Hi there! Data is now one of the most valuable assets for organizations in the digital era. But without proper governance, companies can easily get overwhelmed by the data deluge.
Implementing a sound data governance framework is crucial to help you manage data more efficiently, reduce costs, ensure compliance, and make better business decisions.
In this post, I‘ll explain what data governance is, the key reasons you need it, and how to create an effective governance framework. I‘ll also share examples and data that spotlight the positive impact it has on organizations. Let‘s get started!
What is Data Governance and Why it Matters
Data governance refers to the overall management and oversight of your organization‘s data assets. It establishes standards, policies, procedures, roles, and metrics to manage data throughout its lifecycle. The key goals include:
- Ensuring data quality, consistency, usability and integrity
- Managing data security and privacy risks
- Enabling data accessibility for business needs
- Meeting regulatory and compliance requirements
Here are some key reasons robust data governance is crucial:
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Data volumes are exploding: IDC predicts global data creation will grow to 175 zettabytes by 2025. Proper governance prevents this data flood from overwhelming your systems and people.
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Scattered, duplicate data is rampant: A study by Experian found that 95% of organizations struggle with data fragmentation across systems. Governance centralizes management.
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Compliance mandates are expanding: From GDPR to CCPA, regulations on data management are tightening. Governance helps track compliance.
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Customers demand data responsibility: With breaches rising, consumers want assurance their data is managed ethically. Governance boosts trust.
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Better decisions require reliable data: Governance improves data accuracy, timeliness and completeness for confident decisions.
Without governance, conflicting metrics and misinformation create analysis paralysis. But effective governance empowers your people to use data more impactfully.
According to a NewVantage Partners survey, 95% of executives say their firms urgently need to become data-driven. Robust data governance is the fuel that powers this transformation.
Creating a Data Governance Framework
A data governance framework provides the design elements to manage data consistently across your organization. It brings together people, policies, processes, metrics and tools for harnessing data. Here are the key components:
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Roles: Defines responsibilities of data owners, stewards, committee members, and other stakeholders.
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Policies: Document rules for data security, access, quality, retention, and acceptable use.
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Standards: Specifications for schema, taxonomy, quality, metadata, formats to ensure consistency.
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Processes: Design repeatable workflows for data lifecycle stages – from collection to deletion.
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Metrics: Quantifiable KPIs to measure data quality, usage, and business value generated.
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Tools: Solutions for data discovery, profiling, cataloging, workflow management, and self-service access.
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Architecture: Common models, principles, and guardrails for reliable data management across systems.
Gartner estimates that through 2023, organizations without data governance will lose an average of $15 million per year due to poor data quality. But structuring a governance framework can help you avoid these costs and risks.
Now let‘s explore the step-by-step process to build a data governance framework:
Step 1: Audit Current Data Environment
Conduct assessments to understand existing data landscape – policies, processes, systems, quality, and risks across business units.
Step 2: Define Scope and Goals
Determine the business objectives your governance framework aims to accomplish. Clarify domains and use cases covered.
Step 3: Identify Stakeholders
Pinpoint key stakeholders across business, IT, data owners, compliance teams that must participate in the program.
Step 4: Establish Governance Structure
Define oversight responsibilities and designate roles into a RACI chart to maintain accountability.
Step 5: Develop Policies
Create security, privacy, retention, access, and ethics policies that provide strategic direction.
Step 6: Set Data Standards
Define specifications for schema, taxonomy, quality, formats and conventions to enable consistency.
Step 7: Implement Processes
Design workflows for data lifecycle stages – from creation to destruction – guided by policies.
Step 8: Select Enabling Technologies
Choose solutions for data cataloging, quality, lineage, protection, and access to operationalize governance.
Step 9: Measure Governance Performance
Establish quantifiable KPIs to track data quality, risk reduction, user adoption, and business value.
Step 10: Monitor, Review and Enhance
Continuously collect feedback, assess issues, refine policies and metrics to improve governance and meet changing needs.
Now that you understand the framework components let‘s examine how leading companies have implemented data governance to drive growth.
Data Governance Driving Business Performance
Here are three examples of organizations that created data governance programs to improve decision making, reduce costs, and manage risks:
With over 722 million users, LinkedIn deals with huge data volumes and diversity. To enable confident data-driven decisions, it established a governance program guided by their analytics team.
Key elements included:
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Data dictionary of over 17,000 metrics with standard definitions
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Automated workflow for metric definition review and approval
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Data stewards appointed to govern quality across business units
This governance foundation led to 50% faster analysis and improved productivity across sales, marketing, and recruiting teams.
UPS
To boost efficiency in package delivery operations, UPS implemented centralized data governance covering customer, transportation, and employee data domains.
UPS established:
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A data governance board for oversight
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Data stewards across domains to define standards
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Issue escalation processes for data quality
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Self-service access to trusted data for users
These efforts reduced time spent on data preparation by 65%. Data analysts gained 33% more time for value-added analysis.
Morgan Stanley
The leading financial services firm implemented centralized data governance to improve regulatory reporting, risk management, and decision making.
They executed:
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A data stewardship program with 500+ data stewards
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Policies and standards for data quality and metadata
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Data issue escalation workflows
This enabled reliable analytics and regulatory reporting across capital markets, wealth management, and investment banking units. Data governance drove higher quality insights.
As you can see from these examples, formal data governance is key for large organizations to harness data‘s full potential while managing risks.
According to PriceWaterhouseCoopers, 63% of companies that invested in data governance saw funds previously lost to poor data reduced by 21-40%. The long term ROI is compelling.
Sustaining Data Governance Success
Beyond just establishing structural elements, nurturing a data-focused organizational culture is vital for long term data governance results.
Here are tips to help sustain success:
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Communicate benefits through success stories to showcase the strategic value of governance.
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Incentivize behaviors that reflect governance process adoption across units.
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Enable easy data access to engender trust while managing risks.
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Automate governance workflows via tools to reduce manual overheads.
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Start with focused pilots to demonstrate quick wins and ROI.
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Maintain organizational flexibility to adapt governance as data needs change.
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Keep governance lean yet effective. Don‘t over-engineer. Allow it to mature naturally.
With a flexible yet resilient data governance approach, you can maximize the business potential of your data assets for the long haul.
Continue Your Data Governance Journey
I hope this guide gave you a helpful overview of data governance frameworks – what they comprise and how to implement one tailored to your organization‘s needs.
To dig deeper and strengthen your data governance expertise, check out these handy resources:
- Data Governance 101 – Online course covering data governance fundamentals
- Practical Data Governance – Book by John Ladley explaining execution tactics with use cases
- Active Data Governance – End-to-end data governance platform by erwin
Feel free to reach out if you need any help or have questions while establishing data governance. This is foundational for unlocking data‘s true potential while maintaining trust.
Here‘s to effective data governance and driving even more value from your data!
