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Big Data Unleashed Part 3: Five Considerations For Your Information Management Agenda With Big Data

In Part 2 of the “Big Data Unleashed” series, I discussed how business experimentation and curiosity are at the heart of business enthusiasm for Big Data.  Today, I will discuss how organizations are responding to this Big Data excitement by accelerating the quest for becoming data-centric ─ revisiting and updating their information management roadmaps.

According to its Technology Vision 2011, Accenture declared, “The age of viewing everything through an application lens is coming to an end. Coming next: a world in which the quantity, processing speeds, and distribution of data compel IT leaders to see the world through a data lens.” Indeed, data is taking its center stage and organizations are now recasting the IT project evaluations with a more data centric, platform-point of view to tackle Big Data. It is an opportune time for information management professionals to approach those “years-in-the-making” topics with fresh perspectives including the following five considerations:

1. Place focus on data itself, less on assumptions required by traditional data sampling

What is different about managing information today is how organizations are now storing more data longer than before, given that they know that they can improve business results with data, especially when you can combine large-scale transaction data with interaction data, especially social interaction data. Just several years ago, organizations invested heavily in the resources for developing assumptions and rules to “connect the dots” for better decision support or operations. Given the rapid maturing of Hadoop and other inexpensive computing resources, “no data left behind” type of approaches, the ability to manage and process every single piece of data, is within the realm of possibilities. Organizations can focus on data itself and what it can mean, curtailing guesswork and trial-and-error methods.

2. Data skills reimagined  ─ advancing to interpretation, analysis and projections

With a platform approach to data, menial tasks of hunting, gathering and fixing data can be drastically reduced. Many of the back-and-forth between IT and the business and between various groups with IT, can be substantially reduced. This means that job descriptions for analytical tasks can evolve to put more emphasis on interpretations across domains, deeper analytics and projections including scenario analyses. Business and data analysts are becoming savvier than ever before about data in-context while IT development and architect teams can spend more time working on value-added tasks than having to spend a significant portion of their effort on “keeping the lights on” issues. The impact of these will vary from organization to organization but it is crucial to consider the implications for your organizational designs and hiring strategies.

3. Distributed, fast decision-making is here to stay

Time to complete actions or transactions will be commanding even higher premiums in the coming years with Big Data. Business executives are also accepting that pushing out decisions at points of impact are not only necessary but the key success factors to agility. Transparency and accountability become even more crucial in such a distributed environment.  Successful organizations must have a consistent, comprehensive view into staff decisions, interactions with their peers, managers, customers and partners, and other activities across and beyond the enterprise. Integrating data to ensure effective communications, collaboration and unification including social media and response platform should be also part of the considerations. Distributed decision making and operational support also underscore the need for data virtualization and master data management as we drive businesses from a logical standpoint and yet still have to rely on data independent of location or format.

4. Solving the battle – process versus data

For years, the battles of data and process have not been settled. Today’s enterprise data integration platform makes the decoupling of data and process much simpler.  You can represent and manipulate data without being tied to physical data because you can express them as logical data objects.  Data entities and abstraction layers can be treated separately from physical interfaces. Beyond the technology, organizations face the hurdles for resolving the battles of process versus data on skill set, knowledge and ownerships of business process and work flow tools versus traditional data-centric tools.  Organizations also must keep pace with rapid changes in business processes and business rules.

5. From application-centric to data-centric

At the end of the day, applications come and go, but data is forever.  You may migrate or retire applications, but you would not be completely retiring “data” either out of compliance, analysis or other operational purposes.  Renewed focus on seeing the world from a data-centric point of view is a needed and high-impact way of looking at the challenges which we always wanted to turn into opportunities. Now with context, relationships, insights and influences that can be derived from Big Data, we are in an ideal position to reshape our information management agenda and reframe our conversations about IT investments by taking a data centric view.

This focus on Big Data demands that Business and IT come prepared to the table together and collaborate. The good news is that this is only the beginning of the “Big Data” journey and you will have an entire career ahead to make a big mark!

Up next in Part 4 of the Big Data Unleashed series, I will touch upon how re-thinking of mobile strategies with Big Data is empowering organizations to take a more methodical, measurable approach and thus drive faster results with unique characteristics of mobility including locational intelligence, context-and time-sensitivity and personal interactions.

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