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Commentary

4 Tips for Defining Your Approach to Big Data

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If you’re like me, you’re noticing that everywhere you turn someone is writing about big data. Initially, it was the technology press. Then, business journals and newspapers got into the act. At companies everywhere, senior business leaders and even boards of directors are determining the best ways for their organizations to deal with big data. I like to joke that we don’t need to add the word “big” in front of every utterance of data, but for those who are new to big data, here are four factors to consider:

  1. Strategy. Have an outline of how your company intends to leverage data. Most any technology organization can download an open source copy of Hadoop, install it on some commodity boxes and declare itself on the big data bandwagon. Unfortunately, this misses the point. It is critical to understand what roles data and analytics play in your company and how you want to leverage that in the future. So a data strategy needs to be informed by the answers to these questions: Does the data strategy support your corporate and IT strategy? Will it be a driver for improving results? Will it help your company make better business decisions?
  2. Change. Just as the real world is not static, know that data is ever-changing. Big data technologies allow you to work with much larger datasets and at far-faster processing speeds. Technology innovations such as in-memory processing break through old speed barriers. Real-time processing technology innovations change the paradigm from a store and process data mindset to one of processing data in real-time. Even as storage costs go down, it will be more effective to process data in real-time to get to the true signal than to spend money trying to store the complete data set.
  3. There are four Vs. When defining big data, there are four Vs, not just the three the industry talks about: velocity, variety and volume. The fourth is veracity. In other words, it’s important that data is accurate and reliable, and that you understand what uses it is fit for. Without veracity you can very quickly end up with broken processes or erroneous insights.
  4. Good governance. Enterprises investing in big data require more emphasis on data governance than ever before. Not doing so is a recipe for a data quality disaster and it could lead to very poor management decisions or ineffective real-time analytics. Careful management of your data is an absolute requirement to get the most out of your big data efforts. 

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Comments (2)
For insurers, big data is here to stay. Across the industry, there is a race to gather, manage, and leverage this data. Customers will enjoy a higher level of customer service and potentially significant reductions in paid premiums as a result of accurate data that reflects their positive habits.

Linda Boudreau Data Ladder

Posted by Linda B | Friday, May 16 2014 at 1:53PM ET
One more thought: along with big data comes big data linkage problems. Combining these various sources of data can be a difficult but important first step in improving data accuracy.

We talked about this on our latest blog post: http://www.dataladder.com/blog/2014/05/19/insurance-data-quality-must-improve/

Linda Boudreau http://DataLadder.com

Posted by Linda B | Tuesday, May 20 2014 at 2:11PM ET
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