How Data Discovery Software Uncovers Stories

This is the transcript of a Webinar hosted by InetSoft on the topic of "How Data Discovery Software Uncovers Stories in the Data." The speaker is Abhishek Gupta, Product Manager at InetSoft.

Understanding Information Needs

Now one of the challenges is when you look at information needs across an organization, it’s not all consistent. Management is looking at different levels of information trying to make strategic decisions. Business analysts are trying to do more in depth drill down and different things with it. A lot of frontline staff will actually need to access information to make tactical decisions. And so this is not monolithic, and that’s one of the challenges as you think about business intelligence applications and how to apply them.

So far the focus of business intelligence has been primarily on management and that means operational reports and scorecards and dashboards so they can understand performance at a high level. However, the staff need to slice and dice data, look at trends and so forth to make decisions. For them just the summary is not enough.

Reports, scorecards, and dashboards fall short because they generally show summary, not the detail, it's time consuming and difficult to put these things in and they consume a lot of IT resources. And as a result the staff are largely unsupported, and there is a whole bunch of research that would, support this high level observation. And it gets worse because once staff go to IT or central reporting for custom reports, they often get the wrong data, too late. To get the right data it is a time-consuming, iterative process, and that’s what we are going to focus on today.

Just as a premise or a foundation, there are four key aspects of business intelligence. At the bottom it's getting the data out of the source system and storing it somewhere, usually in a data warehouse or OLAP server. Or at least this used to be the way it was done. Now with data mashup software like ours, you don’t need to do this. Next comes understanding the data. This is where reporting starts getting into the game. Then ultimately it’s really creating human knowledge out of it so you can make decisions.

Most of the effort has been on these bottom layers of the stack which are obviously fundamental to do in order for the top layers to work, but most of the value now is going to come from getting the top part of this right. So focus on the top two layers understanding information and making decisions.

Operational reporting is a well-established solution and discipline. A number of large players like Cognos and Business Objects and Microsoft have done that fairly well. Our reporting solution is equal if not better. Advance analytics has evolved fairly well with some of the large BI vendors, and there are specialists who do that really well such as SAS and SPSS. There are a bunch of tools that the hardcore business analyst or trained statistician can use.

Example of Data Discovery Uncovering Stories

Data discovery has become a powerful tool for uncovering hidden stories within complex datasets, transforming raw numbers into actionable narratives that drive business success. Consider the case of a regional coffee shop chain using a data discovery platform like InetSoft’s StyleBI to analyze customer purchase patterns. By integrating point-of-sale data, customer feedback surveys, and social media sentiment, the chain’s marketing team created an interactive dashboard to explore sales trends. Through ad-hoc querying and visualizations, they discovered a surprising story: a significant uptick in oat milk latte sales among younger customers in urban locations during weekend mornings, revealing an untapped demand for plant-based options that led to a targeted marketing campaign and a 15% revenue increase.

The data discovery process began with the team leveraging StyleBI’s drag-and-drop interface to mash up disparate data sources, including transactional records from their POS system and Twitter API feeds for customer sentiment. Interactive heatmaps highlighted that oat milk lattes were most popular in specific stores near universities, while time-series charts showed peak sales between 8 AM and 11 AM on weekends. This granular insight, uncovered through self-service analytics, allowed the team to drill down into demographic data, revealing that customers aged 18–25 were driving the trend, a story that would have remained hidden in aggregated reports. The platform’s real-time capabilities ensured the dashboard reflected fresh data, enabling the team to act swiftly on this emerging preference.

The uncovered story had a transformative impact, as the coffee chain adjusted its inventory to stock more plant-based milk options and launched a social media campaign promoting oat milk lattes to its target demographic. Collaboration features in StyleBI allowed the marketing team to share the dashboard with store managers, who added annotations to suggest promotional strategies like student discounts. This example illustrates how data discovery empowers organizations to move beyond static reporting, revealing actionable stories that drive strategic decisions, enhance customer engagement, and boost profitability in a competitive market.

Next: The Problem with the Standard Business Intelligence Model