Big Data: Three Criteria to Big Data Project Success

When determining the evaluation criteria for your Big Data project, you need to consider a range of different data inputs, their quality, and expected results.  These aspects can span a wide range of topics and different areas. Discussing your Big Data evaluation criteria with management can bring many different ideas to the table to be evaluated.  To expedite these discussions your team needs to develop a standard procedure that will illuminate the business value within each of your Big Data project ideas.

If you’re going to have your Big Data project be successful you need to have the proper procedures for evaluating each project as a strategic and tactical effort for business profitability.  The following three criteria are good places to start to develop procedures to evaluate your Big Data efforts.  

The first evaluation criteria for your Big Data project should concern its return on investment.  The business needs to understand the possible profit that can be made from each idea for a Big Data project.  By looking at the return on investment for each of your Big Data efforts, each of the efforts can be evaluated under the same business profit criteria.  As I wrote last week about Campbell’s law and developing transparent analytical formulas for your Big Data project, evaluating the return on investment using criteria that everyone understands is vital for each Big Data effort. Determining the return on investment in advance helps flesh out the ideas and builds a good understanding of your Big Data project. This can be critical for everyone from top management to the transformation process developer so the best business logic gets implemented in the project.

Next evaluate how much data each Big Data effort will need.  Today’s Big Data projects use terabytes or petabytes of data that need to be evaluated.  But in reality Big Data projects can start small with only few gigabytes of Big Data. Each specific Big Data effort needs to gain in-depth understanding of the data, detail the aspects of the processing criteria to massage the data and the overall story for taking the results and creating a profitable solution.  Your Big Data processing needs to reflect the number of positive instances required within the Big Data criteria to create an actionable item for your business situation and ultimately impact the business profit.  Does one occurrence cause action in the Big Data of business model to be profitable or are multiple data instances needed?  The number of instances which are needed for profitability will help determine how much data is really necessary for the Big Data project and help the team evaluate the business opportunity.

Finally, think both tactically and strategically for your Big Data project.  Tactically develop a procedure and conversation that evaluates the return on investment for each Big Data proposal.  Many suggested Big Data ideas and alternatives could achieve success and sustainability.  Using other criteria mentioned above, determine which Big Data idea is the best Big Data project and use its development as an ongoing blueprint strategy for subsequent Big Data efforts.  This will help your company develop repeatable processes and a strategy for developing successful business solutions for your Big Data projects.  

Develop your criteria to determine how much data you need to get the results required for a business opportunity.  Evaluate the processes that you are using so that they can be leveraged or improved for the next Big Data project.  Because the majority of projects face difficulties, you want to start building a business conversation and repeatable process that gives transparent accountability for your Big Data analytics processing within your company.


Dave Beulke is an internationally recognized DB2 consultant, DB2 trainer and education instructor.  Dave helps his clients improve their strategic direction, dramatically improve DB2 performance and reduce their CPU demand saving millions in their systems, databases and application areas within their mainframe, UNIX and Windows environments.

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