Effective Data Governance Program & Strategy: Plus Guides

Data governance maximizes your investment in data and analytics initiatives by promoting the proper use of analytics in business processes, ensuring accurate insights based on quality data, reducing risks with security, and guiding the prioritization of projects so the right information is available at the right time.

But to be successful, you need a data governance strategy that is tailor-fit to your business needs — one that will allow you to achieve quick wins and measurable ROI.

In this blog, we cover:

What is a Data Governance Strategy?

A data governance strategy is a high-level plan that defines and outlines the goals and direction for data governance within an organization — guiding decision-making and resource allocation.

There are three key elements to building a strong data governance strategy:

Utilize a data governance framework
Determine the right level of data governance (enterprise-wide or iterative)
Create an actionable roadmap

Graphic outlining the three key steps to build a "data governance strategy," including the utilization of a modern framework with essential components, determining the organization's data governance maturity level, and implementing a program to translate the strategy into actionable steps.

Steps to craft an effective data governance strategy: From choosing the perfect framework to implementing actionable plans.

Why is a Data Governance Strategy Important?

As businesses create vastly more data than they know how to process — coming in from hundreds of sources — they need a data governance strategy in place that will ensure a consistent approach to the valuation, creation, consumption, and control of data. A lack of strategy can lead to many challenges, including:

Inconsistent use of data across the organization: Different departments interpreting and utilizing data in various ways creates confusion and misalignment.
Poor data quality: Resulting in costly errors, rework, and inefficiencies, poor data quality hampers organizational performance.
Lack of transparency: The absence of a clear view of data assets may lead to misuse and uninformed decisions, hindering understanding and proper utilization of data.
Compliance violations: Improper data classification and storage can lead to non-compliance with regulations, posing legal risks and potential penalties.

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How Does Data Governance Support a Data Strategy?

When properly aligned with your data strategy and business objectives, a data governance strategy can guarantee the alignment of data and analytics initiatives with the objective of driving business value.

When done right, your data strategy will rely heavily on effective data governance because,

It will provide the necessary framework, processes, and guidelines for managing and ensuring the quality, integrity, confidentiality, and availability of data.
It will establish accountability, roles, and responsibilities for data management.
It will help enforce data standards and policies and facilitate compliance with regulatory requirements.

Why a data strategy without data governance is a recipe for failure

How to Build Your Data Governance Strategy

1.) Utilize a Data Governance Framework

A data governance framework serves as a guide that provides structure and direction for your strategy. It encompasses a set of practices and procedures that actively manage your organization’s data assets in a structured manner.

Graphic depicting the five essential components of a modern "data governance framework," including program management to drive initiatives, quality standards for data accuracy, analytics management for proper usage, metadata management for tracking usage, and security measures to protect Personally Identifiable Information (PII).

Discover the five key components that make up a modern data governance framework from program management to data security and privacy.

There are several options of data governance frameworks that can be used to develop a data governance strategy, but any modern framework will include (at a minimum) the following key components:

Data Governance Program Management: This involves establishing a team, defining processes, policies, and procedures, and providing education to support a data governance program. The elements of program management are what drive successful implementation of data governance initiatives.
Data Quality Standards: This critical aspect of data governance focuses on ensuring the accuracy, consistency, and reliability of data. It includes getting all business stakeholders involved to establish quality rules, implementing programs to track and resolve data quality issues, and monitoring data quality continuously.
Data and Analytics Management: This aims to prevent uncontrolled analytics practices and maintain governance over data and analytics activities. This is where you establish guidelines and controls for self-service analytics to ensure proper data usage and avoid “wild west” scenarios.
Metadata Management: This is the understanding and management of data assets, their usage, and associated metadata. This allows you to track and control data assets, identify data lineage, and manage data access and usage within your organization.
Data Security and Privacy: This encompasses regulatory compliance, protection against data breaches, and managing data usage permissions. Address regulations such as GDPR, CCPA, HIPAA, and protect Personally Identifiable Information (PII) while defining appropriate data access and usage policies.

There are many widely available frameworks that can easily be adopted by your organization. The right one for your organization depends on your company’s size, needs, risk profile, urgency, and capabilities.

2.) Determine the “Right” Level of Data Governance

There isn’t a one-size-fits-all approach to data governance. There are several factors that will determine the right level for your organization — one is identifying your current data governance maturity level.

Most organizations fit into one of three categories:

Inactive: Organizations with an inactive approach currently have data governance in place, but it is siloed and fails to connect the dots across the business.
Reactive: Reactive behaviors are in response to issues stemming from poor data quality or perceived data quality issues. Some organizations are dealing with regulatory or compliance issues and are reacting to an audit or new law/regulation.
Proactive: Proactive responses are efforts from businesses that understand the value of data governance and want to enable it within their organization.

There are a multitude of steps you can take to improve your data governance maturity without taking on too much, too quickly.

Other factors to determine an appropriate level of data governance include (but are not limited to):

Your strategic objectives: How does data governance (or the lack of it) drive the success of your overall business objectives?
Pain points associated with data governance: What challenges are you dealing with as it relates to data governance, and how do they limit your growth?
Level of regulatory requirements for your business: Industries like banking, healthcare, and education are ripe for regulatory oversite; compliance may be urgent.

Graphic illustrating the stages of "data governance maturity" within an organization, categorized as chaotic, developing, sponsored, enforced, and optimized.

Choose the right data governance program based on your current maturity level and business needs.

3.) Implement Your Data Governance Program:

What is a Data Governance Program?

A data governance program defines how to implement your data governance strategy. It provides guidelines to translate the policies, procedures, structures, roles, and responsibilities outlined in the data governance strategy into tangible actions.

Guides to Start Building Your Data Governance Program

Since every organization is unique, developing a tailored program that meets the specific needs of your organization is crucial. Whether it is an enterprise-level data governance program or a more practical and proportional approach, the goal is to create a program that is maintainable and aligned with your organization’s requirements.

Download: Guide to Building Enterprise-Level Governance Programs

This guide is intended for organizations with mature data governance practices in place seeking to enable a full-scale enterprise data governance program. Have executive buy-in, a multi-year budget, and a change management strategy? This one’s for you.

Download: Guide for Iterative Data Governance

This guide is intended for organizations that want to take an iterative approach to data governance. Use this guide to build out a program that is practical, maintainable, and proportional to your existing business needs.

Final Tips to Make Your Data Governance Program and Strategy Successful

To ensure the success of your data governance program and strategy, keep the following tips in mind:

1.) Embrace adaptability: Recognize that data governance is an ongoing journey rather than a fixed destination. Stay open to adapting your program as per evolving organizational goals, market dynamics, and data sources. Actively assess and adjust your program to meet changing needs.

2.) Prioritize change management: Drive user adoption by integrating change management into your program. Connect it with business objectives and ongoing projects. Clearly communicate changes and their impact on business users. Define their roles in the process, provide relevant training on data literacy, and celebrate successes, big or small.

3.) Focus on intention, not complexity: Develop a well-defined plan, clearly outlining expectations and objectives. Ensure everyone involved understands and supports the plan. Establishing the right people and processes is crucial for immediate value and long-term success. With this approach, your data governance program will enhance data quality and analytics, benefiting the entire organization.

By following these tips, you can enhance the effectiveness of your data governance program, leading to improved data quality and analytics, and ultimately aligning your business with its objectives.