Data and analytics governance specifies decision rights and accountability to ensure appropriate behavior as organizations seek to enhance, create, consume, and control their data, analytics, and information assets. It is essential to tie data governance to the overall business strategy and anchor it to the data and analytics (D&A) assets that the organization’s stakeholders consider essential.
But whether D&A governance initiatives are IT-led or business-led, they fall far short of expectations. In a Gartner D&A Governance Survey conducted in 2021, 61% of respondents said their governance goals included optimizing data for business processes and productivity, but only 42% of this group believed they were on track to achieve this goal.
Adaptive governance enables flexible and agile decision-making processes that help an organization react quickly to opportunities, while continuously addressing investment, risk, and value.
Our analysts estimate that by 2025, 80% of organizations looking to grow their digital businesses will fail because they don’t take a modern approach to data governance.
How to start adaptive governance
Before implementing an adaptive data governance strategy, three steps are necessary:
- Define a clear set of adaptive data governance principles. For example: treat information assets based on their value and sensitivity by partnering with stakeholders and business leaders. Align the principles with your organization’s dynamics, culture and leadership style.
- Establish accountability decision rights in all organizational areassuch as business operations, data and technology teams, and analytics centers of excellence, and assign their role in driving specific business outcomes.
- Apply the right style of adaptive governance to your business scenario, so that the right levels of governance oversight and governance instruments are used to achieve your business results.
- Maintain adaptive governance by basing your governance operating model on it. Ensure the impact of data and analytics governance decisions is understood across the organization.
Adaptive data governance includes several styles
Using adaptive governance or a multi-style approach means that business and IT managers can use one or a combination of four governance styles to meet the requirements of existing use cases, as well as emerging business requirements. digital business.
- Control: When making decisions based on rules, policies, standards and guidelines, think about master data management (MDM) or compliance with requirements such as GDPR.
- Results : When trying to achieve trading results while balancing risk, return and investment performance.
- Agility: When you empower roles and teams to make distributed and/or mandated decisions that create value for their stakeholders.
- Autonomous: When decisions are made in real time by people and “things”. In gas fracturing, for example, prescriptive analytics uses real-time data to make governance decisions based on economic algorithms.
Moving to adaptive governance takes time
Moving from a one-style governance approach to adaptive governance cannot happen overnight. This requires planning and coordination with business stakeholders, both internal and external. Maturity is also essential. Unless an organization is mature enough to undertake adaptive governance, it should not. To be successful, you will likely need to reassess your organization’s D&A strategy, employ careful design and testing, and invest in skills and competencies, such as managing your data governance in an open and transparent manner.
The original article by Saul Judah, VP Analyst at Gartner, is here.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Photo credit: iStockphoto/LunaKate