If you can’t trust the data you’re looking at, how valuable is the data? Unreliable, low-quality, or inaccurate data simply leads to poor analysis and decision-making. This can quickly erode user confidence despite organizational efforts to use data effectively.
Data governance provides the foundation for trust. With the right framework in place, you benefit in a variety of ways by increasing:
- Data quality
- Trust in data accuracy
- Regulatory compliance
- Data security
Yet, 80% of organizations looking to scale digitally fail because of underlying problems with data governance, according to Gartner. Often, it’s because team members see data governance as a set of complex rules rather than a key business driver. Good data governance improves decision-making and creates value.
"Leading firms have eliminated millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars. Data governance is one of the top three differences between firms that capture this value and firms that don’t.” – McKinsey & Company
Still, many companies lack a mature data governance framework. A 2023 survey reported that only 27% of CIOs considered their governance of enterprise information as mature. Nearly 70% said they were in the beginning stages of creating the necessary data discipline.
Component of a Robust Data Governance Framework
While there are no one-size-fits-all data governance policies, there is a set of underlying principles that should guide your adoption.
Data Quality
Data quality focuses on ensuring the data you utilize is accurate, consistent, and reliable. Key elements include:
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Data profiling: Analyzing data structure, content, and quality.
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Data cleansing: Identifying and correcting errors, inconsistencies, and missing values.
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Data standardization: Setting and enforcing clear definitions and formats for data elements.
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Data validation: Implementing processes to verify data accuracy and compliance with standards.
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Monitoring and alerting: Continuously checking and reporting on data quality issues.
Data Stewardship
Stewardship assigns accountability for specific data assets to individuals or teams responsible for its quality, security, and usage. Data stewards act as champions for their assigned data, ensuring it's managed effectively and used responsibly. Best practices include:
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Clear roles and responsibilities: Defining ownership and accountabilities for different data domains.
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Training and empowerment: Equipping data stewards with the knowledge and tools to fulfill their responsibilities.
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Communication and collaboration: Fostering communication between data stewards and other stakeholders.
Data Protection and Compliance
With data breaches rising 78% in 2023, data protection and compliance must prioritize safeguarding data privacy and security. Key components include:
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Data security policies and procedures: Establishing protocols for access control, encryption, and data loss prevention.
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Privacy policies and procedures: Defining how personal data is collected, used, stored, and protected.
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Compliance with regulations: Adhering to relevant data privacy regulations like GDPR and CCPA.
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Incident response plan: Having a process for detecting, responding, and recovering from data breaches.
Data Management
Data management across the entire lifecycle of data is crucial from creation and collection to storage, analysis, and deletion. Robust data management includes:
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Data architecture: Defining the organization and structure of data storage and systems.
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Data classification: Categorizing data based on sensitivity, usage, and retention requirements.
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Data retention and archiving: Determining how long data is stored and how it's archived or disposed of.
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Data access: Defining who can access what data and under what conditions.
Enabling Data Governance
It’s not enough to simply write the rules for data governance. Organizations must put in place the right strategies to enable and enforce a data governance framework. Several key steps will drive adoption.
Executive Sponsorship
Executive leadership must drive the process forward, requiring strict adherence to the framework across the organization. Data must be seen as a strategic priority and key driver for business growth. As such, governance must be universally implemented to be effective.
Integration Across Production and Consumption
To ensure data governance aligns with business needs, the responsibility for governance must be integrated at each step in the process. Product teams and development teams must ensure compliance, especially when adding new technology or modernizing business systems.
Data Prioritization
Effective data governance is holistic, ensuring accuracy throughout organizations. However, it can be a challenging process to retrofit policies against existing datasets. Governance teams can define data criticality within each domain and focus governance initially on the data that has the biggest business impact along with any new data created. Over time, however, all data must conform to governance policies to be effective.
Data Governance Controls
Once data governance has been applied, it’s important to validate it. This might include manual checks, automated reviews, and audits. The right data governance tools or platforms can automate some of these processes to simplify validation.
Monitoring and Adapting
Maintaining data governance is an ongoing process. As needs change or new industry regulations emerge, it will be important to review policies and adapt as necessary. It helps to set up tracking for key metrics, such as data quality scores, anomalies, or breaches for regular review.
Create Broad Awareness
There’s a human component to data governance, too. Ultimately, the quality of the data will depend on broad acceptance by everyone who has access to your data. When team members are committed to data governance principles, they are more likely to ensure that data is high quality and safe. Data governance must become embedded into company culture and not seen as a set of rules, but as essential to informed decision-making.
Data Governance is More Than Compliance
When organizations establish a strong data governance program, it creates a culture of transparency and accountability about data usage. In turn, this increases trust — key to making confident, data-driven business decisions. After all, the goal of data governance isn’t really compliance, it’s to create better business outcomes that add significant value to your organization.
Wyn Enterprise provides industry-leading business intelligence (BI) with extensible security to match your data governance protocols. Built-in end-to-end security, role-based permissions, and data governance and modeling create the framework you need for robust governance. Wyn Enterprises goes beyond simply allowing you to work in a secure, centralized environment. It empowers you to actively manage and configure your data governance and security with granular control.
Bhupesh Malhotra
