Big Data Process & Structure

Operationalize Big Data

Why are process and structure important? In the near term, they represent the best way to “operationalize” big data, to put it to work to solve specific business problems and enhance operations. Longer term, they help organizations internalize analytics-led thinking and instill truly data-driven cultures.

Fundamentally, this is a matter of usage – that is, how do companies get better at using the data they have? How to make it easier for everyone – senior executives, business analysts, front-line staff – to use data to do their jobs more effectively and productively?

Structuring for Big Data Success

Data – including, but not limited to, big data – is the foundation of business decision-making. Thus, it follows that companies who are better at the various phases of the big data lifecycle are more likely to win through better decision making:

• Cost-effective means to collect, store, integrate and manage huge volumes and varieties of data
• Enablement of users to interact with, interrogate and otherwise “dive in” to data streams (ideally on a self-service basis)
• The visualization and presentation of critical trends, signals and patterns in easy-to-consume reports to support standard operational review and performance management cycles
• Real-time monitoring and signaling of market trends to enable responsiveness and enhance operational agility.

It’s also worth noting that big data process and structure are not exclusive exercises for the IT department. Business issues and opportunities – not technology capabilities or constraints – must shape the agenda and drive the discussion. The question is not “what data does IT have that can help us choose the right course of action?” Rather, it’s “what data do we need – from anywhere or anybody – that will lead us to the right decision?”

There’s no thinking about how companies – from C-level executives to business analysts – can use big data without addressing the underlying technical structure.

Solid foundations should be designed around a vision for highly integrated and analytics-enabled ecosystem. That vision must take a holistic view of the strategic imperatives around big data. It also requires a clear grasp of how big data works.

That’s true because there is no converting big data into actionable insights and – ultimately – business value without:

• The integration of repositories across the business to enable reuse and mask complexity to end-users
• Ready access to high-value and high-quality data streams and analytics tools by the right users
• Linking of data and discovery platforms to create agile, self-service capabilities
• Strong data management practices, well defined business rules and strong governance as “guardrails” for ongoing data usage.

The payoff comes in many forms. More precise insights within specific business processes, better pattern recognition capabilities across functions and business units, and greater sensitivity to market shifts. Plus, because they are flexible, well designed big data infrastructures pay off long into the future, as the big data and analytics game continues to change.

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