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Strategies to create a big data management plan: 2015 roadmap

Shahida Sweeney | Feb. 23, 2015
Success lies in starting small with your big data projects.

In the race to embrace big data, companies benefit from a clearly-defined action plan. Here is a step-by-step guide to managing your big data strategy. This roadmap clarifies the concepts and terminologies and ensures your project team is well-armed to walk the talk.

Step 1: Sidestep the jargon

Behind the jargon, concepts around big data are still evolving. Start by clarifying the distinctions between big data and conventional data management. Explain the concepts to key stakeholders and refine the more manageable components.

The danger lies in concepts being lost in translation. Traditional data is clean, with the gaps filled and outliers removed. Hypothesis can be tested together with supporting evidence. This evidence or data is collected or stored in the more traditional enterprise data warehouses.

Big data is messier and comprises structured, semi-structured or unstructured content. This comes from many different sources including mobile devices, internet traffic, streaming, machine-to-machine communication, sensors, or GPS tracking systems.

In this dynamic and unpredictable space, today's big data may become tomorrow's old data. Nothing stays constant around the ticker-tape of human communication or interaction.

As a traveler on the big data journey, start small. Leave the science to the big data scientists, a niche breed perhaps best left to smashing atoms at the Large Hadron Collider. Inside the less exciting trenches, ask yourself: what is your big data strategy?

Does this strategy adapt to your lines-of-business, service delivery and operational needs? Which technologies, standards and practices complement what you want?

Step 2: Avoid more of the same

The danger lies in rebadging your information management plan as a big data strategy. To profitably analyse, share and leverage the more unstructured information, firstly clarify your high-value data sets.

These datasets are open, readily-available and can be freely used, re-used or re-distributed by anyone. Beyond the semantics, assess how the analysis of big data allocates services as and where needed, clarifies policy or improves business processes and governance.

Avoid jumping in to interrogate your big data. A plethora of commercially-available analytics' tools do this for you. Rather, this journey is about exploration, detours, more fluid relationships and adapting to a shifting landscape.

Step 3: Take a look back

If you step "back to the future" take a closer look at your available information resources. Who owns which piece of the puzzle? Strategic planning, a somewhat over-used term, comes into play. This strategy takes a closer look at available data sources, potential of this data, the costs and barriers to access.

This strategy straddles scientific, economic or social research. At an operational level, analytics is useful for customer or client segmentation, market research, managing campaigns or tracking domestic or global economic trends. Fraud detection or managing risk offers untapped potential.

 

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