How to Build a Business Case for Data Governance Tools
How to go from “Maybe next year…” to “Why don’t we already do it?”
In this blog, I will highlight some key elements in creating a business case that gets the job done. With a well-built business case, you are better equipped to sell the value of data governance initiatives to executives and stakeholders.
Building a business case for a business glossary, data catalog, data lineage tool, or data quality tool involves identifying the specific problem or opportunity that the data governance effort will address and then quantifying the potential financial and operational benefits of implementing the solution.
Executive-level managers usually focus on innovation, revenue growth, competitive advantage, or defensive factors like cost control, return on investment, risk reduction, and regulatory compliance. That being said, any investment that accelerates the digital transformation agenda gets their attention too. You can get insights into business drivers for establishing a data-driven culture in our infographic.
Steps to help you build a business case
1. Identify the problem or opportunity
Determine the specific pain points your organization is facing with its data. Such challenges may include one or more of these:
Data silos, keeping different systems synchronized
Disparate data and lack of consolidation
Data duplication and queries or the creation of the same reports by different teams
Underutilized data
Incorrect data resulting in the lack of trust in data
Lack of data discovery and access
Security challenges
Regulatory non-compliance
Organizations are addressing these pain points with the help of metadata management tools or data quality tools. Our 5 Costly Business Problems Easily Fixable with a Business Glossary and Data Catalog article will guide you on identifying space for improvement related to metadata management in your organization. However, fixing your data is not only about buying a tool but also requires a mindset change, which we explain in our data quality monitoring article.
2. Quantify the costs of the problem
Estimate the financial and operational costs associated with the problem, such as lost revenue or increased expenses due to data silos or poor data governance.
Start by outlining the costs of poor data governance, such as lost revenue, wasted time and resources, and decreased customer satisfaction.
Provide specific examples and quantify the costs as much as possible.
An example can be time spent creating a report by one team when another department already uses a similar report. Or the impact on sales or marketing efficiency when reaching out to the wrong contacts. Or time spent on data finding and data cleansing by your costly data professionals.
To calculate the costs of “bad data”, you can use the COPQ formula:
COPQ = (total waste (materials) + defects (variation occurrence)) × inefficiencies (time spent fixing)
As a data project owner, it is important to consider the period you are evaluating, as this will help you narrow the scope. To determine your company's cost of poor data quality, you will need to add up the total waste and variation and then multiply that by the amount of time spent fixing the related issues. This will give you a clear picture of how much poor-quality data is costing your organization. By understanding and addressing these costs, you can help your company improve its processes and ultimately provide better value to your customers.
3. Evaluate the potential benefits of the solution
Determine the potential benefits of implementing a business glossary, data catalog, data lineage tools, and/or a data quality tool, such as improved data discovery and access, better data governance, and improved data-driven decision-making.
When listing the benefits, try thinking outside the box and going beyond the benefits just for your data team or project. For example, what will be the impact on domains/data consumers? Try to discuss with your peers from different domains of the business.
Benefits brought by your data initiative might be critical to business profitability. Like ZDF Studios, where improved metadata management helped ZDF Studios to meet the need of their digital supply chain and enabled them to monetize their movie and TV series content more efficiently.
4. Calculate the return on investment (ROI)
Determine the solution's potential ROI by comparing the cost of the problem to the benefits of the solution. The benefits might include the following:
increased revenue
reduced costs
improved decision-making
Provide specific examples and quantify the potential benefits as much as possible.
Regarding the tool's cost, Accurity provides scalable and transparent pricing, which enables you to customize your solution based on your data governance initiative. Contact us for guidance on a solution or subscription plan.
5. Provide a clear and detailed plan
Identify any potential risks associated with implementing the data governance initiative and develop mitigation plans to address them.
Include specific steps you will take, the costs involved, and the timeline for implementation. We also offer proof-of-concept packages that will allow you to test while building your business case internally.
Be sure to include information on how the initiative will be managed and how progress will be measured.
6. Show how data governance aligns with the company's objectives
A company's main objectives are usually to increase sales, save expenses, boost productivity, digitize operations, etc. These goals have one thing in common: they cannot be realized without reliable data.
Show and explain how using a business glossary, data catalog, data lineage tools, or a data quality tool aligns with the company's overall goals and strategies and how they will contribute to the bottom line.
The question to be answered is not if data should be managed but rather how to do it proactively and prevent problems before they occur.
7. Get support from other stakeholders
To increase the chances of success, try to get support from other stakeholders, such as department heads (both business and IT), who have a vested interest in the success of the data governance initiative. They can help you build a case for the initiative and provide valuable feedback on the plan.
For instance, how they can benefit from having aligned business terms. Just check with the VP of Sales how much time they spent last year arguing with their marketing counterpart what a “lead” is or with the customer success team about what “active partner” means. And why they have different figures in their team-specific reports for the same thing. You will get many ideas of benefits the solution can bring to data harmonization and your overall data governance initiative.
Try to dig deep enough to identify the root causes of their data problems. In case you are unfamiliar with it, I strongly suggest an iterative interrogative technique called “Five whys” originally developed by Sakichi Toyoda and extensively used at Toyota Motor Corporation.
It is used to explore the cause-and-effect relationships underlying a particular problem. The primary goal of the technique is to determine the root cause of a problem by repeating the question "Why?" five times. The answer to the fifth why should reveal the root cause of the problem.
8. Communicate the business case effectively
Communicate the business case clearly and concisely, highlighting the key points and addressing any potential objections to the implementation of the data governance initiative.
Avoid generic arguments, such as optimizing the value of data, and be specific instead. Once you have gathered real data problems from stakeholders, use them to create tangible use cases.
Your executives may have questions or concerns about the initiative, so be prepared to address them. Be ready to answer any questions they may have about the costs, benefits, and risks associated with the initiative.
A template will help you to present your business case.
Summary
By highlighting the costs of poor data governance, showing the potential return on investment, and providing a clear and detailed plan, you can help your executives understand the importance of data governance and see the value of investing in a data governance initiative and relevant tools.
Last, but very likely the most important tip
Remember whom you are focusing on and avoid language that is only understood by data specialists. People tend not to support anything that sounds complicated.
Instead, try to tap into the emotional and logical parts of the brain. Use simple business language that is understandable and relatable to all.
For those of you who are not in your comfort zone when selling new ways of data governance and persuading budget owners, a friendly reminder: we are here for you. On top of us understanding anything data very well, we are happy to assist you in getting buy-in for data governance initiatives from stakeholders.
Suppose you would like to understand better how our Accurity data intelligence platform can help your data governance initiative. In that case, we offer a free demo of Accurity with our product experts.
