<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=520757221678604&amp;ev=PageView&amp;noscript=1">
Aimee Pagano

By: Aimee Pagano on January 10th, 2024

Print/Save as PDF

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

data management | data integration | data activation | data centralization | data governance

mailto:demo@example.com?Subject=HighRoad Solutions - interesting article

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Operating on outdated, incomplete, or duplicate data can cause a number of challenges for your organization, including (just to name a few):

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.

Supporting business objectives
Data governance is simply setting rules within an organization that manage process across all data sources. Just like governing a city or state, association governing frameworks must establish formal policies and procedures that internal (staff) and external (third party technologies) must follow. 

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.    
Establishing a governance way
Another way to look at Data Governance is through its outputs. From that perspective, a governance framework surfaces the 'F-O-A-M' that sits at the top of your organization:
  • 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)
Finding FOAM
Now leveraging a clever little acronym on it's own isn't going to establish data governance. Data reflection may be needed to start putting governance in place.
Before you move forward into governance, you need to first determine where your organization fits in the world of data evolution. Start with a little self-reflection when it comes to data. Is your org:
  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.
Once you've identified your org stage, you can then set measurable means to graduate from one category to the next. Regardless of what stage you're currently in, movement from one stage to the other is predicated on all four factors being in place: 
  • The right integration(s) 
  • The right data sources (tech stack)
  • The right people
  • The right data governance 
For the first three bullets, search under any webinar and/or blog on our website and you'll find solutions to help you get these in place. For data governance, keep moving along with us here😉.
Data intervention
Once you have the first three factors locked in, it's now time ask yourself the hard questions about your data across five main governance areas. Here are some examples for each: 

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?"  

5—Asset Management: How do you define taxonomy, naming conventions, and classify objects across your systems? Do you have a cadence and procedure for cleaning out these assets? 
IT must be part of the conversation 
Because of data flow, these governance areas can be complex and tie into one another. For instance, Association X may use Personify as their Association Management System (AMS), CVENT as their Event Management System (EMS), and HubSpot for marketing automation. While these tools may be adopted by different teams for separate use cases, they are likely using the same information, like contact data, in which case consent and audience management need to be addressed universally and simultaneously. 

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.

Time to set priorities and establish your governance policies  
Once you've worked your way through the critical governance questions, and have come to reasonably manageable and agreed upon protocols and SOPs, you can begin establishing your policies org wide.
Note that it's nearly impossible to create formal processes and procedures that address all five areas simultaneously. Therefore, you'll need to prioritize which governance functions require the most immediate attention.
For example, you may find that your consent management policies are drastically behind the ball. Given the risks and legal liabilities associated with not adhering to today's permission-based sending models and requirements, this may be a first start for organizations. For some, it may be security for obvious reasons. Regardless of what your hierarchy looks like, make sure you don't bite off more than you can chew so that, from a change management perspective, your org doesn't get turned off by the process entirely.

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. 

Need to centralize your data for governance? We can help.
Book a consultation today to learn more about how Spark can help create that single source of truth within your org.

About Aimee Pagano

Aimee joins HighRoad Solution with 15+ years of integrated marketing and communications experience, primarily in client-facing roles within the association and SaaS space. Her specialties include persona development, content strategy/management, lead gen and awareness campaign development, and website development/optimization.