Global organizations are investing in technologies capable of storing and manipulating data in previously unimagined ways. In certain cases, businesses also re-platform their current IT environments on the basis of such new systems. Such big data systems brought measurable results: higher sales and lower costs. And there are far from assured positive outcomes. All digital systems must be controlled to really get value from one’s data.
In the hearts of many data professionals the word data governance strikes fear. Since it is often vaguely defined and misunderstood, many simply turn to a technology-only approach to address their needs for governance. The uncertainty that comes with a lot of big data systems makes this technology-based solution especially appealing although it is well recognized that technology alone will never suffice. It is even less understood is that technology itself needs to be revisited in today’s data governance optimization.
Defining the governance of data
Perhaps it would be important to consider what data governance isn’t until we determine what data governance is.
Data management is not a legacy of data, stewardship or master data management. Each of these words is frequently heard in conjunction with data governance in big data. In reality, these activities are components of data governance programs of certain organizations. They are essential components but nevertheless they are merely components.
Data governance in big data at its core is about the systematic management of critical data in the organization and thereby ensuring meaning is extracted from it. Although maturity levels can differ by organization, data governance is generally achieved through a mix of people and processes, with the technology used to simplify and automate process aspects.
How different is data governance in the Big Data Age?
The big data three Versus are:
Volume: The amount of data found in big data systems will reach into and beyond the petabytes.
Variety: Data is no longer in simple relational format; it can be structured, semi-structured, or even unstructured; data repositories cover directories, NoSQL tables, and streams.
Velocity: Information must be quickly consumed from devices across the globe like IoT sources. You need to evaluate the data in real-time.
It can be difficult to rule certain processes. Organizations are typically required to bind separate clusters together, each with its own business purpose or store and process specific types of data such as folders, tables, or streams. However, if the stitching itself is handled carefully, holes are easily revealed, as it can be incredibly error-prone to reliably protected data sets across several repositories.
Robust governance systems will still be rooted in people and processes, but technology is crucial for the right choice and use. Big data ‘s specific collection of problems makes the assertion more true now than ever. Technology can be used to simplify governance aspects (such as security) and close the gaps that would otherwise cause key practices (such as data lineage) problems.
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