Over the course of the last year or so, the buzz-phrase “dark data” has entered the common lexicon of data management, information management, technology and business analytics circles. When I first heard the term my mind conjured up the image of the bat-cave wired with the technical capabilities to track Gotham City’s super villains. Armed with my rapacious curiosity I set out on a deliberate quest to study the shadowed periphery of the information landscape.
This four part blog series shall examine the emergent phenomenon known as “dark data” with the objective of evaluating and contextualizing the trend’s influence on information management practice discipline.
PART 3: DARK GOVERNANCE RISES – MATURING BEYOND AN OPERATIONAL DATA GOVERNANCE MODEL
The reality of big data – is that prioritization is key since the data landscape is growing at an exponential rate. We can’t boil the ocean as much as many would like to. As such, enterprise governance and risk management strategies are not only valuable in standardizing operational data management practices and monitor for risk, benefits realization, performance and compliance… but also can assist in rationalizing existing data management processes prioritized within larger enterprise data, business, compliance and technology views. Interestingly, a recent article published in CIO magazine echoes these sentiments while providing an overview of enterprise risk considerations as it relates to the prevalence of unstructured dark data.
Unfortunately, the most common data governance framework that is adopted by organizations is often reactive and/or point-solution-based reinforcing a lack of data management practice maturity. The results of this piecemeal and operationally-driven approach for data governance may include:
(i) operational data management service delivery issues prioritized in a vacuum;
(ii) the emergence of a fractured data ownership (and decision-making) model;
(iii) inconsistent procedural practices across data stores;
(iv) lack of enforceability or alignment with broader organizational data management policies or procedures;
(v) siloed and/or fractured data management decision-making that may fail to consider downstream/upstream process impacts;
(vi) a lack of oversight into broader legal, compliance or governance risks impacting data management practices;
(vii) minimal detective/predictive controls implemented to support proactive governance capabilities;
(viii) increased emphasis on governing performance and process as opposed to risk; and
(ix) a lack of measurable and demonstrated value of the governance function.
In a nutshell, a data management program that seeks to implement governance only at the operational data management layer will undoubtedly find difficulty in unlocking the value and/or manage the risk of dark data in a consistent fashion unless it matures to include an enterprise governance function.
Stay Tuned for Part 4 of our Series…
The Bane of Dark Data’s Existence – the Governance of Supplier Management Risk.