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Why Your BI Dashboard is Causing an Insights Gap—and How to Fix It

Founded In 1951, LEO was the first computer to analyze business data and marked the inception of data analytics and business intelligence—a strategy that involves the tools, technology and processes required to make correlations between data points and identify trends. It lays the foundation for businesses to start asking the right questions that can lead to valuable insights.  

Since then, the role of data analytics and BI has evolved drastically, with most organizations today relying on self-service analytics and dashboards to deliver insights to the masses of business users and analysts. The dashboard’s ability to visualize data for users has democratized data and generated digestible insights for everyone.   

While BI dashboards have democratized data analytics, in recent years, they’ve become outdated for modern workflows and unable to keep up with the scale and complexity of today’s data demand. As a result, an insights gap has emerged between the data experts that can analyze big data and business users and analysts seeking insights to make intelligence-driven decisions.  

For example, a regional sales team requires a dashboard to view fourth-quarter sales compared to last year’s figures. However, if they find a problem in a specific state, they will need a new dashboard to understand what caused the discrepancy. In this case, it may involve mining relevant factors like demographics and buyer behaviors. With every additional variable, a new dashboard is added to the library and answers become more difficult to find. It falls onto the analyst to interact with several dashboards to identify relevant insights, while simultaneously running manual analysis on these insights.  

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Another problem many companies face is turning data analytics into insights within a timeframe that makes them valuable. While it is realistic to identify trends and correlations across different variables in a smaller dataset, this process grows exponentially more difficult when finding insights across hundreds of variables and diverse datasets. And by the time opportunities are identified, it could be too late, which often means loss of revenue and customers.  

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Because dashboards lack intelligent reasoning, users are forced to analyze results manually, which opens the door to human error and time loss. This is an antiquated approach to search for information. For analysts and business users, there are better ways to gain practical insights without relying heavily on data scientists – look at the way consumers interact with their smartphones.   

Technology can be a heavy burden on the user, but through innovations like natural language processing (NLP), AI can not only understand intention but also sift through trillions of results and deliver useful information in a way that makes sense to the user.   

Ultimately, it is vital that organizations invest in tools that can augment BI and analytics to help drive more intelligent insights. Technologies like NLP can augment your traditional analytics and answer difficult questions like “how” and “why” to close the insights gap.  

Coupled with innovations in ML and AI, these tools can deliver insights in real-time, rather than spending hours or days after starting a query… When businesses can leverage augmented analytics and make them as accessible as speaking to a smartphone, they can evolve beyond dashboards and promote creative decision-making across the entire organization.   

Today, organizations are generating such a large amount of data they’re unable to maximize its value. Traditional BI tools provide the lowest common denominator to those seeking insights. Legacy BI tools are inefficient and cause organizations to spend time and resources on data science expertise that could be best spent proactively solving other problems.   

BI Dashboards alone are not capable of arming analysts and users with the necessary insights to identify new opportunities. If you’re struggling to get the best from your data, consider modern solutions that can augment BI with AI and ML to deliver greater insights.  

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