What are Data Insights? Examples and How to Get Them

Data insights are the “” that emerge after you spot meaningful patterns or trends in your data.

Unlike raw numbers that simply show you what happened, insights reveal why things happened and what you might do about it.

You might have noticed that 70% of your customers abandon their on Mondays. As useful as this information is, it’s also just data.

Data insights emerge when you uncover that abandonment is occurring because your website slows during the Monday lunch hour traffic spike—an actionable finding. For example, you could boost your server capacity or optimize site performance specifically during these peak times to reduce the likelihood of customers abandoning their purchases.

Valuable insights such as these usually share three key traits:

• They reveal something you didn’t already know
• They point to specific actions you can take
• They help solve real business problems

In short, data insights (in addition to giving you an idea of where things stand) enable you to see the best course of action and help you avoid costly mistakes.

, insights, and are often seen and work together. Depending on your business and industry, the terms can also overlap or be used interchangeably.

However, there are some distinctions:

• Data tells you what’s happening
• Analytics shows you the patterns
• Insights address the “so what?” and “now what?” questions

Say a fitness app collects information, including daily step counts, workout time, and user locations—this is the data. When the company runs analytics, it processes this data to look for trends and patterns. For instance, the company may want to explore its most active or “successful” users.

The insights appear when the company discovers that users who work out with friends are three times more likely to stick to their fitness goals. This information could lead the app to release helpful new social features, such as adding friends or syncing up workouts.

When used together, these three elements (data, analytics, and insights) can result in some first-rate decision-making. Data captures the past, analytics helps you understand the present, and insights light the way to the future.

Pretty much all successful companies use some form of data insights in their decision-making process. Data insights help you spot opportunities before they become too obvious (and your competition has got hold of them) as well as obstacles before it’s too late.

Take from DVD rentals to streaming giant. The company recognized early on that, thanks to rising internet speeds, audiences would likely want instant access to content. Over time, user data also showed that people often watched more than one episode of a show (which was originally released weekly) in one sitting. The insight? Modern viewers craved binge-worthy content on demand.

This realization led Netflix to revolutionize how we consume , turning it into an anytime, anywhere experience. The platform now often drops entire, original seasons at once and has fundamentally changed its business model—and influenced its competitors.

In a more general sense, data insights matter because they:

• Cut through gut feelings with solid evidence
• Identify problems while they’re still minor and fixable
• Find hidden opportunities your competitors might miss
• Help you save money by showing you where you’re wasting resources
• Enable you to understand what your customers want (not just what they might say they want)

With insights, you gain confidence. When you need to make tough decisions—such as whether to or enter a new market—having robust data insights to back up your decision helps you move forward with purpose rather than just hope.

The data insights process is a careful blend of data collection, and organization, analysis, interpretation, and presentation.

Let’s go through some of these stages in more detail.

Ask the right questions

Smart questions shape everything that follows. It’s best to start broad with what you’re looking for, then narrow down:

• Begin by analyzing your business goals. For example, if your overall aim is to improve retention, you might ask, “How can we reduce customer ?”
• Break this main question into specific queries, such as, “What do customers use right before canceling?”
• Define your . For example, you might identify the top three reasons for churn and reduce them by 15%.
• Challenge your assumptions. Are you sure price is the main reason people leave? Could something else be affecting churn? Going in with an open mind will mean you’re more likely to get effective (maybe unexpected) insights.

Gather clean data

Next, it’s time to collect your information. For this, you’ll need to:

• your data sources. You might get information from , usage logs, support tickets, sales data, and more.
• Create a checklist. Look for completeness, accuracy, and timeliness in what you gather.
• Watch for red flags, such as duplicate entries or outliers. These could skew the end results and lead to poor decisions.
• Document where your data comes from and any limitations. Do certain rules or regulations dictate how you can use the data?
• Consider requirements. Ensure the information is properly organized so that those who need to access and analyze it can do so, and it’s safe from potential breaches.

Spot patterns and trends

This step is where both technology and human insights shine.

• Use tools to make trends more visible. Where are the obvious spikes? The dips?
• Compare different time periods to spot seasonal effects—this is particularly important if you offer seasonal products or time-sensitive promotions.
• Look for between different metrics. How does traffic volume relate to conversion rates, for example?
• Identify typical and exceptions so you can clearly see when patterns break their usual rhythm.
• Group similar findings into broader themes. For example, you might categorize customer feedback into themes such as product quality, shipping issues, and customer complaints to help you identify areas for improvement later.

Ask, “Why?”

Once you have the analytics, you need to question it.

• Start with your surface-level findings, such as, “Email opens are down.”
• Dig deeper. You may realize that open rates dropped after you changed the style of the subject lines.
• Consider the context. Are your competitors’ emails experiencing similar trends? Could it be a shift in the general industry or economy? Has something unexpected happened (such as a snap election, global pandemic, etc.) that could influence open rates?
• Challenge the more obvious explanations. Is the dip due to a change in the subject line, or did you also change the time you sent it?
• Get input from different teams to help you see multiple perspectives. What one team misses, another might catch.

Turn findings into actions

The final stage involves transforming your new understanding into noticeable impact and improvements.

• Create a clear narrative that connects data to your decisions on what to do next. Use this thinking to develop specific, actionable recommendations and prioritize each action based on the value to your customers and business.
• Estimate the potential impact of each action. How much do you hope this change will improve email open rates? Give a (realistic) figure that your team can track against.
• Identify what could go wrong (e.g., your change might lead to fewer opens or other unintended consequences) and how you’ll monitor progress and react if things go south.
• Will you instantly roll the change out to everyone? Build a timeline for implementation and perhaps explain how you’ll use things such as blue/green deployment or to help minimize risks.
• Set up ways to of your changes. Which metrics matter most to you? These should relate to your original business questions and help you see whether you achieve the desired outcome.
• Finally, get buy-in from stakeholders and the teams that will help implement the changes. Present your plan and ensure it’s clear. You can’t push ahead without their approval!

It’s helpful to remember that obtaining data insights isn’t a one-time thing. Once you’ve reached this “final” stage, you’ll likely have more questions that need to be answered—and so, the cycle starts again.

The best insights solve today’s problems and help you spot tomorrow’s opportunities.

Bonus tip

Each step of the data insights process should flow naturally into the next. If you’re struggling, it often helps to step back and revisit earlier stages.

Sometimes, what looks like an analysis problem is, in fact, a question-framing issue, or what seems like a data problem might be solved by asking different questions entirely.

The real skill is knowing when to dig deeper and when you have enough insight to act. Perfect data isn’t the goal—making better decisions is.

Successful companies never release a based purely on a hunch. Instead, their product teams use data to guide their journey from idea to launch and beyond.

Data insights help product developers:

• Find pain points before they become deal-breakers
• Spot features users love but perhaps never outright asked for
• Understand why some features go unused
• Identify which improvements will have the most significant impact
• Know when to get rid of features that don’t deliver user value

The teams might gather these insights from:

• Usage patterns and
• Customer feedback and support tickets
• results
• Abandonment points
• Time spent on features
• Error rates and crash reports

The key is to connect the dots of this data to understand the “why” behind user behavior. Many product data insights often challenge your hard-worn beliefs or assumptions.

In some cases, what users do speaks louder than what they say (or don’t say) they want.

When Adobe noticed many of its customers were using Photoshop only for basic photo editing, its insight wasn’t just to simplify its tools and scrap the more advanced stuff. Instead, the company understood that more casual users needed entirely different workflows than professionals.

As a result, Adobe has since developed separate product lines—including —for more hobbyist photographers and editors, something the brand wouldn’t have been able to do without digging into the insights.

use data insights to help them find the audience with the right message at the right time. They can have a conversation with thousands of customers, while knowing exactly what each one cares about.

Teams use data insights to:

• Predict when customers are ready to buy—and when they’re wavering
• Find unexpected customer
• Maximize ad spending across different channels
• Craft messages that resonate
• Time campaigns so they’ll have the biggest impact

Much like product development, the magic often happens when your data insights reveal the unexpected in your marketing strategies.

Maybe your best customers aren’t who you thought they were, and your most effective marketing message is hiding somewhere in your customer service logs.

Just look at how used insights to disrupt the razor industry. The brand used market analysis and consumer to see how people weren’t loyal to big razor brands—they were just frustrated by high prices and forgetting to buy replacements.

This insight shaped the company’s entire : Dollar Shave Club’s messaging largely focuses on convenience (highlighting its subscription, model, and lower cost) rather than premium features or luxury branding—many traditional razor companies have since followed suit.

Turning masses of data into clear, actionable insights doesn’t need to feel daunting.

helps you cut through complexity, giving you a complete view of your customer’s digital journey.

• Real-time behavioral analytics show what your users do and why they do it
• insights help you spot meaningful trends before they land in your competitors’ laps
• mapping reveals hidden patterns in how people use your product—not just what you think they do
• Automated insight detection flags significant changes and opportunities you might have missed with manual processes

The provides the insights you need to make confident decisions—all backed by solid data.

Whether fine-tuning a feature or planning your next big launch, you’ll know exactly where to focus your efforts for the most considerable impact.