Data Governance as Value Driver
Executives everywhere know data is important, and is the new fuel. Some do realise that analytics could well be a revenue driver and also be the propellant behind Digital Transformation. Also, that the same executives how know data is important must also realise that data has to be clean, contextually relevant, and not stale and also have a sense of what their enterprise data is actually like. In many cases, the quality of available data causes people to massage data and create multiple versions of truth.
In our analytics programs, we advice (or help establish) a data governance framework. A good governance framework should enable these data attributes thus helping create value. In many places we see established data governance programs are ineffective either because of their complexity in operations or structure, or even lack of appropriate sponsorship.
From our experience we have found five factors which enable effective data governance to drive value.
Direct Senior Leadership Sponsorship
Successful data governance needs appreciation of its need from senior leadership, business domain leaders and also from IT. This sponsorship allows data governance, and the required structures to be set up with the appropriate governance strategy based on business needs. As a trickle down this enables the Chief Data Officer to set up her team, set up the structure, define day-to-day governance elements, processes and also quality benchmarks and measurement mechanisms.
Prioritise Data Assets and Align with Strategy
Large and distributed enterprises are likely to have number of data assets, aligning all of which synchronously is likely to be a challenge; likely causing slowness of progress and possible decoupling from business needs.
Inclusion of data assets into the Data Governance framework, then, should be prioritized by need arising out of factors ( e.g. compliance, digital transformation program, or direct needs emanating from operational areas etc.) and fitted into a road-map. This prioritization could be based on business domains, or even the criticality of the data asset itself as part of a domain. This approach enables the overall framework to be built and evolved in stages rather than all at once.
Fit for purpose Governance
Starting with minimal required governance and complexity is appropriate. The level of robustness of governance, application of benchmarks (and measurements) has to be allowed to evolve for higher levels of maturity and value. The sophistication and level of data complexity can also increase with the level of data consumption, application of technology and certainly be influenced by the size and spread of the organization.
Iterative Implementation
As mentioned above, it is appropriate for data governance to be implemented small as long as it is allowed to evolve. This evolution in terms of spread or complexity is better done iteratively. This iterative inclusion must encompass day-to-day operations, integration of governance with business needs, prioritizing specific data assets, or use cases among others. These iterations should run breadth first to ensure coverage before depth and complexity.
…and Establish a Data Culture
As people start seeing the value of data, and data quality they will start putting in that much care and start owning data and its related quality. This of course is a generic Change Management concern, to locate the supporters and evangelists but surely is the most difficult part of the overall Governance program because it deals not with the inanimate.
We have seen organizations use various forms of interventions; from CXO level folk setting examples to recognition for high quality sources or identification of right use cases, training, publication etc.
Having said all that, what is most important is to break the inertia and getting started. Many organizations continue to struggle till their Data Governance frameworks and policies stand in silos. Embedding them into business as usual ways of working is what makes the critical domino topple.
How far is your Data Analytics program evolved? Did you find it easy or difficult to set up Data Governance? We would love to hear your experiences. Do visit our web site or write to us.
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