Big Data Analytics Success Depends on These 3 Ingredients

Big Data Analytics Success Depends on These 3 Ingredients

Big Data Analytics Success Depends on These 3 Ingredients
The explosion of activity in the big data analytics sector is both undeniable and understandable. 

Done well, big data can help companies realize valuable data and business insights that translate into business value.

The Royal Bank of Scotland, for example, has used big data analytics to underpin a strategy it calls “personology.” This strategy is about delivering a more personable and personalized customer experience, using data to better understand, anticipate and service customer needs and queries. This data-driven approach towards user experience has boosted customer loyalty and helped to improve activity and visibility across loans and insurance.

However, simply investing money in technology is not enough to deliver big data success. Many big data projects have failed and usually for the same few reasons.

In their rush to jump on the big data bandwagon, many companies ignore the three key pillars that support successful big data analytics in business:

For many organizations, ensuring these pillars are not overlooked requires a clear methodology and technical approach. A Design Thinking methodology, leveraging open source and techniques like AI and automation can enable organizations to mine a range of big data assets in order to identify and leverage the valuable, actionable data within both structured and unstructured data sets.

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At its core, big data requires at least three things to work: gathering disparate sources, storing and gaining insights from the data. The problems of gathering and storing data has largely been solved (though both still present challenges). 

Extracting useful signals from the noise in almost real-time, however, remains difficult.

Finding useful information from data stores is difficult for a lot of reasons, but the biggest problem is more fundamental and more common problem than most organizations realize: companies have not scoped out at the start what they intend to do with the data they are gathering.

A Design Thinking methodology can help in this regard.

Before starting on a big data journey we need to know the minimum viable problem that needs to be addressed.

For this, Design Thinking provides a critical method to getting to the root of a known problem, identifying an as yet unrecognized problem, or both. It does this while staying as close to business reality as possible. Essentially, it ensures that businesses focus on the right problems while developing viable solutions.

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Discovering and defining problems is the most important part of any big data journey. The reason is simple: Design Thinking helps to focus businesses on addressing customer pain points by looking for issues that aren’t always obvious. Once the pain points that need addressing are identified, a company can define what data it needs to gather. By focusing data on a particular issue, we can discover new actionable insights that we can execute on.

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