5 Big Data Analytics Success Factors

Everyone is talking about implementing analytics within their company or department and using the power of big data to gain a competitive advantage.  Previously, I talked about how to get your analytics efforts going in this blog post (http://davebeulke.com/big-data-five-ways-to-get-analytics-started/).  The truth is getting an analytics reporting process into everyday business activities is a difficult task.  Below are five analytic success factors to help you continue to build on your analytic practices within your company.

  1. Get support from the corporate culture.  The corporate culture matters more than anything because it either embraces or kills any big data analytics initiative.  Most departments that are not used to using analytic results to drive their business decisions are against anything that has to do with change, let alone any initiative using big data analytics.

    To modify the corporate culture big data analytics efforts need support. The best way is to get it is from the top of the corporate structure.  Negotiate with high level sponsors to understand their needs.  Work to embrace the existing reports first to become informed on existing analytics, and then extend those with big data analytical reports through more formulas, data checkpoints and more data.  This helps everyone understand the starting context of the data available, the people supporting the infrastructure, and then more data points from the big data analytics.

  2. Start at the top, work the bottom, and meet in the middle.  Most departments that are not used to using big data analytics to drive their business decisions are sometimes against anything that has to do with analytics initiatives.  As I said in the previous point, getting high level sponsorship is vital.  The key to understanding the bottom is analyzing the existing reports being used. 

    By understanding all the generated reports and then analyzing which ones are actually being used, you will understand which business areas need to be expanded with more data and more analytical points of comparison.  Use this report analysis to extend your big data analytics efforts to the middle of business. Report on areas that the top management and the rank and file workers can both use so they can meet in the middle of the company issues and find the analytics immediately beneficial.

  3. Confirm and handle the truth.  Big data analytical reports are not always pretty in the sense that they sometimes expose company issues, bad processes, or bad departments.  By looking at the existing reports and then extending them, your new analytics can confirm existing situations. Then the reports can be expanded for everyone who wants more analytical information on additional areas. 

    One of the biggest challenges is when the new big data analytical reports points out bad news.  Handling the truth about a bad situation is never easy. Analytics reports that point this out will definitely bring an assault on the integrity, processes and big data used to develop the reporting.  Be ready, because sometimes even if your analytics are flawless and even though the message recipients shouldn’t, they do kill the messenger.
  4. Start by thinking of the answer or outcome.  Whether your analytics practices are just starting out or are mature, it is always best to think of the answers or outcomes first.  By thinking of the big data analytics output first (the amount of data, its type, comparison points, analytic formulas, etc.), assumptions and benefits can be discussed before the analytics begin.  Sometimes completing an analytical report or answer takes many intermediate steps, involves many data sources, and many important detailed integrity checks.  Discussions of the overall process, its context, the data cleansing, different data sources, and final output formats are best refined through discussions with subject matter experts and business area experts that know the existing business processes.  Discussing the big data analytics processing and getting these subject matter experts and business experts involved is critical for the acceptance of the big data analytics results.

  5. Build an analytics narrative.  Building a narrative or story around big data analytics is always the best way to describe the findings and provide a deeper understanding of the results.  The story behind the data can reflect the different aspects of how the results were determined such as the intense data cleansing, the acquisition of the diverse data sources, their relationships,  their value, and importance to the analytics.

    This big data analytics narrative can be dramatically enhanced through good graphic displays of the results.  Through line, pie, bar graphs, location maps, and other graphic depictions of the analytic results, management and users of the data can quickly understand analytical processes, the analytics value, the data differential, and the deeper meaning of the analysis.

Concentrating on these areas will help you build support and continually build more supporters for your big data analytics efforts and reports.

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