Data Discovery Software

How can BI tools help users discover data?

One of the major advantages of modern business intelligence software is the democratization of data. With legacy BI tools, data analysis is the domain of IT professionals and data scientists, who are the only people who can access the tools necessary to do it. These tools are often slow, complex, on-premise tools that the average user couldn’t navigate even if they wanted to.

The entire company relies on their data team and IT staff to build out dashboards, reports, and visualizations. The end user’s entire data experience is curated by the people actually interacting with the tool; the end user isn’t actually interacting with the tool or the data in any meaningful way.

With the advent of cloud-based, user-focused business intelligence software, even the non-trained user can interact with and utilize data. The average employee now has the power to build their own dashboards, reports, and visualizations, without any input from a data professional.

A user-facing dashboard in Sigma.

The average user now can interact with their own data to draw out insight. This new paradigm in business intelligence design is known as data discovery. Through data discovery, users can collect and analyze data from many different sources to find patterns, trends, and insight all by themselves. Data discovery is the idea that any employee can be a data expert.

Fundamentals of data discovery

Data discovery tools help the average employee do the sorts of complex data analysis and transformation that would require a data professional in a legacy BI tool. To understand how data discovery tools improve on legacy BI systems, it’s necessary to briefly explain how BI tools process their data.

All BI tools use some sort of query language to sort and process their data. The most common query language is called SQL, which is the industry standard.

Think of a query language as a BI tool’s language. For a BI tool to properly execute a command, that command has to be expressed in terms that the BI tool can understand. In the past, this meant users had to talk to their BI tool in that tool’s query language. Naturally, this was a very involved process that necessitated a massive amount of technical expertise.

Some BI tools, like Looker, allow their users to interact directly with their tool's scripting language. 

Modern data discovery tools have workarounds for this process. They have what’s known as a semantic layer between the end user and the inner workings of their tool. This semantic layer takes a user’s basic inputs—things like clicks, drags, drops, and text searches—and transforms them into SQL queries so that the BI tool can understand what’s happening.

The semantic layer acts as a translator between the actions and inputs of the user and the inner mechanics of their tool. This allows employees to interact with complicated business intelligence systems as if they were any other piece of software.

Data discovery tools leverage the power of semantic layers to streamline and simplify their user experience. These tools are just as powerful as many tech-focused systems, but are far more user-friendly. Any user can use data discovery tools to perform complex data analysis or build complicated visualizations without needing much in the way of data expertise.

Features of data discovery software

Data discovery tools rely on their suite of user-focused, intuitive data tools to help average people discover trends and patterns in their own data. While the exact feature set might differ from tool to tool, there are a few common features that all tools should have.

Visualizations and visual analytics

Data discovery tools help users to see datasets and analytics in a visual way. Raw data is almost impossible for the average person to analyze without special training; people just can’t understand raw numbers in a very useful way. Data visualization tools help to transform numbers on a page into clear insight by translating a data stream into some kind of chart, graph, map, or plot.

Users can build these visualizations themselves without any external help. Many tools let end users edit or change visualizations with a few keystrokes, which helps them to see the visualizations in the most helpful way.

When data is visualized in these ways, it’s much easier for those without any data training to see trends and patterns. When a data stream is a few entries in a database, it’s difficult to see a trend, but when those numbers turn into a line on a chart, it’s easy to see how that line is moving.

Most BI tools allow users to create and edit data visualizations, but data discovery tools go a step further. They allow for further interactivity, which helps end users understand their data on a much deeper level. For instance, a user may be able to mouse over an abstracted line on a chart to see exactly what data point that line corresponds to.

These added interactivity features are called visual analytics. They allow users to investigate the data that underlies their visualizations and draw conclusions that might not be visible at the surface level. With visual analytics, users can do ad-hoc analytics right on their visualizations, without needing to reach any deeper into their BI tool.

Visual analytics also help a user to follow their curiosity; a user can mouse over a visualization to find out the value for a specific data point, click on that data point to drill down and get more context, and from there start interacting with the data directly, leading to user-driven insight. Since users can navigate their tool like any other piece of software, it’s much easier to go on these sorts of data diversions.


In many cases, a BI tool doesn’t need any user input to transform data. Many tools can automate certain steps of the data analysis process, which helps to streamline and promote data discovery.

Automations are tasks that a human could do, but an algorithm or code routine handles instead. A BI tool might automate some tasks because a computer can do it faster (like with data cleansing or normalization) or because automation might streamline workflows in some way to make a task easier for a human to do.

Automations are extremely helpful for the average employee looking to navigate a complex business intelligence tool. Instead of knowing the intricate process to implement some data analysis process, they just need to know how to properly implement the automation.

Many automations help to do away with the busywork of a BI tool. Many complex and time-consuming tasks can now be handled in just a few seconds with automation. As long as the automation works correctly, the end user doesn’t have to worry about the process.

For example, in a legacy BI tool, data experts would need to do a lot of work to transform raw data into a form that could be used for analysis. Data experts had to remove inaccurate, incomplete, and inconsistent data, and they had to convert all of the data points expressed in different ways into a consistent form. This process, known as data cleansing, was a tedious, time-consuming task.

Now, automated procedures can cleanse data in just a few seconds. The key here is that the end user doesn’t need to do any of that complex work to analyze their data; automations allow the end user to get right to the important data analysis.

Users don’t need to know the details of what's actually happening with their tool. Automations allow for another layer of abstraction beyond the semantic layer; they mean that users don’t need to interact with levels of the BI tool that aren’t important for their analysis.

Ad-hoc analytics

In a legacy BI system, end users can’t do almost anything themselves. Even the most basic BI tasks, like building out new visualizations and connecting new data streams, have to be done by those that know the system inside and out. Not only do end users rarely have access to these tools, they wouldn’t even know how to do those things if they wanted to.

Modern, cloud-based BI tools often allow end users much more freedom in how they can utilize their data. In many tools, it’s simple for anyone with access to a data stream to build new visualizations off of it. Users can even manipulate their data in new ways; for instance, a user might bring three different data sources together into one visualization or seperate a consolidated data stream into its component parts.

When a user manipulates data in a novel way without any input from data professionals or IT, it’s known as ad-hoc analytics. Ad-hoc analytics is an important tool for driving data discovery. It allows users to interact with data in a much more personal way and helps uncover novel modes of analysis.

When users have to contact IT to do even the most minor analytics, they won’t unless they have a really compelling reason to. In most cases, they’ll make do with the analytics and visualizations they have. It’s difficult to find novel solutions to problems with the same old analytics, and without the ability to easily implement new analytics, businesses will stagnate.

Ad-hoc analytics help end users interact with data in new ways. Users can easily build out new analytics and visualizations, which can help them to find unique solutions to novel business problems. It’s also easier to find novel solutions to old problems, which can help drive efficiency and save cash.

Data discovery—democratize the data process

Data discovery is exponential. Once end users start interacting with data in one way, they’ll get more comfortable interacting with data in other ways. Interacting with visual analytics helps users understand data further, and that data journey can inspire them to investigate deeper. At that point, they may want to do ad-hoc analysis to do completely new analysis and drive novel insight.

It doesn’t take a data expert to drive insight. For many people, the limiting factor preventing them from using data to its fullest isn’t a lack of ingenuity or a lack of curiosity, but a lack of access to the tools they need. Data discovery tools make it as easy as possible for everyone in an organization to leverage data for insight.

Interested in learning more about how data discovery tools can transform your business? Contact us today for a no-cost, no-obligation consultation. Our team of experts will go over your business and use case to connect you with the BI tool that’s the best fit.