In today’s fast-paced, highly competitive and extremely volatile market place, data governance is one of the key topics of contemplation in large organisations.
One of major contributor for effective monitoring of market trends, to gain competitive edge, innovate and achieve revenue growth, is reliable business analytics. Quality analytics are only possible if the underlying data is complete, accurate and of the best achievable quality.
The term data governance implies an effective and efficient management of data that flows through an enterprise. The key factors that contribute to effective management of data are consistency, quality, completeness (re-)usability, security, availability, and data categorisation. A data governance organisation, defines data policies, processes and standards and monitors them through support of appropriate technology.
Data governance is not specific to IT function. Everyone is involved and affected: from the data originators to data consumers, from the operational staff to C-Suite executives, and from internal to external stakeholders. A typically effective data governance organisation involves resources from across multiple business functions and lines of business (or business units).
To be a lucrative data governance enterprise, data governance will be viewed as a multidisciplinary endeavour, with a clear framework. Implementing a successful data governance programme that operates on three key principles.
The first key principle is accountability. If there is no clear ownership of data, the data objectives will become unattainable and adrift from the business objectives. As mentioned earlier, data governance is typically viewed as an IT initiative; consequently the business doesn’t feel the need to support or pitch into the data related efforts. The accountability must come from the business and IT function will be leveraged as a support structure. The business must define the objectives of data and align them to the enterprise-wide objectives.
To successfully achieve the data objectives, a multilayered data governance organisation is defined. Data council or steering committee sits on the top that consensually decide the strategic direction for data, (or use of data) define the policies, processes and standards. The following layer identifies the data owners, the folks responsible for their respective data domains and make day-to-day data related decisions. Finally the data stewards, who work cross-functionally, ensure compliance to policies, processes and standards. This typical governance organisational structure may vary from enterprise to enterprise, however the principle of accountability must be religiously observed.
The second principle is establishing necessary policies, processes and standards. The policies, processes and standards pertaining to data must be implemented to the entire enterprise, rather than lines of businesses or business functions in silos. Policies, processes and standards that are observed and monitored in business silos will lack data completeness, quality and accuracy of data; and once again, the data objectives, and consequently the business objectives, will be negatively impacted.
Finally, leverage technology to ensure consistency, quality, completeness (re-)usability, security, availability, and categorisation of data. Technology must be used as means to accomplish data objectives and not to direct the data objectives. Technology enables a data governance enterprise to effectively manage data in three ways. It implements data models to retain complete, well-categorised and high-quality of data. Secondly, technology ensures that the data is secure and compliant with the industry and government regulations. Thirdly, the data is appropriately utilised and made readily available across all lines of businesses, business functions and external stakeholders by leveraging data management, business intelligence and reporting tools and applications.
A formalised data governance programme provides intangible benefits and potential cost savings through unification of existing data related initiatives, addresses data gaps and provides synergies. Additionally, data governance establishes a cross-functional model with clear ownership and boundaries that promotes an environment for long term collaboration and coordination among business functions, lines of business and technology.
The data aware enterprises where data related initiatives are already underway, in silos and/or owned by technology, it is time to identify synergies that will lead into a data governance programme and plan the handoff of data governance from IT to business.
The author is a business and technology consultant at Deloitte.