Today’s digital business environment depends heavily on data to guide decisions and determine business direction. Organizations have begun to implement data governance, particularly in response to regulatory requirements. But successful businesses recognize the need for adaptive data governance to power more efficient decision making while promoting data value.
Adaptive Data Governance Defined
Traditional data governance approaches take a defensive posture, focusing on minimizing risk and achieving regulatory compliance. While important considerations, this reactive approach typically involves rigid controls dictated by centralized compliance officers. It can tend to introduce obstacles rather than promote growth.
Adaptive data governance, on the other hand, encourages flexibility as a key characteristic. Under this model, organizations employ multiple data governance strategies to address changing business situations. Proactive strategies include compliance but reach beyond constraints to promote business value.
Limitations of Traditional Approach
Because the traditional approach to data governance emphasizes regulatory compliance, it downplays the importance of supporting innovation and agile decision-making. A one-size-fits-all approach means that organizations cannot react quickly to changes such as a market disruption or a business merger.
Traditional data governance views data as a risk first and an asset second. Consequently, businesses using a traditional model often fail to fully understand and clarify decision rights. That is, they may not clearly understand what business decisions need to be made, who owns those decisions and what data access they need to make them.
Multiple Governance Strategies Available
Adaptive data governance may involve using multiple data governance strategies in concert to address various needs in digital business. This allows the organization to adapt quickly, placing quality data at the right time in the hands of the right decision makers.
In a recent article on adaptive data and analytics governance, global advisory firm Gartner outlines four key strategies in order of increasing complexity:
- Control – Also referred to as “command-and-control” governance, this strategy focuses on making decisions based on regulations, policies, and standards. This strategy involves the least complexity, and businesses generally employ it for regulatory compliance.
- Outcomes – A strategy that focuses on outcomes balances risk with return on investment while operating within company guidelines. An outcomes approach lends itself to supply chain analytics and the multichannel customer experience, for instance.
- Agility – An agility-minded strategy focuses on empowering decision makers to make informed decisions that will create business value. This considers the way individual people think and work, rather than relying on strict rules. And it puts data into the hands of those who need it through activities like self-service analytics.
- Autonomous – Autonomous strategies facilitate real-time decisions based on algorithms and logic. Humans define the models, but in many cases, machines carry out the decisions. For instance, credit card companies use AI algorithms for fraud detection. This allows them to react almost instantaneously in some cases.
Benefits of Adaptive Data Governance
Adaptive data governance delivers several key benefits to the organization. First and foremost, it supports agile decision making. By democratizing data responsibly, it provides decision makers with the quality data they need to make timely decisions that drive growth. In doing so, it builds data value.
Additionally, adaptive data governance allows businesses to respond quickly to changes in the business environment, to market disruptions and to regulations. By matching governance strategies to the situation, businesses can take a proactive approach instead of getting bogged down in red tape.
Moving to a More Flexible and Powerful Governance
Shifting to adaptive data governance from a traditional approach that emphasizes compliance and control requires time and careful planning. Businesses need to start by understanding what business decisions need to be made, who makes them, what data informs those decisions and how stakeholders access that data.
Then, businesses need to evaluate the current decision rights model against business needs. This process will likely uncover necessary changes in business processes, roles and data management. As organizations gain a better understanding of how stakeholders use data, they can determine the right data governance strategies to employ.
The data governance experts at Messaging Architects will help you implement data governance best practices that will take decision making and innovation to the next level.
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