Does Big Data need to have Big Governance

 'Big' seems to be almost entirely irrelevant. It is more about the governance agility.

Big Data has big potential; it may also cause big hassles. How does what you are doing in Big Data and Analytics change the focus of Data Governance? Does Big Data need to have Big Governance?

The biggest challenge is in dealing with the relatively undisciplined 'culture' of 'big data'; it is exacerbated by the data management solutions being those applicable to unstructured, multiple, remotely controlled data sets, rather than the more straightforward approaches relevant to structured, locally controlled data sets. Big data is now getting all businesses to think of how to apply it in different areas, the process governing, rather than the data itself. With Big Data,  it becomes easier to fall into the traps laid by poor governance, especially when examining streaming data. Big Data not only requires strong governance but it is also a source of identifying new ways to govern data. The demands of analytics put a premium on the accuracy of the data as well as its comparability when combining data from across industry verticals.

Data governance evolution: So perhaps what might be discomforting is when these old issues are raised, particularly by data professionals, as though they were specific to 'big data', or at least avoiding clarification in that regard. Perhaps the hype of 'big data' is being used as a kind of signal to move typical data management issues that would otherwise have been largely ignored into the lime light. If so such a strategy will become counter -productive as 'big data' heads into the trough of disillusionment. The good news is we all agree on the importance of data governance, as well as what is necessary for the process. Where the argument is about what are factors at play in the "big data" world that will force businesses to extend the techniques they are using beyond those they are currently using. Data governance is perhaps at the inflection point of evolution. 

Governance agility: The value placed on the quality of structured vs. unstructured data, with the relative pressure for data management / governance is being naturally focused more on so called structured data. Although managing data derived from unstructured sources might be technically more challenging than managing structured data, perhaps this is offset by such derived data seeing less critical use. It is also about the probabilistic relationship between data and information (the distance between these as it were) driving decisions or action. The relationship between 'data' and its associated 'information' content, or at least the value of that content, is more direct (closer perhaps) for typical, structured transactional data than it is for decision support data. Decision support information is often more probabilistic than transaction information. For structured data that distance is typically smaller than for unstructured data. The smaller the distance, the more likely it directs action, so it is more 'critical' in that sense. Whether this 'distance' between data and information makes any difference to data management, or governance, Data management is largely about closing the distance (increasing 'quality' for the purpose of decisions). 'Big' seems to be almost entirely irrelevant. It is more about the governance agility.

Big Data spurs the creative thinking of the next level of data governance, it is not necessarily big, but it has to be agile to adapt to the 4 “V”s characteristics of Big Data; and it has to be effective enough to steer and navigate the thorny analytics journey all the forward-looking organizations are heading  toward.