Choose Adaptive Data Governance Over One-Size-Fits-All for Greater Flexibility

Unprecedented disruptions, such as the COVID-19 pandemic or political and economic conflict, expose enterprise fragility and demonstrate that being robust or resilient, while vital, is not enough. 

Responding to varying levels of uncertainty in today’s world requires speed and agility, and traditional approaches to data governance are becoming obsolete. A typical ‘one-size-fits-all,’ command-and-control-based IT governance capability has neither the scope nor the agility to meet the needs of digital business.

Data and analytics governance specifies decision rights and accountability to ensure appropriate behavior as organizations seek to value, create, consume, and control their data, analytics and information assets. It’s critical to link data governance to overall business strategy and to anchor it to those data and analytics (D&A) assets that organizational stakeholders consider critical.

But whether D&A governance initiatives are IT-led or business-led, they are falling well short of expectations. In a Gartner D&A governance survey conducted in 2021, 61% of respondents said their governance objectives included optimization of data for business processes and productivity, but only 42% of that group believed that they were on track to meet that goal.

Adaptive governance enables flexible and nimble decision-making processes that help an organization respond quickly to opportunities, while continuously addressing investments, risk and value. 

Our analysts estimate that through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data governance.

How to start adaptive governance

Before implementing an adaptive data governance strategy, three steps are required:

1. Define a clear set of adaptive data governance principles. For example: treating information assets in accordance with their value and sensitivity by partnering with business stakeholders and enterprise leaders. Align principles with your organization’s dynamics, culture, and leadership style. 
2. Establish accountability decision rights across organizational areas, such as business operations, data, and technology teams, and analytics centers of excellence, and assign their role in achieving specific business outcomes.
3. Apply the right adaptive governance style to your business scenario, so that the correct levels of governance oversight and governance instruments are used to achieve your business outcomes.
4. Sustain adaptive governance by basing your governance operating model on it. Ensure that the impact of data and analytics governance decisions is understood across the organization.

Adaptive data governance includes multiple styles

Using adaptive governance or a multi-style approach means that business and IT leaders can use one or a combination of four governance styles to meet the demands of existing use cases, as well as the emerging requirements of digital business. 

1. Control: When making decisions according to rules, policies, standards, and directives, think master data management (MDM) or compliance with requirements such as GDPR. 
2. Outcomes: When trying to achieve business outcomes while balancing risk, return, and performance on investments.
3. Agility: When empowering roles and teams with authority to make distributed and/or mandated decisions that create value for their stakeholders.
4. Autonomous: When decisions are made in real-time by people and “things.” In gas fracking, for example, prescriptive analytics uses real-time data to make governance decisions based on economic algorithms.

Moving to adaptive governance takes time

The shift from a single-style governance approach to adaptive governance cannot happen overnight. “It requires planning and coordination with business stakeholders — both internal and external,” says Judah. “Maturity is also key. Unless an organization is mature enough to undertake adaptive governance, they shouldn’t.” To succeed, 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 operating your data governance in an open and transparent manner.

The original article by Saul Judah, vice president 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. Image credits: iStockphoto/Steven White; Chart/Gartner