How to Succeed in Big Data Without Really Trying: 4 Steps From Booz Allen

In a recent Health Affairs blog post, two principals at Booz Allen with experience working on large-scale government and healthcare data analytics projects share four best practices for big data success.

1. Get the right data. The data that is readily available may not be the best data for a given analytics project, according to the post. The most successful big data projects the analysts have seen involved first determining what data sets would be the most useful and then allocating the necessary resources to acquire that data. The upfront effort will pay off in better, more usable results.

2. Know the data. To get the best results, it's important to know where data sets originated and any manipulations or changes they have undergone in the past. The authors give the example of a federal health agency's big data project that initially found a significant increase in the number of less-experienced clinical investigators in a set of therapeutic areas, only to later discover the introduction of a new address field in the data set duplicated many of the entries, skewing the results.

3. Allow exploration. Because modern big data platforms can offer subject-matter experts the ability to directly query and analyze the data, successful projects allow these experts to explore the data, looking for new patterns or previously unthought-of connections. This approach will generate fresher insights then following conventional analysis processes, according to the post.

4. Ensure initial pilots have wide applicability and resonance. To get the entire organization on board with a big data project, the first pilots need to show not only that data analytics work but that the process can be applied to other departments and other challenges. Leaders especially need to be able to see big data's potential to help with the transformational change needed to get an organization to fully embrace data analytics.


More Articles on Big Data:

Survey: In Analytics, Data Variety More Challenging Than Volume
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