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Do You Need a Business Intelligence Dashboard?

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The advent of artificial intelligence and machine learning has advanced the capabilities of contemporary business intelligence (BI) tools considerably. With that said, the insights returned are inly as useful as the user’s ability to interpret them. 

 

Dashboards have made this easier to accomplish by making it easy to analyze and share the results. So, in that regard, the answer to the central quest ion here — do you need a business intelligence dashboard — is yes, with a caveat.

 

Read on.

 

What is a BI Dashboard?

This tool puts the results of even the most in-depth analytics in the hands of those who need the information most. Moreover, the dashboard does so in a manner that makes it easy to both understand and apply to the situation at hand. Embeddable dashboard tools enable automated data visualization, which can deliver key takeaways in a way they can be readily digested. 

 

Even better, this makes it easy for anyone on your team, who is authorized to do so, to make requests, access the results and use them to effect decision making. Moreover, flexibility is a core aspect of the best business intelligence dashboards, as they can be tailored specifically to the individual needs of a multitude of end users.

 

The Caveat 

Sudheesh Nair, CEO of ThoughtSpot, one of the industry’s leading data analytics firms, says the dashboard has an inherent limitation users need to keep in mind. The issue drawing his concern is predicated upon the way data has traditionally been used to enable companies to serve customers in an informed manner. 

 

To understand his perspective, one must look back at 2020 and all of the disruptions imposed by the COVID-19 pandemic. Businesses were suddenly forced to cope with conditions they’d never seen before, let alone anticipated. Nair says relying upon historical aggregated data going forward, given the past year’s disruptions have rendered it less relevant, is problematic. Therefore, business intelligence dashboard relying upon the traditional analytics model is likely to be inaccurate. 

 

The Solution

Meanwhile, ThoughtSpot has positioned itself as something of a “Google” for data. Its platform works in a similar fashion to that of the venerable search engine, in that users can ask specific questions based upon the data provided and get answers to the question they asked. However, with ThoughtSpot, they also get alternative questions and answers that have some bearing on the query. 

 

As an example, Nair cites a scenario in which a financial institution is looking to get existing customers to use more of its products. In this case, the lender is trying to get holders of its car loans to take advantage of its mortgages tooSo it runs a query to see which car loan customers might be considering buying a home. 

 

The results come back and customers identified by the algorithm get a mailer touting the benefits of the home loan. However, there is a group of car loan customers out there holding a bit of ill will against the lender for late charges imposed on payments that arrived one day after the due date. Irritated with the creditor, these borrowers ignore the offer altogether. 

 

Had the company used a platform that leveraged AI the way ThoughtSpot does, those customers would have been flagged, giving the lender the option of not sending them the offer, or better still — sending the offer with an apology and a refund of the late fee. 

 

Looking Forward as Well as Backward

Having this capability gives ThoughtSpot the ability to take a use case driven approach. This is going to be key in coming years because the COVID-19 disruption is going to skew results based upon the traditional aggregation modelsWhile companies will still need the ability to ask “what”, they also need to be able to ask “what if” and “what’s next”. 

 

So, do you need a business intelligence dashboard? It is still useful, however you really need to rethink the way it’s informed if you want the best actionable results going forward. 

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