Data Governance | What It Is & Why It Matters
Why data governance matters
Data drives today’s business. The most successful companies harness the power of their data. They use it to better understand customer behaviour, spot market trends and take advantage of new opportunities more quickly than the competition.
You know this, and you’re likely working hard to understand the value of your data. You want to use it to fuel growth and improve performance at your company. To succeed, you need to foster trust in your data, create a data-friendly company culture, and implement practices that support data-driven decision making.
The key to doing all these things is data governance.
Whether you’re a data professional or a business leader, you should be focused on data governance and excited by its benefits for your business.
Data governance is a key enabler of digital transformation and an essential tool of both data and business leaders at successful companies around the world.
To help you better understand this important capability, we’ll use this page to answer basic questions around what is data management and why it matters for your business. We’ll also explain the key benefits and guiding principles of data governance.
What is data governance?
Data governance promotes an environment of trust, transparency, and ownership of your data. It establishes the conditions needed for data to successfully support your business needs and add value to your company.
Good data governance provides the structure you need to execute your data asset management strategy and extract maximum value from your data assets. It helps build a culture that values data and promotes investment in data assets — all with the goal of supporting stronger data and business performance, decreasing risk, and enabling increased value.
From a practical perspective, data governance includes defining and overseeing the roles, responsibilities, and processes for collecting, storing, processing, maintaining, sharing, accessing, using, and disposing of data.
Data governance helps define and assign the roles, accountability, and actions needed to keep your data assets in tip-top condition. It helps you understand the business use cases and outcomes supported by different data assets. Plus, good data governance provides the assurance you need that your business-critical data assets are fit for purpose and being looked after properly.
Data governance captures the requirements and specifications you need to get the most out of your data. Equally important, it defines standards to make sure your data is accessible and is captured and used ethically, safely, and in compliance with necessary regulations and legislation.
But data governance is not just about roles and responsibilities. It helps you define the rules of the game and set up the processes and procedures you need to support your data goals. Data governance makes sure you know the data you need is accessible and ready to provide value in the way the business needs. It’s also a helpful tool for treating your data as an asset and managing it throughout its lifecycle.
Goals & benefits of data governance
Data governance involves setting up policies and processes, but that’s far from the main goal. Effective data governance is key to getting the most from your data assets and ensuring the success of your digital programs.
It helps provide a clear line of sight between your data assets and the business outcomes they support. This important contribution helps build a data-driven culture in your organisation.
Data governance seeks to deliver improved business performance by optimizing how your data is treated. Part of that is fostering ownership and a sense of appreciation for data’s value throughout your organization. Data governance aims to shift the view of data from an IT concern to a source of value for the entire business.
Data governance efforts are most successful when they’re built on consensus and teamwork between data and business professionals. It helps both sides answer the ‘what’s in it for me?’ question and get on board with investments to improve your data.
What about data governance benefits? Is it really worth all the work of getting business buy-in and establishing a detailed system for managing your data?
Yes. There’s no question about it — good data governance is a challenge, but the associated benefits are more than worth the effort.
Data governance fosters an environment of trust and transparency by setting clear expectations and responsibilities for every part of the data life cycle and for all data use cases. When properly implemented, this leads to a stable and high performing asset ready to power better business results.
The level of business risk associated with poor data decreases. At the same time, the level of value and number of opportunities to the business increases. Continued investment in data becomes a no-brainer for data professionals and business leaders.
In other words, data governance is a game-changer!
A deeper look: Why adopting a data governance framework matters
We’ve briefly covered a few data governance benefits, but it’s worth taking a deeper look at why adopting a data governance framework should be high on your list of digital transformation priorities.
Here are four reasons why implementing a data governance framework matters for you.
1. Data governance establishes trust.
In the world of data, trust is everything. As a data professional, you know that data has tremendous value as an enabler of business growth and performance, but only if people trust the data enough to use it to make better decisions.
A data governance framework helps establish the trust you need by introducing transparency and standards into every step of the data life cycle. When people know where your data comes from, how it has been handled, who is responsible for it, and the business uses case it supports, they’re much more likely to trust it. They’ll also look after it and value it enough to support future investment in data and information projects.
2. Data governance gives structure to your strategy.
The transition from strategy to delivery is often difficult, and many good strategies fail due to poor execution. As they say, culture eats strategy for breakfast. You can avoid this pitfall with data governance that supports and gives structure to your strategy.
Good data governance ensures you understand which data assets are business-critical and who is accountable for making sure they support business use cases and goals. It also plays a role in assessing whether your data is fit for its intended business purposes.
This helps you see what’s needed to deliver better business value and improve performance. You can also clearly communicate the value proposition to your stakeholders, making it easier for you to get key decision-makers on board with investments in data.
3. Data governance enables performance tracking.
Once you’ve defined your data asset management plans, it’s important to be able to track whether or not you’re delivering value as expected. Data governance gives you the tools you need to track the performance of your data.
With a strong framework in place, you can track how, when, and by whom data is used and measure the impact on the business. You can compare actual data quality vs. the standards you set and track progress against your plans. This ability to provide visibility and insight about performance builds confidence and trust that your data program is delivering sustainable value to your business.
4. Data governance is a key tool for root-cause analysis and corrective interventions.
Unlocking and delivering value from your data is almost always an iterative process requiring multiple readjustments and course corrections. When something’s not working, your data governance capability helps you perform in-depth root-cause analysis to discover where the problem is and why you’re not getting the value or outcomes you expect. This allows you to quickly identify what needs improving and intervene to improve performance.
Data governance principles
Now that we’ve covered the goals and benefits of data governance, we’ll turn our attention to some foundational data governance principles. All strong data governance programs are built on these five principles.
1. Ownership & Accountability
As the American economist Milton Friedman once said, “When everybody owns something, nobody owns it, and nobody has a distinct interest in maintaining or improving its condition.”
This principle is certainly true regarding your company’s data, which is why all good data governance assigns clear ownership & accountability for both data and data management tasks throughout the data asset life cycle.
2. Clear Standards & Requirements
Effective data governance relies on clear standards and requirements for data collection, verification, processing, storage, retention, access, sharing, security, distribution, use, quality, and disposal.
3. Stewardship & Responsibility
Maintaining good data stewardship ensures that everyone from end-users to technical data professionals understands and fulfils their responsibilities to properly use and maintain your company’s valuable data assets.
4. Data Condition & Remediation
Data condition is related to data quality and refers to whether data is fit for a particular purpose. Data governance helps define the required data condition for different types of data by capturing the business use cases for a data asset. If the data condition doesn’t meet the needs of the business, data governance outlines steps for improvement and remediation.
5. Transparency & Ethical Use
Good data governance requires transparency around all data sources, processes, and use cases. This makes sure the data is always collected with proper consent and used ethically and in compliance with both internal best practices and relevant regulations and legislation.
Data governance vs. data management
Our discussion of data governance would be incomplete without addressing the issue of data governance vs. data management. Contrary to popular misunderstanding, they are not the same thing. Instead, they’re separate but equally important parts of the same puzzle.
As we’ve discussed, data governance focuses on the structure and processes for how data is accessed, used, and handled to best support the needs of the business and maintain compliance with legal and ethical regulations.
Data management involves the technical work needed to store, maintain, secure, and deliver data in a way that supports your business goals and your data governance framework.
Strong data governance and good data management are both essential components of your complete data asset management approach, with data governance providing the processes and structure you need and data management ensuring you have a strong technical foundation to begin maximizing the value of your data.
Moving forward - From data governance to data asset management
As a data leader, data governance is one of your most helpful and important tools, but good governance by itself can’t help you fully understand the strategic and monetary value of your data and deliver big benefits to your company.
To do that, it’s necessary to fully embrace the data asset management approach and develop a big-picture strategy for evaluating and investing in your data. Here at Anmut, we’ve seen the power of data asset management to deliver massive value and enable impressive gains in performance for companies around the world and across industries.
We’re eager to help your company see the same results. Contact us today to find out more about what we offer and how we can help you.
See More Resources
A Guide To Data Valuation
A Guide To Enterprise Data Valuation
A Guide To Data Strategy
A Guide To Data Asset Management
A Guide To Data Management
A Guide To Digital Transformation
A Guide To Data Culture
A Guide To Data Monetization
A Guide To Data Condition
A Guide To Data Quality
Read More Insights
The key elements of a data driven culture
The role of data in enterprise digital transformation initiatives
Why managing data as an asset is inevitable
Why you should be treating data as an asset
Different data valuation methodologies
Data quality vs data condition: the power of context
5 Reasons why a Chief Data Officer fails
Climbing the data maturity ladder