Introduction

Building a strong business case and engaging stakeholders are critical steps in selling data governance internally. This article will guide you through these processes, helping you to gain the necessary support and resources.

Building a Compelling Business Case

1. Identify the Benefits:
   Highlight how data governance will improve data quality, compliance, security, and operational efficiency. For instance, credit unions can use clean and accurate data to enhance fraud detection and prevention measures.
   
2. Quantify the Value:
   Quantifying the value of data governance involves demonstrating its tangible and intangible benefits using specific metrics and financial calculations. Here are several key areas where data governance can create value for credit unions, along with ways to quantify this value:
   
   
   1. Improved Data Quality
      
      Metric: Reduction in data errors and inaccuracies
      
      Example Calculation:
      • Identify the current error rate in member data (e.g., incorrect addresses, duplicate records).
      • Estimate the costs associated with these errors, such as operational inefficiencies, member dissatisfaction, and compliance risks.
      • Calculate the expected reduction in errors after implementing data governance.
      • Quantify the cost savings from reduced errors.
      
      Scenario: Before data governance: 10,000 errors/year with a correction cost of $5 per error = $50,000/year. After data governance: 2,000 errors/year = $10,000/year. Savings: $40,000/year.
      
   2. Regulatory Compliance
      
      Metric: Reduction in compliance-related fines and penalties
      
      Example Calculation:
      • List recent fines or penalties incurred due to non-compliance with data regulations.
      • Estimate the reduction in risk of fines with robust data governance in place.
      • Quantify the potential savings from avoiding fines.
      Scenario: Previous fines: $100,000 over three years. Expected reduction in fines with data governance: 80%. Savings: $80,000 over three years.
      
   3. Enhanced Data Security
      
      Metric: Reduction in data breaches and associated costs
      
      Example Calculation:
      • Determine the average cost of a data breach (e.g., $150 per record breached).
      • Estimate the likelihood and potential impact of breaches before and after implementing data governance.
      • Quantify the reduction in costs associated with fewer breaches.
      Scenario: Cost of breach: $150,000 (1,000 records at $150/record). Expected reduction in breaches with data governance: 70%. Savings: $105,000.
      
   4. Operational Efficiency
      
      Metric: Time and cost savings from streamlined processes
      
      Example Calculation:
      • Identify processes that are currently inefficient due to poor data quality (e.g., manual data entry, member service inquiries).
      • Estimate the time spent on these processes and the potential reduction with data governance.
      • Quantify the cost savings from increased efficiency.
      Scenario: Time spent on manual data entry: 500 hours/year at $30/hour = $15,000/year. Expected reduction in time: 50%. Savings: $7,500/year.
      
   5. Strategic Decision Making
      
      Metric: Improved decision-making capabilities leading to better financial performance
      
      Example Calculation:
      • Identify key decisions that were previously hindered by poor data quality (e.g., product development, marketing strategies).
      • Estimate the financial impact of improved decision-making.
      • Quantify the value added from more accurate and timely insights.
      Scenario: Revenue increase from better-targeted marketing campaigns: $200,000/year. Attributed to improved data governance: 20%. Added Value: $40,000/year.
      
3. Address Risks and Mitigation:
   Outline the risks of not implementing data governance and how it mitigates those risks. Discuss recent data breaches in the financial industry and how data governance could prevent similar incidents.
   
4. Align with Organizational Goals:
   Show how data governance supports the credit union’s strategic objectives, such as improving member experience, expanding digital services, and enhancing compliance capabilities.

Engaging Stakeholders

1. Identify Key Stakeholders
   Recognize who needs to be involved, including executives, department heads, and data stewards. For example, the Chief Information Officer (CIO), Chief Lending Officer, Chief Marketing Officer, Chief Risk Officer (CRO) are critical allies in promoting data governance.
   
2. Communicate Effectively
   Use clear and compelling messaging to explain the benefits and importance of data governance. Share success stories from other credit unions that have successfully implemented data governance.
   
3. Foster Collaboration
   Encourage cross-departmental collaboration to break down data silos and unify efforts. Highlight examples of how departments like lending, compliance, and IT can work together to achieve data governance goals.
   
4. Provide Training and Education
   Equip stakeholders with the knowledge and skills they need to support data governance initiatives. Offer training sessions on data governance best practices and the specific tools your credit union will use.

Conclusion

Building a solid business case and engaging stakeholders are foundational steps in selling data governance internally. In the next article, we will discuss how to create a roadmap and execute the data governance plan.

Checkout our next article: "Creating a Data Governance Roadmap and Executing It."