Big Data: Best Practice & How to Avoid Pitfalls

The big data premise is simple and logical – in an age where every pixel can be tracked and measured, the challenge isn’t having the data or accessing it, but making sense of it all. And for companies interested in learning what’s working and what’s not, sorting through mountains of data in order to see those insights is a promise that’s hard to ignore.

But before you get into it here are five best practices that you should be aware of to avoid major pitfalls.

Right sizing the scope

Defining multiple clusters of inter-related functions will allow for a more carefully thought out solution to each problem. More often than not these will converge or intersect to a greater or smaller extent.

A Clear Problem Statement

Defining the problem that needs a Big data solution is the first step to a successful outcome. Take the time to identify, understand and build consensus. There will often be more than one problem(s). Repeat step 1 for each.

Commit to Delivering a Solution: Build a Resource Strategy

Lack of designated resources with the right skill set can be the single most important cause of time and cost overhead. Build a big data group separate from preexisting BI, data warehousing, and data management teams. Start small – a combination of business and IT users and leaders to shape projects would be the right place to begin.

Avoid Overinvesting – Identify a Non-starter & Course Correct

Take your Big Data Solution model for a test drive. Apply a set of test data to it to see how well the rules perform. This will help you identify the norm as well as the outliers.

Learn as you Go – Adapt as you Learn

A viable Big Data solution can be called so if it stands the test of time including market fluctuations and ever changing customer needs. Therefore it is imperative that the solutions be looked upon as living entities that need to grow and evolve to stay relevant and produce the necessary results in business.

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