By Saul Judah
Unprecedented disruptions, such as economic uncertainty, political conflict, or the COVID-19 pandemic, expose enterprise fragility and prove that resilience, while vital, is not enough.
Responding to varying levels of uncertainty in today’s world requires speed and agility, and traditional approaches to data and analytics 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.
In fact, Gartner predicts that through 2025, 80 per cent of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.
Data and analytics (D&A) governance specifies decision rights and accountability to ensure appropriate behaviour as organizations seek to value, create, consume, and control their data, analytics and information assets. Traditional governance is often too slow and cumbersome for many digital business scenarios, resulting in non-engagement and in some cases, resistance to governance initiatives.
Adaptive governance enables flexible and nimble decision-making processes that help an organization respond quickly to opportunities, while continuously addressing investment, risk, and value.
Start adaptive governance
Take the following four steps to implement adaptive data and analytics governance:
- Define a clear set of adaptive data and analytics 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.
- 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.
- 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.
- 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 and analytics governance offers multiple styles
Using adaptive governance 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.
- Control: When making decisions according to rules, policies, standards, and directives, think master data management (MDM) or compliance with requirements such as CASL.
- Outcomes: Achieving organizational outcomes in a volatile business environment while balancing risk, return and performance on D&A investments.
- Agility: Empowering roles and teams with authority to make distributed and/or locally mandated decisions that create value for their stakeholders.
- Autonomous: When decisions are made in real time by people and “things.”
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. Maturity is also key. Unless an organization is mature enough to undertake adaptive governance, they should not. To succeed, organizations must mature their data and analytics governance practices in key areas, such as data literacy, decision rights, trust, and risk management.
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Saul Judah is a VP Analyst at Gartner, Inc. where he advises clients on information governance, data quality and information strategy. Gartner analysts will provide additional analysis on data governance at the Gartner Data & Analytics Summit taking place August 22-24.