’s take a look at what data storytelling actually is, explore the science behind why it works, and introduce you to some failsafe ways of using your data to tell a story worth listening to.

Buckle in and sit tight; we’re about to blow your mind!

What is data storytelling?

Put simply, data storytelling is a means of taking data and weaving it into an understandable narrative for its intended audience. Because while having data is all well and good, it loses its impact if it can’t be turned into something compelling.

Us humans have delighted in sharing stories since the beginning of time. It’s just what we do, and authors the world over are immeasurably happy about it. But storytelling doesn’t only belong in the pages of novels.

Now that the world has gone full-on, unavoidably, deep-seatedly digital, data storytelling stands strong as a beacon of hope for helping us to understand the increasingly complex and forever expanding levels of data available to us. Without it, we’d be in a bit of a tailspin.

(If this piques your interest, check out our webinar masterclass on Storytelling the human experience of data. In this masterclass, we show you how a handful of storytelling techniques can help you deliver compelling and actionable insights effortlessly.)

Data storytelling combines three key areas: data science (extracting vital knowledge and insight from data), visualizations (transforming that lovely rich data into charts and graphs to help us understand key findings), and narrative (translating knowledge, insight, and findings into a language we can understand and relate to). You’re speaking the language of humans, and telling the full story. 

Wonderful stuff, right?

You can use data storytelling internally to communicate why product iterations are needed, for example, or externally for convincing potential customers to invest in your products or services.

The science behind why storytelling works

Let’s face it: data can be incredibly dry; boring; yawn-inducing. Sometimes, it needs spicing up a bit to help us sit back and take note. We’re only human, after all – and how many story-loving humans can honestly lay claim to being gripped by the crustiness of tedious, monotonous, standalone data?

Stories, or narratives, help us to process information (data) far more easily than we would otherwise be able to. Even the analysts among us must admit that a little storyline can go a long way.

But why do our brains love stories so darn much?

Okay, time to get science-y. Our brains opt for stories over pure data because they are – quite frankly – overloaded to the max. We take in so much information day in, day out that somehow, our brains need to decide what to process, and what to forget. The cool thing about hearing a story is that different parts of our brains kick into action:

1. Wernicke’s area: This controls language comprehension
2. The amygdala: This processes our emotional response
3. Mirror neurons: These help us to empathise with others

When these areas are all engaged, our hippocampus (which handles storing short-term memories) converts the narrative into a long-term memory. Amazing!

So instead of plonking a load of valuable data into a boring old spreadsheet and hoping that people somehow magically process it, connect with it, and remember it, you can trust to the immense power of data storytelling to engage parts of the brain, trigger an emotional response, and encourage action. It’s what makes us human, right?

How to tell a story with data

Right then, now you know why you should tell a story with your data, let’s tackle how you weave a beautifully compelling narrative out of plain old facts and figures.

Much like the stories you’re used to reading (or avoiding since school, if you’re not a book person), data storytelling draws on the self-same narrative components. In case you need a refresher, a good story calls for characters, a clear setting, conflict, and resolution (we hate an open ending).

To put this into a data-related context, let us walk you through a relatable scenario: you’ve carried out some stellar research to discover why your company is suffering from a drop in sales. The data you’ve gathered shows that this worrying decline is down to one of those pesky social media posts going wild after someone had a poor experience with your customer service team. You do a little digging (analyse the data) and find that customers aged 19-35 are boycotting your brand. Yikes!

So how do you communicate that to the stakeholders in your company?

Here’s how to turn your data-based insights into a compelling narrative:

1. Craft your characters: The central characters in your story are the 19 to 35-year-old customers who are swerving your company due to a post about poor customer service. The stakeholders you’re presenting to should also be factored in, as the way you present your story will vary depending on your audience. You don’t need to talk about ‘characters’ in your presentation, but you need to have them clearly defined in your own mind.
2. Establish a setting: If your audience is going to really buy into the story you’re trying to tell them, it’s crucial that you set the scene for them. In the case of our example, you’d do this by painting a picture of the situation you’re addressing – sales have fallen because a viral post about poor customer service has driven away customers between the ages of 19-35. To bring your story to life, you can use visualization (like graphs and charts) to show this drop in sales.
3. Create the conflict: This is where you get to the heart of the matter. Your audience (stakeholders) need to understand the root issue, and what’s caused it. This would involve a deeper explanation of the social media post that went viral, because of a bad experience with a member of your company’s customer services team. To help illustrate the impact, you’d delve further into the importance of good customer service in today’s digitally led era, drawing on research to back up your findings and suggestions. Again, visualizations can really help here.
4. End with resolution: We all like a story to end well; or at the very least, to end with a definite conclusion – and it’s no different when you’re telling a story through data. Once your audience has a clear understanding of the issue you’re illustrating, it’s time to propose both an immediate, and longer-term, solution. This will be based on your data, and likely factor in external research into how to improve customer service, along with public perception of customer service. Visualizations can be useful to prove the need for investment, or to justify certain actions.

Examples of effective data storytelling

Data storytelling isn’t solely the preserve of data analysts and a boardroom of top stakeholders; in fact, loads of companies use data storytelling to better engage consumers!

Ready to see some effective data storytelling in action? Let’s go!

Spotify: Your year, wrapped!

Popular music app Spotify likes to remind its users of just how much value and enjoyment their app has delivered throughout the year. With a slideshow that recaps the year gone by, Spotify users can see how many minutes they’ve spent listening to music on the app, which songs have had the most plays, and even their favourite genres. The combination of visualization and storytelling really helps to establish a connection between product and consumer – and by using data to tell stories about the customer, Spotify successfully creates clear stories about the brand itself.

Google: Year in Search!

Google’s ‘Year in Search’ campaign uses data to tell a story about top trends in the year gone by, and what that data tells us about “the questions we shared, the people who inspired us, and the moments that captured the world’s attention”. Google factors in its audience by making the videos easily consumable (between 1.5-3 minutes in length), whilst highlighting heart-warming news stories that help to establish a positive association with the brand. Visualizations are also used to perfect effect in a way that really connects with the audience.

Uber: Your Year in the Rear-View!

Data, narrative, and visuals all combine to create a story worth listening to in the hands of taxi service Uber. The brand employs data storytelling each year to illustrate just how much value its users get from the service – giving miles travelled, days since joining, and star status. To create a more compelling and engaging narrative, ‘miles travelled’ are likened to something far more fun than how far users have traversed across the city (such as how many Olympic-sized swimming pools, or trips around the moon!)

How can Forsta help?

Creating effective data storytelling shouldn’t be a chore. With Forsta’s infographic storytelling, we help you to take your boring black and white data and turn it into stories packed with colour.

Our software makes it easy to bring your data to life: illustrating your insight with infographics that inspire, explain, and convince people to act. Whether you want to show the steps in a customer journey, spotlight the true scale of a knotty issue, or reveal trends that are trapped in your data, we’ve got it covered.

Using drag-and-drop tools, design libraries and no-snags export, you can work fast to tell visual stories with impact. We’ll even plug your data directly into your graphics – saving you the tedium of manual entry, and reducing the risk of human error.

Ready to see what we’re all about? Book your free demo now, and start crafting your own bestseller.

It’s story time!

After reading all of that incredibly useful insight, we bet you’re chomping at the bit to get going with your own top notch narrative, right? If  this has whetted your appetite, be sure to check out our other blog on storytelling in market research for more insight into this technique.

If you’re still feeling a little reticent, that’s understandable, too. The concept of data storytelling can be a touch intimidating if you’re not natural-born storyteller, but the great news is, you really don’t have to be!

Give it a go, and don’t forget to reach out if you find yourself facing writer’s block…

Here, we have picked out just six of the changes most eagerly awaited by customers. They also illustrate how our visualization capabilities are shaping up to be a game changer for the industry – and for your business too.  

Taken together, these enhancements support researchers and insight teams at every stage of their analytical journey. From exploratory analysis through to creating reports and presentations. From creating dynamic dashboards with stunning visuals to automating complex reporting streams delivered online or in PowerPoint. For a growing number of our customers, Forsta is now the only platform they need to use when analyzing and reporting data.  

Editable PowerPoint

Presenting data online, in an interactive dashboard, has always been a key strength for the platform. It was what attracted many of our long-standing customers in the first place. Yet sometimes customers want their charts to be output to PowerPoint as well – and sometimes they only want PowerPoint.  

For a long time now you have been able to take dashboard pages or slides created in StoryTeller and export them out to PowerPoint. These were faithful copies of what you would see on screen – but when you opened them up in PowerPoint they were static images. This meant the only place to make changes was to step back into Forsta.  Then, two years ago we added the capability to build native PowerPoint slides from within StoryCreator. Editable PowerPoint is available in the StoryTeller module as well, so you can enjoy the same benefits no matter which part of the software you are in. 

This means any PowerPoint slides you create can be opened and edited within PowerPoint. If you notice a small adjustment you wish to make, such as the color of a bar on a chart, or the text in a label, you can make the change directly within PowerPoint. This can be a real time saver. It also means anyone you send the PowerPoint can go in and tailor it for themselves, e.g. for presentation they are making to their colleagues.  

At the same time, Forsta now lets you fine tune the look and feel of the PowerPoint output from within the software by allowing you to create and apply actual PowerPoint templates. So the PowerPoints created can look every bit as good as all the ones you lovingly created the hard way!  

Clipboard

We noticed that people creating their own slides in StoryCreator often had decks containing several very similar slides. Clipboard lets you copy or drag slides you would like to reuse into a special holding area – your personal clipboard. You can then drag or copy any, or even all of the slides held in your clipboard to a new place in your existing deck, or on to another deck. You can then modify the pasted slides to make them into exactly the slides you want.  

Breaking out of the loop

It’s not unusual to find surveys that contain more than one level of data: households and individuals, passengers and journeys, owners and their different pets. It’s easy to collect this kind of data using a loop within the survey in Forsta but it can be a headache to deal with at the analysis stage. Now it is just as easy to analyse these and get the percentage base right whether you are working with StoryCreator, StoryTeller or Crosstabs. 

 At the flick of a switch, you can now decide whether the chart or tab you are creating should roll up all the looped responses for each respondent or count them separately. When you are presenting a looped variable, you simply pick which base to use for percentages or mean averages – households or individuals, passengers or journeys and so on.  

Smart legends

This feature has a heroic ring to its name – and quite deserves to.  It will be an absolute time-saver for anyone charting questions in StoryCreator that come from a loop or a grid in the original survey. These often suffer from very wordy and repetitive headings, like “Restaurant meals: favorite meal: Italian”, “Restaurant meals: favorite meal: Chinese” and so on – and that’s before we get to “Restaurant meals: would consider”! Labels like this look terrible on charts, and there’s always the danger the most important word in the caption gets chopped off due to lack of space.  

Smart Legends moves repeated text items from the legend into a new text heading above the chart, which makes the text easier to read without anyone having to make manual and time-consuming changes. In our example, “Restaurant meals: favorite meal” would be promoted to become the title for the chart while the legend for each series would simply say “Italian”, “Chinese” etc. 

Color me by value

Forsta can already make all sorts of automatic adjustments when it presents a chart so that they respond to the data they are presenting and make what matters stand out to the reader. Now, using new “value-based coloring” you can get the system to pick the color for each series in a chart according to its value. It could be red for scores below 5, or gold for 95% satisfaction – it provides another way for you to bring the story out of the data by highlighting what is remarkable or unexpected.  

Value based coloring works on column, pie and donut charts and can be done in both StoryTeller and StoryCreater.  

Year on year

The October 2021 release introduced several changes focused on smarter working with time periods in StoryTeller. Previously, if you wanted to give your report users the option to filter a report slide or dashboard on one period such as a specific month or year, you had to define the filter yourself – defining the labels and the date periods.  Now, you can add “native” time periods (weeks, months, quarters or years) simply by selecting the option – the system does all the work for you, and will even keep them up to date when you add more data covering new periods    

Even better, once you have a native time period filter in place, you can use it to select not just one period but previous periods on charts that show a trend across several periods. For example, if a line chart shows six consecutive months of data, and the report user picks June 2021 as a filter, it won’t restrict the chart to just show June, but do the sensible thing and show the previous five months too – i.e. January to June.  

Another improvement is built-in functionality to support “year over year” comparisons without having to do any additional work such as creating additional working variables in the background.  

And there’s more!

We have only touched on some of the recent enhancements. You can read about all the recent changes in the release notes. Don’t be put off – we have made them easy to read, and we always include an overview at the beginning that provides a quick at-a-glance explanation of what the big changes are and the benefits they bring.  

Our development team is already hard at work on making more improvements, so it won’t be long before there will be even more new features to explore.  

Module Buster – the main components of our Visualizations module explained: 

StoryTeller. Create charts and tables and organize these into slides which you can publish as a dashboard for your clients or other audiences to view as dynamic web pages.  You can also use StoryTeller to automate the creation of PowerPoint decks based on the slides you create.   

StoryCreator. Explore your data, test different analytical scenarios and create your own portfolio of charts and tables. You can also share with others who have access to StoryCreator, or by outputting what you produced into PowerPoint. 

Cross-tab. Build cross-tabular reports or ‘pivot tables’ as a way of examining your data and looking for relationships between different responses and groups of participants in your research. You can share cross-tab output with others by outputting it to Excel.  

E-news teaser

With three new releases and 36 new features added in just seven months, Forsta Visualizations continues to advance at warp speed. Even if you are a regular user of the platform, you may not realize just how much has changed, or how Forsta Visualizations is shaping up to be a game changer for the industry – and for your business too.  We’ve picked out some of the most eagerly awaited improvements… 

Thanks to the development of dashboard applications, access to high-quality data-related insights require minimum time and knowledge. One would think that this development would encourage the use of data and analytics across the board. However, though “digital natives” are quite good at exploiting data, many companies still fail to take full advantage of this opportunity. Why? Well, the challenge isn’t to simply introduce new technologies; teams need to adopt new ways of thinking and learn new skills to continuously apply in their day-to-day work. One of these skills is visual storytelling – which will be the most widespread means of using analytics in 2025, according to Gartner.

What distinguishes visual storytelling from classic data visualization?

Visual storytelling tells a story with the help of infographics. The combination of text, images, symbols and diagrams creates communication that is both entertaining and effective. Information and entertainment merge to produce infotainment. Classic data visualization, on the other hand, uses tables and diagrams and often views information and data too abstractly and in isolation. It is thus easy to overlook interrelation-ship and principles of effect. Visual storytelling is the answer to the ever-increasing flood of information and needs to democratize data.

What makes for good visual storytelling?

Like any good story, the visual version starts with an event that is worthy of a more detailed look. This can be, for example, a recent problem or opportunity that has emerged. A good visual story establishes connections between facts, emotions, frameworks, attitudes and courses of action. It thus establishes the causal relationships between all relevant facts. Whether implied, hypothesized or asserted, causal relationships are crucial for a story to work. In other words: In a story, everything happens for a reason.

Stories in the form of infographics can be compared to narrative sequences. They provide a visual, narrative path through the relevant facts. Infographics guide the viewer through the world of data rather than simply throwing them into it. These stories put facts and their interactions in a specific order, making it easier to gain insights and derive measures more effectively.

What effect can visual storytelling have?

1. Visual storytelling is a valuable tool in change management. Storytelling cannot do without a strong problem-solving orientation, so change is already built into the story. A good story promotes improvement and development. It lets the audience know the ways and circumstances under which a change is possible, stimulating the audience to think further.
2. Consistently applied visual storytelling enables data silos to be torn down. A wide variety of departments collect and maintain lots of interesting information. However, this information is trapped in silos and often lacks a holistic view of customers, markets and business processes. Storytelling is directly tied to addressing problems. It inadvertently must include as many perspectives – and thus data – as possible in the analysis from various departments and systems.
3. Visual storytelling is the key to democratizing business data across the enterprise. An ever-increasing number of stakeholders require actionable and decision-relevant information. However, many of these stakeholders are not familiar with data and analytics. By using infographics, complex issues can be illustrated to these users in a way that is easy to understand and reliable in terms of interpretation. This leads to data being used more widely and frequently in decision-making processes.
4. Visual storytelling fosters collaboration across the enterprise. Unlike abstract diagrams, stories allow us to talk about insights and solutions. They help us understand causes, effect relationships and principles and, more importantly, share them with others. This results in collaboration that better supports teams to create innovative solutions.

Summary

Used correctly, visual storytelling is a key skill that all employees need to learn. This is because visual storytelling supports change processes, helps break down data silos and promotes the democratization of data and collaborative decision-making processes.

Surveys customized. Workload downsized. 

Global research firm KS&R run a lot of surveys. And with unique needs for every client, these surveys can get really complicated.  

KS&R’s speed and efficiency has stepped up a gear since they started using Forsta. Their VP and Principal, Chris Reimann, said it was all down to customization.  

KS&R’s quantitative online surveys now have more interesting-looking questions that engage the user more. Their questionnaires’ complex routing now works as a plus, because Reimann’s team can automatically adjust the wording to personalize it for each participant.  

On top of this, Forsta handles multiple languages and adapts effortlessly to mobile displays – saving Reimann’s team time and money. 

More participation. More visualization. 

These more interesting, easy to follow surveys get far more responses. So KS&R’s fieldwork progresses faster. This all makes for more data, which goes into creating better insights for clients. 

To better challenge these insights, KS&R switched to Forsta Visualizations. This made it easier for them to get creative with how they filter and present their findings. And even with this more creative, iterative approach, their reporting takes four days less, per project, than it did before. 

But we’re still nowhere near Forsta’s limits 

When you put powerful tech in the hands of creative people, it’s only a matter of time before a sentence begins with ‘in theory, could we…’ 

This is often how we discover brand new things our platform can do. Clients like KS&R come to our in-house experts for a totally new challenge, and we create a brand-new solution we’d never even thought of before. As Reimann says: 

“Forsta has been a very good partner when we need to do something different.”