Cross-Pollinating Data—the Force Behind Monetizing Insights

Cross-Pollinating Data—the Force Behind Monetizing Insights

October 05, 2018 0 Comments
Cross-Pollinating Data—the Force Behind Monetizing Insights_870x450

How can you get access to better data, and then use that to gain new insights? It's all about cross-pollination, as Abhishek Tandon explains.

Traditionally, BI has always gone forward with the “Frog in a Well” approach. IT has been so engrossed in solving internal data problems that they never took the opportunity to explore utilizing data outside of the company walls. Even business users have been inclined to view different cuts of their own internal data repositories, not realizing that the real answer may lie outside.

This approach could have been justified in the small data world, which was technically deficient to build solutions capable of allowing mashup (cross-pollination) of data. But with today’s advancements in Big Data, it is almost criminal not to integrate multiple sources of data for analysis to derive “never seen before” business insights out of it.   

As exciting as the above may sound, without relevant external data sources there will be little monetary value associated with the analysis. Imagine a scenario where companies share data with each other. A telecom provider sharing data with a credit card company to better tune fraud analytics techniques, or a retailer utilizing demographic data from a local bank to increase product sales. The combinations are endless.

Skeptics would argue that issues of privacy and conflicting interest would wreak havoc on such a model. However, the analysis does not need to happen at a customer level. The information can be shared at an aggregated level in order to determine segment patterns. These patterns when combined with internal analysis generate insights that, when monetized, could re-present millions of dollars in new revenue.   

The need of the hour is an insights platform that provides this cross-pollination of data and offers an insights layer on top of it. The platform can act as gatekeeper between the collaborating companies while the insights layer can provide business value to the enriched data. The use case opportunities are huge. The winning mantra will soon be: “you give some—you get millions.”

Are you using a mix of external and internal data in your decision making today? How have you found it helpful? Feel free to share your thoughts in the comments below. 

Abhishek Tandon

Abhishek Tandon

Abhishek is a data junkie who lives and breathes solving customer problems using analytics. He has a breadth of experience - from implementing large-scale enterprise data warehouses to helping manufacturers analyze asset behavior and predict failures. Due to his business background, he has a unique ability to understand functional requirements and translate them into technology solutions. He is part of the customer success team and leads solution engineering initiatives, traveling all over the world to explain how Progress DataRPM can help companies save millions of dollars.

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