4 Ways to Shrink the Gap between Data Integration and Insight

4 Ways to Shrink the Gap between Data Integration and Insight

4 Ways to Shrink the Gap between Data Integration and Insight

According to a study conducted last year by my company, Xplenty, nearly one-third of business intelligence professionals say they spend between 50 and 90 percent of their time just cleaning raw data for analytics. As a result of valuable time and talent devoted to preparing data, businesses are often slow to unlock its insights or to act on them.

It’s also why accelerating “time-to-insight” has become today’s top data challenge. Here are four tips to shorten the time between data integration and analytics.

Before identifying and formalizing a data-collection process, you need to start with your business objectives in mind: What are the desired outcomes and goals? What problems do you want to address?  These questions guide what data to collect and the subsequent integration process. As analytics expert Ram Chandrasekar said, “Find out what the top three or four problems are and how they map to the data that is within the company, the data outside the company, and the need to integrate them.”

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As data’s value has grown clearer, the cost of data storage has been reduced, making collection easier than ever. Businesses can now collect all the data they can find. But without a hard focus and set limitations, more data does not enable greater insight. Instead, it only slows value extraction.

With a finite scope and a tight focus on specific goals and problems, you can begin to design your collection and integration process. Opening the floodgates only adds time to an already onerous data-analysis process. 

Select an Integration Platform that is Flexible, Elastic, and Scalable

An estimated 2.5 quintillion bytes of data are created on a daily basis worldwide. The volume and velocity of data are overwhelming. Given the always-shifting priorities and demands of big data, the possibilities (and potential for dead-end insights) are limitless.

When it comes to data, you are limited only by what you don’t collect (and, sometimes, the decision about what not to collect is more important than what you do choose to collect).

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How to Collect Big Data


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