Implementing a data governance program and building the framework to execute it can seem like a very time-consuming and costly project. But it doesn’t have to be. There is an easier way to approach data governance and it will yield big returns for a data-driven organization.

The number one asset of any organization today is its data. Data has the power to provide insights across every business unit for better decision making across the organization. That’s why the way data is governed—collected, organized, and managed—should be a top priority for any organization—big or small.

Data governance means a lot of things and covers a lot of areas, but it doesn’t have to be complex. In fact, a simple data governance program and data governance framework that integrate more naturally in business processes will get better user adoption, and lead to better decision-making across the organization. In this blog, we will explain what a democratized approach to data governance is and why it’s our preferred approach. We will also cover how to build a data governance framework to support your program, and best practices to follow in keeping it simple and effective.

What Is A Data Governance Program?

A data governance program is a collection of practices and processes that form an approach to manage the data assets of an organization. You can have a more centralized approach to your data governance program, which is more traditional and has strict rules in place about who has access to what data and how they can use it. Or, you can have a more democratized approach, where all your business users are granted access to big data and enabled to self-serve analytics, and ultimately lead to faster decision-making. Although there isn’t a one-size-fits all approach, and it usually depends on the size of your organization and the resources you have to commit to a data governance program, a democratized approach will provide “just enough” data governance to see quick wins and measurable ROI, as well as allow you to start small and scale as you learn more.

Why Is A Data Governance Program Important?

As businesses create vastly more data than they know how to process and it’s coming in from hundreds of sources, they need a data governance program in place that will ensure a consistent approach to the valuation, creation, consumption, and control of big data. The data governance program holds people, processes, and technology accountable by answering questions such as:

  • What constitutes data?
  • Where and how is data collected, extracted, transformed, delivered, and used?
  • Who cares for and maintains which data?

A data governance program is required in a modern and cross functional data and analytics environment because it ensures that data is usable, high quality, trustworthy, accessible, and secure—and by extension, brings value to everything else that the organization is trying to do with the data. The most effective way to ensure your program is successful is to start with building a data governance framework.

How Do You Build a Data Governance Framework to Support Your Data Governance Program?

Implementing a data governance program is a cultural shift within the organization. And in order for the implementation to be successful, you need a data governance framework that is easy to follow, easy to understand, and easy to communicate.

What Is A Data Governance Framework?

A data governance framework defines how you will implement a data governance program. It creates a single set of rules and processes around data management—and in turn makes it easier to execute your data governance program.

Here are a few steps you can take in creating a data governance framework when approaching a more democratized data governance program:

  • Develop Policies and Standards: To be able to effectively communicate your data governance program and get buy-in from the organization, you need to establish policies and standards for data management from the beginning and assign data stewards whose role it is to ensure that the policies and standards turn into practice across the organization. The two most critical things to address are data quality and data security. Establish and maintain thresholds for data quality metrics and identify and create rules for sensitive data and regulatory compliance. Democratizing your data still requires security, it’s just not as governed. Take inventory of policies and standards already in place, identify gaps, and add initiatives to a backlog to address the gaps. Define user roles and responsibilities for who will manage and monitor data quality and data security.
  • Create A Business Glossary: A business glossary provides an agreed upon standard set of definitions across an organization and in many cases may force the business to collaborate on the definitions with stakeholders from multiple departments—sometimes to even re-think or improve the way they were defining a key metric. Start with a business glossary of your organization’s core KPIs and expand in iterations to provide maximum value. Avoid any kind of manually documented column-level technical metadata—required with many traditional data dictionaries—as it takes effort to build, is not a good use of time to maintain, quickly becomes outdated, and is usually too technical for business users. Although there is value in data dictionaries and data catalogs if implemented incrementally with appropriate automation and scope, you will still need to first get agreement on the business definitions, which is why your first step should be to start with a business glossary.
  • Establish Data Governance Processes: It’s critical to establish new data governance processes that can be integrated within existing business processes. Establishing data governance processes will allow you to scale and mature your data governance program over time. Examples include:
    • Processes to govern self-service: Even though business users are empowered to use self-serve analytics, you should still put in place guidelines on how to proceed. Processes may include a level of approval required before the business user can publish their analytics or use the results in an external facing presentation.
    • Processes to govern how new data sources are integrated: Consuming more data from new data sources can help you meet but your business objectives, but you should have some guardrails in how that data is integrated. You can dictate that new data acquired must have input from the business on how it will be used before back-end decisions are made on how it is modeled. A process can require that your data governance committee agree on definitions for any new business metrics.
    • Processes to govern new report or dashboard requests: When business users want to create a new dashboard, you should have processes in place that can confirm that there isn’t overlap with existing content or projects. Other processes can include, for example, documenting and standardizing if something like data validation is required.
  • Apply Governance Metrics: Defining and applying metrics to track and measure various aspects of your data governance program is crucial to understanding the impact, value, and relevance of the program across the organization. Some governance metrics examples include:
    • People metrics: An indicator of a successful data governance program is an increase in user adoption. You can track how often business users are logging in to view and refreshing their reports and dashboards.
    • Data quality metrics:  An indicator of good data quality is decreased reliance on IT support and improved productivity among business users.

What Are Best Practices to Get Started with a Simple Data Governance Program?

Although building a data governance program can be a time-consuming and costly endeavor, it doesn’t necessarily have to be. Every organization is different, but all can benefit from following best practices when approaching data governance. Here are a few things to keep in mind:

1.) Start with Identifying Leadership:

A top-down approach to data governance will garner the most support across the organization. Start with getting executive buy-in, then identify leaders throughout the organization that can steer a committee dedicated to seeing the program through.

2.) Establish a Data Governance Committee:

The committee should include staff from a variety of business units whose purpose is to establish clear data definitions, develop comprehensive policies, oversee documentation by which internal business units collect, steward, disseminate, and integrate data on behalf of the organization. Your data governance committee should include data owners and data stewards.

3.) Aim For “Just Enough” Governance:

Start small and iterate. Don’t attempt to govern every aspect of your data. Start with two to four business areas that are most interested in helping with data governance and begin by looking at the data subject areas that they’re most interested in. Form working groups right from the start so that the main work of data governance can be accomplished in small groups and the larger committee meetings can be focused on holistic progress and setting direction for the working groups.

4.) Be Transparent:

Lineage shows the end-to-end history—all the metadata—of data from its creation all the way through to its transformation. By being transparent and defining data lineage, everyone who uses the data knows the who, what, and where behind it. This will ensure the integrity and trustworthiness of your data across the organization.

5.) Automate Data Governance:

This is where technology can help, and you may already have the tools to get started. Look into how you can automate data lineage in an existing ETL tool—for example, dbt includes a baked-in lineage feature that you can use. It also allows for embedding metric definitions and auditing tests. You can automate back-end monitoring by adding extra coding in your existing data transformation tools. Or you can automate KPIs and scorecards right within your existing BI Platform. There are also specialized data governance tools on the market that can help with front-end business rules automation such as Atlan, Collibra, and Alation just to name a few.

6.) Focus on People and Process:

Technology alone cannot lead to a successful data governance program. People drive its success. Data governance activities are likely already happening within your organization in a less formal or undocumented fashion. Identify the people in the business who are doing this kind of work and designate them as data stewards. Without the right knowledge at the table, data governance efforts will go nowhere. Relieving the pressure on those individuals can only be accomplished by involving them heavily in this effort and then spreading out their knowledge from there.

7.) Don’t Ignore Change Management:

For your data governance program to be successful, you need to drive user adoption and that can only happen if you treat change management as part of the program. Attach the program to business objectives and on-going projects, clearly communicate the changes and how it affects the business users, explain their roles throughout the process, and most importantly, celebrate wins—big or small.

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