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Big data analytical capabilities will continue to grow and evolve to influence businesses across all industries. To ensure maximum value, a holistic view of how these analytical efforts affect an entire organization need to be considered. The following considerations should be made to ensure strategic fundamentals, organizational needs, and future needs.

Build the right metrics to answer the right questions

Having a lot data isn’t nearly as valuable or important as having the right data. Emphasize knowing the questions you want/need to answer and the data sources needed (and available). Use a firm-wide strategy to build KPIs and components of KPIs that will help drive business decisions now and have the flexibility to grow over time.

Create a value ecosystem

Emphasize that the “customer” of big data analytics is both internal and external. Provide use cases and demonstrate value achieve buy-in of stakeholders. Analytical needs might have originated in one major business function (Sales, Marketing), but it’s value to the entire organization needs to be displayed.

Identify a Champion

Ensure someone is driving and owns big data analytical efforts; this could be a Chief Data Officer, Chief Analytics Officer, Enterprise PMO, etc. Higher visibility should drive higher levels of cooperation and adoption. Creation and distribution of dashboards and reports should be available to all levels of employees to ensure relevant analytics and accomplishments can be explained and celebrated.

Change with a purpose and train to make sure the change sticks

Change is always difficult but moving from one technology to another might create as many (if not more) organizational process issues than technical implementation issues. Plan a number of alignment and training sessions to ensure success.

Protect the Customer and the Company

Encourage open communication about what data is important horizontally (across functions) and vertically across the organization. Don’t forget to communicate with third party vendors and identify any restrictions/difficulties they might have with data modifications/flexibility. Ask questions about key assumptions, data sets, and how things can change.

Build base knowledge

Avoid dependency on reporting without knowing the underlying reasoning and assumptions behind big data analytics. Ensure that basic statistics and limitations of datasets are understood and documented to avoid faulty analysis. Spending time understanding the basis of your analysis can pay figurative and literal dividends.

Continuously challenge and innovate your capabilities.

Big data analytics is all about having fluid and dynamic data available to help solve business problems. But the problems today likely won’t be the problems of tomorrow. A clear technology roadmap and improvement plan need to be continually developed and updated to ensure long lasting success and value.