How AI-enabled data democratization helps you build better experiences—and a better business

What is AI-enabled

Data democratization is about empowering everybody in your business, regardless of their technical background, to access and use data to make better decisions. 

This is achieved through enacting a technological and cultural shift within your business. Teams need both tools and training to access and use data with confidence—without assistance from data specialists.

AI-enabled data democratization facilitates and radically accelerates the process of transforming into a data-driven organization by automating the surfacing of insights, seamlessly integrating these insights into workflows, and enabling everybody to analyze and ask questions of the data using natural language inquiries. 

How does AI-enabled data democratization help improve your customer experience?

With an AI-powered self-service analytics solution like Heap, teams can…

Make better decisions, faster 

Having access to advanced insights enables more informed, and therefore more confident, decisions. Moreover, teams can easily measure the impact of these decisions in near real-time, and make adjustments based on this analysis on an ad hoc basis.

Data democratization massively shortens time-to-data by removing the bottlenecks associated with having to request access and analysis. 

Today’s business landscape moves at a breakneck pace; self-service analytics allows your teams to make the rapid, data-driven decisions required of them.

• Executives can easily gauge high-level performance metrics at a moment’s notice. For example, they can see how many active monthly users your mobile app has and compare that metric to last month’s performance. 

• Product managers and developers can easily track product usage to identify popular and unpopular features, helping them to prioritize which features to focus on. They can also deep-dive into the behavioral data to understand why some features aren’t being used. And they can run experiments to optimize and get rapid feedback on what works and doesn’t work. All without contacting an analyst.

• Growth teams can easily track usage, acquisition, and retention rates  

• Marketers can use the data to understand customer preferences, experiences, and campaign performance. They can also adapt faster to changing market conditions and customer preferences

• Developers can seamlessly integrate analytics into their workflows, drawing on AI-generated summaries of usage and performance data to guide (and back up) their strategic decisions

Unlock a more collaborative culture

If everybody can access the same data, everybody can communicate with it. Data becomes a common language between teams that can be shared, understood, and used to drive cross-functional collaboration. 

• Developers can share insights with product managers to get buy-in for optimizations 

• Product managers can share their insights with executives to support strategic initiatives 

• Marketers can share data with sales and customer support to help support campaigns

When data is shared and used by every team at every level, this naturally creates a culture where data is valued for its role in making better, more informed decisions. 

Be more efficient, freeing up time for innovation 

When the time-consuming and resource-hogging process of data analysis is streamlined, there’s much more space for innovation. 

• Product, growth, and marketing teams can run data-driven experiments without having to wait for access or assistance from your data analysts 

• Data analysts, meanwhile, no longer have to deal with endless requests and tickets. Instead, they can focus on innovative, advanced data work, such as developing AI and machine learning models. 

2 key challenges blocking data democratization—and how to address them

Challenge 1: lack of access to high-quality, secure, compliant data

Teams can be given access to all the data, but if that data isn’t standardized, centralized, organized, accurate, or complete, it won’t do them much good. Data has to be trustworthy before it can be valuable. 

An effective data democratization initiative also has to ensure that your company’s valuable, confidential information is secured with rigorous access controls to avoid data breaches and noncompliance with GDPR, CCPA, and DPP.

Solution 1: data governance

Data governance is a set of processes, policies, and standards designed to manage how your company accesses, handles, and protects data.

While data democratization involves helping a wide range of teams use data productively, data governance ensures the data they’re using is accessible, reliable, trustworthy, and secure. 

Data governance is, therefore, an essential prerequisite of data democratization. Without it, reporting will be inconsistent and untrustworthy, and may result in security breaches and noncompliance. 

Challenge 2: lack of data literacy

Most teams aren’t data literate. This means that even if they have trustworthy data insights to work with, they’re liable to misinterpret, misapply, and mistrust them. 

Not all data analytics solutions help bridge this literacy gap. Highly complex, technical, and inaccessible solutions further reinforce the harmful message that data is for specialists only. 

Solution 2: user-friendly self-service analytics

Data literacy has its limits. While it’s advisable to educate your workforce on the effective use of data, training everybody in your business to be a data specialist is impractical. 

Instead, you need to provide user-friendly self-service analytics tools everybody can rapidly onboard onto and get to grips with. 

Ideally, these tools should leverage generative AI to make data analytics intuitive and accessible to everyone in your business.

What does a self-service analytics platform need?

The right self-service analytics solution can catalyze data democratization by enabling easy access to timely, trustworthy, and actionable insights throughout your business.

The wrong solution, on the other hand, can simply compound the problem of users feeling unable to run data analyses for themselves. 

Here are some common mistakes businesses make when selecting a self-service analytics platform:

• Data isn’t cleaned up and organized first, so no matter how powerful the solution is, the results aren’t consistent, trustworthy, or secure 

• Lack of adoption due to complexity. Remember: if teams can’t use your solution, they’ll default back to asking a data specialist to help them—and you’re back to square one.

• A tool that still requires users to run SQL analyses (which most users aren’t trained to do) 

• An over-reliance on static dashboards instead of real-time insights

With the need to avoid these pitfalls in mind, what you need is a self-service analytics platform that is

• Backed by robust data governance, so data is accessible, secure, and compliant, and can be easily accessed by those who need it

• User-friendly, so every user feels able to run reports, segment and analyze findings, and run experiments 

• Able to automatically alert users to insights, so important events and data trends aren’t missed

• Able to report on data in near-real time, so speed to insight is optimal

How Heap enables true customer data democratization

Heap is a digital insights platform that’s designed to help you create great digital experiences for your customers. 

Over 10,000 companies use Heap to understand their customers’ digital journeys and optimize them for conversion, activation, retention, and revenue.  

Data democratization is a huge part of what makes Heap so effective. Here’s how the platform makes it easy for your teams to access and action trustworthy customer data insights.

Everyone can be an analyst with AI CoPilot 

Heap’s AI CoPilot is a built-in AI assistant that makes it easy for anyone to start analyzing data and get valuable, actionable insights within minutes. 

Talk to your data (in plain English)

Like Chat GPT, AI CoPilot is powered by generative AI. This means you can talk to it (about your business’s customer data—for everything else, ask Google.)

Let’s say you want to know how many users viewed your blog last week. However, you’re not a data specialist, so you mightn’t know exactly which events, segments, properties, filters, and groupings you need to analyze to find this out.

Good news: your company has implemented Heap’s AI CoPilot, so it’s easy to find out. Just ask AI CoPilot in plain English: “How many users viewed the blog last week?” 

In a matter of seconds, AI CoPilot will answer the question for you, drawing on a combination of analytics data and Heap’s help center documentation, best practice guides, and online training materials.

Auto-generate chart summaries 

AI CoPilot automatically gives your chart a name and description and highlights key choices, such as events, groupings, or filters, that impact the analysis.

This isn’t just for your benefit: it also means you can send the chart to your colleague and they’ll instantly understand what they’re looking at.

Ask follow-up questions 

Once you have your chart, you can ask AI CoPilot further questions about it—including how AI CoPilot arrived at its answer.

What’s more, AI CoPilot will use its auto-generated description on the chart to suggest further questions you can ask. This makes iterative analysis quick, easy, and (potentially?) fun.

You can find out how many viewed your blog the week before, or a year ago—and AI CoPilot can help you figure out why that number’s changed by detecting shifts in user behavior and highlighting potential influential factors. 

Get automatic insights via Heap Illuminate 

You don’t have to ask AI CoPilot to get insights into the user experience from Heap. The platform’s data science layer, Heap Illuminate, does so automatically, by continuously analyzing your customer data and surfacing relevant insights.

Illuminate shows you what’s working and what’s not in your user journeys, bringing hidden drivers of conversion and points of friction to your attention. Here’s how it works:

• Autogenerated Top Events tables let you track the key events happening between 2 moments in your conversion funnels, revealing hidden contributors to CRO 

• Effort Analysis shows you what users are doing between steps in your funnels, how long they’re taking, and what percentage return to complete the funnel after leaving

• Group Suggestions identifies and compares important user segments when you run a graph or funnel query—comparing, for example, the behavior of new vs. returning users

• Pageview Suggestions automatically recommends pages on your site to define as events within Heap, so you can dive deeper into user interactions on those pages 

• Rage clicks alerts flag when users are clicking rapidly and repeatedly without moving forward—a clear sign of mounting frustration. This helps you quickly locate bugs, slow response times, or UX-related confusion

Data you can trust

Unlike other analytics platforms, Heap is set up to capture, label, and secure all of the behavioral data your teams need to understand and optimize user journeys. 

Autocapture ensures you have all the data you need 

Most analytics platforms require you to manually tag events. Aside from taking up a lot of time, this means you can never be sure you’re dealing with a complete dataset.

Heap removes this uncertainty. Just enter a single snippet of code and Heap automatically captures 100% of user behavior across web and mobile. Plus, you can enrich this dataset with custom tracking, using APIs to capture client and server-side events.

Manage and secure your evolving dataset with built-in data governance 

Not only is the data you capture with Heap guaranteed to be complete—it’s also guaranteed to be clean and trustworthy. Heap’s data governance tools make it easy to manage large datasets and generate trustworthy insights. You can

• Maximize accuracy with data that’s structured, labeled, and stored in a way that promotes consistency

• Easily keep your data up-to-date as your digital experience evolves with automated alerts letting your admins know when events need to be verified, repaired, or archived

• Secure your data and ensure compliance with robust access control and permissions thanks to Heap’s suite of highly accessible privacy and security tools

Getting started with Heap and AI CoPilot

Heap’s AI CoPilot is designed to help you unlock insights faster. No need for any SQL or manual data exploration—you can get started in 5 simple steps. 

Step 1: Define your business goals

Before diving into the data, identify the top 2-3 questions you want answered. For example, you might want to know “How are users engaging with our latest feature?”, or “Where do mobile users drop off the most in our onboarding flow?”, or “Which user segments convert at the highest rate?”

Step 2: Ask AI CoPilot direct questions for instant insights

Now you know what you want answered, it’s time to ask AI CoPilot. Non-technicians needn’t worry: there’s no need for technical jargon. Your questions can be phrased in plain English. (Plus, you don’t need to apply any filters.) Here are 3 example queries to give you an idea: 

1. “What are the biggest drop-off points in my checkout funnel?”

2. “Which user segments have the highest churn risk?”

3. “How does behavior differ between mobile and desktop users?”

Within seconds, AI CoPilot will provide you with a concise, AI-generated summary that highlights patterns, anomalies, and key behavioral shifts. 

Step 3: Ask follow-up questions…

So you’ve got your answer—but that answer might easily throw up more questions. 

For example, once you find the biggest drop-offs in your checkout funnel, you might want to know if this varies between new and returning customers, or if its affected by region, or if the drop-off points have changed since last quarter. 

Luckily, AI CoPilot allows you to ask as many follow-up questions as you need to, so you can dig deeper into every insight you get from it.

Don’t have any follow-up questions in mind? AI CoPilot will help you out there, suggesting up to 3 follow-up questions you might want to ask based on questions others at your organization have asked previously.  

Step 4: And get AI-powered suggestions

AI CoPilot will also help you act on what you’ve found, suggesting what you can look at next, and providing relevant general best practices to follow. 

Step 5: Set up alerts and continue to iterate

AI CoPilot helps detect anomalies and behavioral shifts, so you can proactively monitor key trends. You can set up alerts yourself, such as:

• “Monitor checkout completion—notify me if it drops by more than 5%.”

• “Track feature adoption—alert me if usage declines week over week.”

Final thoughts: Your AI-powered growth assistant

By leveraging Heap—and in particular the platform’s AI CoPilot—anyone in your business can quickly surface key insights, identify trends, and make data-driven decisions without the manual effort. 

This is analysis for everyone. Whether you’re a product manager, growth marketer, or data analyst, Heap’s AI-powered analytics is here to help you turn data into action, faster than ever.