Click to learn more about author Ajay Khanna.

There is currently a massive insights gap caused by silos between business intelligence (BI) dashboards and machine learning (ML) and AI tools that is preventing organizations from capturing new business opportunities hidden in their data. According to a study from the Boston Consulting Group and MIT Sloan Management Review, 59% of companies have an AI strategy but only 10% report significant financial benefits from implementing it. This investment in AI tools hasn’t been matched with new BI tools, as many organizations aren’t getting what they need from their existing BI dashboards. 

The legacy BI applications that many organizations rely on were built more than 20 years ago around older data structures that pre-aggregate results, don’t support machine learning, and feature pre-defined drill paths and static dashboards. These static dashboards created by BI applications are key to self-service BI, allowing users to visualize data and interact with elements according to a specific set of rules created by IT professionals and data analysts. But, if a question requires more information beyond the BI dashboards’ preset functions, new requests have to be sent to IT in order to delve into the findings. Simply, they’re not up to snuff.

So, what can businesses do? To solve for challenges created by traditional BI platforms, organizations need to invest in solutions that empower any employee (no matter their technical skill level) to ask questions of their data, analyze billions of data records in seconds, and gain comprehensive, automated insights. Here are the three key features to look out for when evaluating solutions to replace your legacy BI dashboards.

Automated Insights

Every employee needs to be able to interpret and leverage data to make intelligent business decisions. High-level metrics are nice, but the real value sits in the reasons behind “why” values change and the differences between data sets. Dashboards prohibit businesses from getting these deeper insights in a timely manner, forcing organizations to try bridging insight gaps with assumptions and gut decisions. 

Instead of relying solely on intuition, enterprises should look for an AI- and ML-driven solution that can automate the process of discovering the most important findings from data without laborious manual analysis or complex feature engineering. This will allow organizations to capitalize on big opportunities, such as first-mover advantages or competitive business models. With the right solution in place, there will be more time to devote to the tasks that seem to always fall by the wayside, like marketing campaigns, customer support, or employee training. By helping get to the “what” and, more importantly, the “why” faster, your decision intelligence platform will drive better decisions and outcomes, elevating your business.

A Modern Workflow

Built to give simple, aggregated answers to user questions, BI dashboards serve their role well, but can complicate workflows when teams need dozens, if not hundreds, of answers to reach the “why” behind their data. 

Consider a sales teams that might need a dashboard to see how Q1 sales stack up against last year’s numbers and compare the performance of individual states and regions. If someone spots a problem with sales in Maryland, they’ll need new dashboards to understand why. In this case, it might involve digging into relevant factors like demographics, popular sales channels, supply chain logistics, inventory management, and more. 

However, with every new BI dashboard, answers become harder to find, as the dashboards add up in an organization’s library. Team members don’t have time to scan the hundreds of existing dashboards to drill down and find the answer they need, so they create a new dashboard, compounding the problem. 

Technology is supposed to make our lives easier, and the dashboard library is inherently old-school. Instead of this backward way of looking up information, wouldn’t it be easier to use a tool that resembles one that we all know and love: Google? Like the search engine, a modern decision intelligence platform leverages AI and natural language processing (NLP) to allow users to ask questions of their data. The platform will then understand the intent behind our questions, filter through trillions of results in milliseconds, and deliver useful answers in a way that makes sense (and can be used to inform decisions).

Seamless Data Preparation

Augmented analytics address the limitations of dashboards by automatically sourcing data from platforms, data lakes, and integrated tools to prepare it for analysis – a process that data scientists would ordinarily do by hand. A decision intelligence platform should be equipped to blend, transform, and clean data to make insights easier. 

By automatically tracking all data preparation steps in a visual data pipeline, business teams can see there is clear data lineage, reproducibility, and reusability of transformations for future projects. Data refreshes should also be able to automatically process and trigger the generation of new, timely insights. With the data preparation step handled, getting to insights that drive more valuable business decisions is that much easier. 

All individuals – whether they are advanced analysts, citizen data scientists, or business users – should be able to quickly gain data insights that will inform better, more impactful business decisions. By moving beyond the dashboard and leveraging a true decision intelligence platform that automates insights within a modern workflow and seamlessly prepares data, businesses can close the data insights gap, cut down on analysis time, and augment their teams’ capabilities to get faster, more valuable insights with ease.