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Summary: The key steps to building a business case for data governance are:
The builder includes a list of critical questions for reference along with potential solutions. This comprehensive document provides a definitive structure for you to follow.
There is no “one-size-fits-all” model for data governance. Nor is there a standardized initiation process. It is up to each enterprise to make a qualified business decision as to how a data governance strategy is rolled out and by whom.
While some companies may decide to commit to an enterprise-wide program, others may prefer to implement changes department by department. However, before you begin, you must understand the type of organization you represent. Further down the line, critical implementation steps are dependent on this factor.
Although the scope of data governance ranges from application integrations to analytics, the maximum value of data governance is in analytics. That’s why in this blog, we’ll be focusing on analytical use cases.
Before you start planning a data governance strategy, you need to identify your organization’s existing data initiatives. In general, a company's data preparedness comes under two categories, mature—in the field of data analytics—or fledgling.
A mature organization will already utilize its data for analysis and turn these insights into progressive business decisions. On the other hand, a fledgling organization will have limited warehousing facilities and may have yet to start a period of focused data-driven growth.
Here’s how to identify whether your organization is mature or fledgling:
The first step to building a business case is to understand the value of the data initiatives you have or plan to have. It’s not worth investing in a massive data platform if you don’t know the value of potential use cases.
In mature organizations, there are already various data initiatives in place. So, to understand the importance of data governance, you simply need to determine how a data governance program could help speed up or improve the efficiency of these initiatives. On the other hand, in a fledgling organization, you must determine the potential value of these initiatives first.
Mature organizations will usually have built a business case before implementing a significant web of data lakes and data warehouses. A mature organization will ask itself whether it has achieved the objectives it set out to achieve, and if not, why not.
In a mature organization, it can be difficult to build a definitive business case because there already exist many initiatives. The main objective is to create an inventory of these business cases and their objectives and record their successes and/or failures. The next step would be to focus on the problems you’ve identified. If any initiatives are inefficient you should focus on how to improve them. Often, data initiatives are interlinked, but many people within an organization are unaware of these connections.
The primary aim of a mature organization is to establish existing problems that would be solved through a data governance program and create a new business case from this research.
When you align data governance program objectives with business goals it receives maximum traction within an organization. The following are examples of business goals and objectives:
With a fledgling organization, the aim is to build a brand new business case for data analytics and the data governance processes required to support it. In a mature organization, the business case is based on investigating and documenting existing practices, while fledgling organizations are required to start from scratch.
So, how is it done? There are three key areas that a new business case can be built from.
The first is revenue generation. An organization’s data can’t itself grow a business, but the clever use of this data can. In healthcare, banking, technology, retail, and many other industries, there is huge potential to use data to boost the top line.
Using the healthcare industry as an example, they will receive a patient code from a doctor and then use it to bill the patient. If they can verify the legitimacy of the code, there will be less of a chance that a patient would appeal the fee and more of a likelihood that the claim will be accepted the first time. Higher approval rates will encourage more business from prospective clients.
In another example, the business case for revenue generation could be made for a retail company using data to increase profits through a targeted marketing campaign. By aiming particular products at particular customers, retail businesses can realize greater profits.
Data can be equally important for improving the operational efficiency of a company. Essentially, this improved efficiency will lead to a reduction in costs. This business case is often adopted by utility companies and organizations involved with banking and financial services.
To increase operational efficiency, you need to recognize the current state of operations within your organization and then streamline the process, perhaps through automation. To do this, you need to initiate Key Performance Indicators (KPIs) through a data warehouse.
There are many examples of how operational efficiency could be improved through data governance, but let’s focus on utilities. Let’s say an electricity provider is undergoing regular monthly maintenance based on the prescription provided by the manufacturer of its components. However, it could be the case that maintenance sessions are too frequent. This leads to both greater costs and more regular downtime.
By optimizing the maintenance process through data analysis, you will not only save money but you will also have far fewer periods of downtime. Based on information from sensors that monitor the company’s equipment, bi-monthly maintenance tasks could be completed quarterly instead.
The third business case is risk reduction. This is usually focused on compliance issues, like adhering to the EU’s General Data Protection Regulation (GDPR). Even if a company is aware of the responsibilities they have, a data governance program can enable them to reduce the risk of unknowingly breaking compliance laws.
As a practical example, this risk reduction strategy could involve a company limiting access to certain data sets to protect PII.
In a mature organization, various pain points exist. These pain points stop data initiatives from achieving their full potential. Although pain points are well known to individuals, they are usually not understood at a company-wide level. The main objective of this step is to document existing pain points and identify the potential benefits of addressing them.
To identify prominent issues, mature organizations are required to follow a particular methodology—fledgling organizations will use slightly different methods. The best way to discover these issues is to interview staff from each data-focused department, such as data warehousing, development, and implementation projects. These interviews can be conducted by you, a data governance officer, or a champion of data governance. You can also employ a data management consultant for the task.
You’ll need to make a list of all the problems currently affecting your organization. A pre-built template is the best resource to determine this information and will help in the interview process. With a spreadsheet like this, the time it takes to reveal the information you need is slashed because there is no requirement for brainstorming. Interviewers can simply distribute the sheet and check which problems arise.
To simplify the interview process, download this free Business Case Builder which includes a list of critical questions and potential solutions. You can use it to identify common pain points, match these pain points with OvalEdge solutions, and then build a business case around these solutions.
As with the methodology for a mature organization, a fledgling organization would need to interview certain teams to understand where the most gains could be made. Because a fledgling organization is unlikely to have a major presence in regards to a data team, other members of the organization will need to be approached.
Unlike a mature organization, a business case is enough to ratify the investment in a data governance platform for the company. There is no need to identify existing problems with current data analytics practices as there are unlikely to be many, if any, in operation
Under these circumstances, the OvalEdge spreadsheet can still be a useful tool because a business can see what issues might arise if no data governance initiative is initiated.
The next step is to develop a bespoke solution based on the outcome of your research. The solution will focus on a data literacy program, data quality improvement program, data access management, and other advanced governance tools.
With a mature organization, the primary aim is to select an arsenal of tools that support the existing data analysis processes, while a fledgling organization is better served by a governance program that introduces analytics alongside governance. Data governance is different from data analysis, but you can’t have one without the other. They are the core components of any data strategy.
By implementing the above processes, you will be able to remove any inefficiencies from the data value creation process. This will give you a complete cost-benefit analysis of the upcoming data governance program.
A solution like the above can’t be implemented with fragmented tools and techniques. When you start a comprehensive data governance program, you need a suite of software and tools like OvalEdge.
OvalEdge offers a progressive approach to data governance. Regardless of whether you are a mature or fledgling organization, we enable you to implement data governance strategies at your own pace. Far from the traditional, “one-size-fits-all” approach, we give organizations the opportunity to scale up their data governance at their own pace. This creates more opportunities for success because companies aren’t overwhelmed by the technology and can align a bespoke solution with their budget.
By the time you have considered the steps we mention in this blog, you should have a very sturdy case for the ROI of a data governance program. The most important thing to remember is that your data strategy should always be a combined offering of analytics and governance, fail to do this and your strategy is very unlikely to succeed.
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