How to implement a big data analytics program

For several years now I've wanted to do something great with the empty flower beds around our house. Starting a garden is no small task, however. What kinds of plants do I want? Will they grow here? I'm a gardening novice at best, so whose expertise should I draw on? How will I make sure the plants survive through the hot summer?

There's a lot to think about, but all of the answers point to the creation of something beautiful that will make my house a more enjoyable and ultimately more valuable place. The same can be said for implementing a big data analytics program—there's lot to think about, but ultimately your business will be better for it. Fortunately, expert advice and resources are just a page away.

Frank J. Ohlhorst's Big Data Analytics: Turning Big Data into Big Money is a new resource that can help organizations figure out how to create a successful big data analytics program. Ohlhorst stresses the importance of implementing effective project management processes when doing so, and he offers the following advice:

  • Decide what data to include and what to leave out. Not all of a company’s data sources, or all of the information within a relevant data source, will need to be analyzed. For instance, what combination of information can pinpoint key customer-retention factors? Or what data are required to uncover hidden patterns in stock market transactions?
  • Build effective business rules and then work through the complexity they create. It is essential to include business-focused data owners in the process to make sure that all of the necessary business rules are identified in advance. Once the rules are documented, technical staffers can assess how much complexity they create and the work required to turn the data inputs into relevant and valuable findings.
  • Translate business rules into relevant analytics in a collaborative fashion. After business rules are established, IT or analytics professionals need to create the analytical queries and algorithms required to generate the desired outputs. Ongoing communication and collaboration between the project team and business departments leads to a much smoother analytics development process.
  • Have a maintenance plan. Regular query maintenance and keeping on top of changes in business requirements are important, as is the ability to support iterative development processes in dynamic business environments. An analytics system will retain its value over time if it can adapt to changing requirements.
  • Keep your users in mind—all of them. Different types of people—from senior executives to operational workers, business analysts, and statisticians—will be accessing Big Data analytics applications in one way or another, and their adoption of the tools will help to ensure overall project success.

As Ohlhorst says, "There’s no one way to ensure Big Data analytics success. But following a set of frameworks and best practices, including the tips outlined here, can help organizations to keep their Big Data initiatives on track."

Learn more about how to implement a successful big data analytics program for your business with Big Data Analytics.

tags: big data, big data analytics, Frank Ohlhorst

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