Who’s in the driver’s seat? Building your data governance framework.

Data can be a lot of things. It can drive stability. It can weed out ambiguity. At its best, it can lay the foundation for smart decision-making, elevated performance, and growth. 

Associations and nonprofits are no exception to these rules. They rely on data to properly engage members and prospects, create membership programs, optimize content, provide services, and manage many other functions throughout the day-to-day. 

The challenge is ensuring that the data used is reliable. 

Operating on outdated, incomplete, or duplicate data can cause a number of challenges for your organization, including (just to name a few):

• Non-compliance issues
• Unsatisfied members
• Disjointed customer experiences
• Money wasted on insufficient campaigns 
• Lackluster business intel for programmatic development

As such, it's vital to ensure data quality, measurability, meaning, and consistency to avoid adverse outcomes. All of which are achievable through data governance.

To that end, the purpose of association data governance is to support and protect your business objectives. It ensures that your leaders can make the best possible decisions, that employees are using clean, reliable information to meet their responsibilities, and that goals are cross-functionally owned. 

By setting, documenting, and enforcing data standards across teams, a strong governing framework can:

• Drive data centralization: Establishing a single source of truth by synching data across your systems.  
• Maintain data integrity: Ensuring all information used is accurate, complete, and up-to-date.  
• Keep data secure: Keeping your proprietary and customer information in the hands of authorized individuals.  
• Help with compliance: Adhering to compliance standards, particularly with privacy regulations and today's permission-based sending models.    

• Flow (what gets collected and how it moves from system to system)
• Ownership (who is responsible)
• Accessibility (who can use/view it)
• Management (who can create or alter it)

1. Data Myopic: Association vs member centric, without intention, accountability or metrics.
2. Data Intended: Philosophically goal-tied and member-centric without 
   the right tech, centralization, and policies in place.
3. Data Democratic: Culture, framework, and technology map to methodology and practice.

• The right integration(s) 
• The right data sources (tech stack)
• The right people
• The right data governance 

1—Consent Management: To mitigate compliance risks, are you leveraging permission-based sending models. How are you honoring contact preferences?   

2—Audience Management: What minimum set of data is needed to build each contact record? Do you have SOPs on importing external lists?

3—User Management: Within a granular scope, who is authorized to create, delete, or modify records? Who creates operational workflows? 

4—Engagement Management: How do you identify interest? Is there a threshold that changes a contact status, like moving from "contact browsing" to "contact interested?"  

Including your IT team in the discussion at the onset, along with all relevant stakeholders, promotes transparency and adherence so that you're maintaining continuity across your entire tech stack.

Once you've set governance across each area, you'll want to do a few things as part of your change management plan. This isn't an exhaustive list, but it's a start:

• Create an accessible, centralized documentation portal
• Socialize that documentation portal as the go-to for data management
• Identify a few "champions," (those on the ground floor pushing the proverbial chirp near the water cooler) to give staff a safe space to voice concerns about their role, the process, expectations, etc.
  
• Identify a few "enforcers" (well-respected leaders cascading the message from the top) to ensure the rules are understood and adhered to.
• Build a training cadence to ensure each user knows their role and responsibility relating to data management practices.

Your data governance library

At minimum, consider the following as documentation to establish and steward data governance at your organization: 

Data Czar Team: Also known as your Data Governance Team, this group is identified to effectively establish governance over the collection, management, and use of your data. This team is also accountable for ensuring your entire org is knowledgeable about and accountable for the plan. 

Data Governance Plan: This is a master plan that clearly identifies the org's single source of truth along with ways that to sustain it as the source of master data. It explains data goals, data use cases, and data owners, and outlines what protocols will be put in place around your five governance areas.

Data Dictionary: This is an organizational guide that dives into data categories, data sources, data models, data fields and attributes for org-wide efficiency. 

Data Compliance Handbook: This guide clearly defines what organizations needs to adhere to in order to stay compliant with government and industry regulations. 

Data Continuity Guide: This manual standardizes the ways teams store, organize, access, and utilize data across the organization.

Of course building and culturalizing your data governance framework isn't a one-and-done deal. Yes, the bulk of work lies in ideation, documentation, and change management. But ongoing activities will balance effort with measurement and refinement.