Do inconsistent data definitions keep your teams confused? Are spreadsheet-based processes limiting your data potential? An authoritative data governance framework may be the missing link needed to connect your data to corporate strategy.
In this comprehensive guide, we’ll explore pragmatic approaches for developing a tailored data governance strategy. With careful planning and commitment to excellence, your organization can also implement governance capabilities that reduce risk, spark innovation, and accelerate data-driven decision making.
What Exactly is a Data Governance Framework?
A data governance framework refers to an organization‘s integrated set of policies, procedures, roles, technologies and metrics that standardize and control data management across the enterprise. It provides the backbone for ensuring high quality data and analytics that truly serve business objectives and create value.
This overarching structure establishes systematic processes for governing data throughout key phases – from capturing, processing, analyzing to reporting data in consistent, secured and compliant ways that engender trust in organizational data assets.
In essence, a data governance framework codifies the guidelines for data accountability across multiple functions needed to uphold stability and confidence in data leveraged for crucial business decisions. It connects appropriate data stewardship with data valorization.
The Critical Drivers for Prioritizing Data Governance Today
With ever expanding volumes of data spread across complex, hybrid technology landscapes, many organizations hoje struggle to reconcile data management with strategic aims. A 2022 IDC survey found data governance and data culture building to be top data management priorities for companies over the next 1-2 ears.
Moreover, factors like turbulent market conditions, evolving consumer preferences, and an accelerated pace of digital transformation are mandating more holistic data mastery for sustained competitive strength.
Against this backdrop, a formalized data governance discipline enables enterprises to:
Gain More Value from Data Investments
- Ensure high quality, trustworthy analytics data for advanced modeling and insights
- Consistent data definitions and rules prevent flawed assumptions or decisions
- Prioritize investments based on measurement of data asset value
Strengthen Operational Resilience
- Prevent unauthorized data access via protocols and controls
- Rapid data breach assessment and remediation
- Meet regulatory compliance and audits through documentation
Increase Business Agility
- Faster location of required data when launching initiatives
- Ability to swiftly adapt existing data to new business needs
- Fund new innovations via monetization of external data sharing
The incentives for implementing a robust framework make a compelling case. But what are the key considerations and best practices for brings this vision to reality?
8 Steps to Build a Future-Ready Data Governance Strategy
Developing an impactful governance strategy requires coordination across people, processes and enabling technologies. The major phases include:
Step 1) Define the Vision and Set Goals
Clarify the intended outcomes, priorities and scope for governing data assets. Document how this helps advance enterprise objectives. Outline milestones for capabilities to implement.
Step 2) Secure Executive Buy-In
Earn support from senior leadership by quantifying the projected cost savings and revenue upside, tied to governance. Assign a senior executive as the data governance sponsor to secure buy-in across functions.
Step 3) Map Your Data Landscape
Discover existing data sources, systems and flows. Catalog data objects into an inventory detailing security classifications, business definitions, data stewards and technology metadata via automated data discovery and cataloguing tools.
Step 4) Profile Your Data Health
Assess quality metrics (like completeness, uniqueness, validity) across data assets to establish benchmarks. Perform gap analysis to identify and prioritize areas needing governance focus due to excessive duplication, deficient integrity or breach risks.
Step 5) Establish Policies and Rules
Codify security protocols, user access guidelines, acceptable usage standards, change control procedures, data dictionary definitions and persisted quality rules optimized for your data ecosystem.
Step 6) Define Governance Roles and Responsibilities
The governance organizational structure tying accountability to outcomes typically consists of:
- Data Owners: Executive leaders who own data strategy for their respective business domains.
- Data Stewards: Subject matter experts, accountable for defining policies and the veracity of data used in business functions and decision systems they support.
- Data Custodians: IT professionals responsible for implementing governance controls and processes within systems that store, process and manage data.
- Data Trustees / Council: Cross-functional leadership team governing data as an enterprise asset. Defines policies for data stewards and custodians to carry out.
Step 7) Implement Supporting Practices and Technologies
Capabilities to deploy include data quality monitoring and issue resolution workflows, access request ticketing systems, reference data management, data integration management, activity logging, as well as tools like data catalogs, data lineage and usage tracking.
Step 8) Monitor, Measure and Optimize
Ongoing performance measurement via data quality KPIs, policy violation alerts and risk metrics maintains visibility over assets. Regular data governance maturity assessments identify areas for enhancing policies or accountability for continuous improvement. A virtuous cycle sustaining data‘s fitness to serve evolving business needs.
Real World Data Governance Success Stories
Effective data governance fueling performance isn‘t just theoretical. Enterprise trailblazers reaping big rewards from dedicated governance strategies include:
Transforming Retail and eCommerce Experiences
UK supermarket giant Tesco centralized control over product data standards and quality across markets. This accelerated expansion into new countries while boosting supply chain efficiencies. They‘ve registered £100 million in incremental revenues since overhauling data governance.
New Heights of Customer Trust and Loyalty
Mastercard constructed strong data accountability measures aligned to ethics codes protecting consumer data. This supported radical digital innovation in payments while strengthening brand reputation and customer satisfaction scores by as much as 72%.
Breakthroughs in Healthcare and Drug Discovery
Novartis instituted consistency in research data collection, storage and accessibility protocols across labs. This facilitated better clinical analytics insights, fuelling development of blockbuster leukemia treatments saving thousands of lives annually.
The decision to commit to excellence in data governance is truly a watershed moment positioning leading organizations to thrive amidst volatility and maximise next-generation opportunities fuelled by AI and advanced analytics.
5 Keys to Unlocking Successful Data Governance Framework Implementation
However, according to Gartner research, 60% of all data governance programs fail to fully meet initial objectives. The roots of these shortcomings mostly stem from cultural and organizational barriers rather than just technology alone. This underscores that realizing the benefits of data governance relies on change management proficiency.
Here are 5 indispensable guideposts to avoid the pitfalls and steer your data governance strategy to success:
Secure Executive Sponsorship
Having an influential executive own governance oversight is vital. Their authority to allocate resources and align initiatives is essential to drive adoption.
Lead with Business Outcomes
Continually reconnect program elements like policies and metrics back to business value delivered to maintain stakeholder engagement momentum.
Encourage Data Literacy Companywide
Equipping employees at all levels with context for how governance enables performance and risk management accelerates culture transformation.
Take Iterative Actions
Piloting defined use cases and building out capabilities in phases provides room for feedback and flexibility needed to stick the landing.
Embed Accountability
Connecting data quality and policy adherence to personal performance evaluation motivates ongoing participation and priority across the workforces
Looking To The Future
As data‘s role as the fuel energizing digital business models and experiences intensifies, crafting a structured data governance approach offers organizations the best chance of accelerating innovation while keeping risk in check. Advances in automation and machine learning are opening possibilities to streamline governance processes for added business agility.
“By 2023, organizations that leverage ML-enabled data governance applications to automate key processes will achieve 40% greater accuracy than their peers,” predicts Gartner.
Going forward, leading companies will increasingly differentiate themselves by the data governance strategies they follow to securely optimize data value. The future strongly favors disciplined data masterers. Is your organization ready?
