A CIO’s guide to data governance strategies and standards
MAY. 27, 2024
Implementing a data governance strategy can feel like an impossible mission, but it doesn’t have to.
Data governance. Two words that strike fear into the hearts of many CIOs. On one front, it means dealing with ever-expanding data sets and their security. On the other, the pressure to turn that data into insights that fuel innovation and growth.
Implementing a data governance strategy can feel like an impossible mission, but it doesn’t have to.
The right approach simplifies the complexities of managing vast amounts of data by establishing clear policies, roles, and responsibilities. It breaks down data silos by promoting data sharing and collaboration across departments. And it aligns your data management efforts with critical business objectives by ensuring data is accurate, secure, and used in decision-making and innovation.
Learn more about what this looks like in practice.
Assess your data governance maturity to find quick wins
Start by assessing your current data governance maturity. Several frameworks can guide this evaluation, each focusing on different capabilities and levels of progress:
- The Data Governance Institute’s Data Governance Framework defines five levels of maturity, from “Initial” to “Optimized”, based on capabilities like data quality, metadata management, and data stewardship. This model is well-suited for organizations just getting started with formal data governance that want to progressively build core capabilities.
- The Data Management Maturity (DMM) Model by the CMMI Institute assesses maturity across six process areas, including data governance, quality, and operations. The DMM model provides a comprehensive evaluation for organizations with more advanced data management practices seeking to benchmark against industry standards.
- IBM’s Data Governance Maturity Model focuses on four dimensions: organizational structures, stewardship, policy, and value creation. IBM’s model is helpful for large enterprises looking to align data governance with overall business strategy and measure the value governance delivers.
- Databricks Data Governance Maturity Model evaluates across eight elements, including quality, lineage, and collaboration. Their model provides a comprehensive assessment for organizations looking to enable data democratization while ensuring security and compliance.
Your organization’s goals and culture should determine the right framework. Run surveys, interviews, and workshops with a diverse group of key stakeholders — data owners, stewards, and consumers — to evaluate your current data management practices, pain points, and priorities.
By mapping your findings to the chosen maturity model, you can identify strengths, weaknesses, and quick wins — high-impact, low-effort initiatives that show early value. For example, if you discover inconsistent data definitions across departments, launching a data glossary to establish a common language for the organization is a great quick win.
Develop a data governance roadmap
Developing a data governance roadmap helps you prioritize initiatives, allocate resources, and communicate your strategy to stakeholders. Avoid trying to tackle everything at once. Implement data governance in phases, prioritizing initiatives based on business impact and feasibility.
Start with foundational elements like data quality, master data management, and metadata management. These practices lay the basis for more advanced capabilities. As you progress, introduce data security, privacy, and compliance initiatives.
If you’re in a highly regulated industry like healthcare, finance, or energy, compliance with GDPR, HIPAA, or SOX is a top priority. In such cases, you must handle data security, privacy, and compliance initiatives alongside foundational data governance. Work in tandem with legal and compliance teams to align your policies and standards with industry-specific regulatory requirements.
For example, the data governance strategy of a healthcare organization subject to HIPAA must prioritize protecting patient data from day one. This involves implementing access controls, encryption, and auditing mechanisms for safeguarding the confidentiality, integrity, and availability of protected health information (PHI).
In addition to industry-specific regulations, adopting security and privacy standards can help protect your data. Frameworks like ISO 27000, NIST SP 1800, or PCI DSS offer best practices and guidelines for implementing security controls, managing risk, and maintaining compliance.
Establish the right data governance operating model
Determining the organizational structure and decision-making framework for data governance activities is crucial as you develop your roadmap.
CIOs should evaluate these operating models:
Centralized
A single team oversees all data governance activities across the organization. This model suits smaller organizations needing tight control and standardization.
Decentralized
Each business unit or department manages data governance on its own with minimal central oversight. This model empowers data producers and domain experts, allowing responsiveness and flexibility for diverse business unit needs in large organizations with an aggressive strategy.
Hybrid
A central data governance team sets policies and standards, while business units have autonomy in implementation. This model balances the trade-offs, allowing growing organizations to efficiently manage data assets while adapting to evolving needs.
As CIO, you should guide the selection and establish clear communication channels between the central data governance team and the various business units. This ensures data governance policies and standards are consistently understood and applied across the organization.
Build a business case for data governance
The biggest challenge for CIOs implementing data governance is getting stakeholders on board. You need to convince others this initiative isn’t a bureaucratic burden by building a strong case for its business value.
Common objections you’ll hear:
- “Data governance is costly and time-consuming.”
- “Why do we need formal data governance now?”
- “Data governance will slow us down and hinder innovation.”
Consider these tactics to counter such perceptions.
Address concerns head-on
Time and costs. Implementing data governance requires an upfront investment of time and resources. But the long-term benefits — increased efficiency, reduced risk, better decision-making — outweigh the initial costs.
Why now? Some organizations managed without formal data governance in the past. The volume, variety, and velocity of data today, along with regulatory scrutiny and competitive pressures, make it a necessity.
Speed and innovation. When done right, data governance enables innovation by ensuring data quality, security, and accessibility. It provides a foundation for data-driven experimentation and insights.
Articulate the benefits of data governance with data
You’ll need metrics, case studies, and projections that quantify the potential impact of governance on your organization’s strategic goals. Such numbers and materials resonate with business leaders and build a compelling case for data governance.
You can also use your organization’s data to demonstrate the potential impact of data governance. For example, if poor data quality is causing a 20% error rate in your marketing campaigns, calculate the potential revenue gain from improving accuracy through governance.
Understand executive pain points and priorities
CIOs must understand other senior leaders’ unique challenges and objectives to secure stakeholder alignment and resources. To this end, you should schedule one-on-one meetings with key stakeholders to discuss their pain points, goals, and concerns related to data management.
For example, the Chief Financial Officer may be concerned about the cost of implementing data governance, while the Chief Marketing Officer may worry about the impact on customer data analytics. By understanding these specific pain points, you can tailor your plans and communication to address their concerns directly.
Create a data-driven culture with governance
Effective data governance is about more than policies and procedures – it’s about embedding data-driven practices into the daily rhythm of your organization.
Empower IT teams to drive data governance
To execute your data governance strategy, IT teams should have clear roles and responsibilities for:
Data quality management
Assign data stewards to monitor and maintain data quality for accuracy, completeness, and consistency.
Security and compliance
Establish a data security team responsible for implementing and enforcing controls, access management, and regulatory compliance.
Metadata management
Designate a metadata management team to document, track, and maintain metadata across the organization for improved data discovery and lineage.
Enable data-driven decision-making
Providing access to governed data isn’t enough — you must equip your teams with the tools, skills, and motivation to use that data effectively in their daily work.
Deploy self-service analytics platforms to let employees explore and derive insights from trusted data sources. Intuitive data visualization and business intelligence solutions can democratize access and allow for data-driven decision-making at all organizational levels.
Next, invest in human capital. Offer targeted training to upskill your workforce and spread data literacy across departments. Regularly share best practices and success stories to inspire and educate.
Finally, incentivize the adoption of data-driven practices. Implement recognition programs for employees who use data to innovate, optimize processes, and deliver business value. You can also align incentive structures with data governance objectives to reinforce desired behaviors and accelerate change.
Lead by example
You set the tone for how your organization values and manages its data assets. Make data governance a personal priority and use your leadership, influence, and political capital to inspire ongoing stakeholder engagement.
Share your data-driven insights with your team and stakeholders, explaining how you used trusted, high-quality information. When presenting to the board or executive committee, highlight the role of data governance in enabling better strategic decisions and business outcomes.
For example, Capital One’s CIO played a crucial role in building a data-driven culture by:
- Investing in a foundational data ecosystem that provides a marketplace for employees to access and use data.
- Providing training and an internal forum for users to learn data best practices and share methods.
- Encouraging executives to “live a day in the life” of a data user to understand challenges and determine where to focus data initiatives.
Encourage your direct reports and other leaders to do the same. Recognize individuals and teams that effectively use governed data to drive innovation, efficiency, and customer value.
Keep evolving your data governance strategy
Data governance is about compliance, but it’s also an ongoing mission to unlock the full potential of your data assets for innovation, efficiency, and competitive advantage.
To effectively monitor and improve your program:
- Establish key performance indicators (KPIs) that align with your strategic objectives, such as data quality scores, incident rates, usage metrics, compliance rates, and training coverage.
- Implement automated monitoring and reporting tools for real-time visibility, like data quality dashboards and governance scorecards.
- Regularly audit your data management practices to assess their effectiveness. Collaborate with different departments and use established data governance frameworks to ensure a comprehensive and structured assessment.
- Establish a data governance council or steering committee to monitor results and drive continuous improvement. This team should meet regularly to analyze metrics, identify areas for optimization, and prioritize initiatives.
More than anything else, the success of your data governance strategy depends on people. Your most important role as CIO is to inspire and empower your teams to embrace data governance as part of their daily work and decision-making processes.
