The optimal approach to starting a data governance program should include building a strong business case to identify the value and benefits of a data governance initiative
When it comes to executing a data governance strategy, there is no standard approach. Of course, there are common methods and tools, but it’s up to each company to decide how best to implement data governance initiatives to achieve the optimum business value, based on business requirements and challenges.
Some business leaders will prefer to go all in and implement governance initiatives in every department. Others will take a more measured approach, slowly introducing programs as staff become more data literate and data management solutions are finalized. The most important step to deciding which approach an organization should take is to determine the business case for data governance.
Discovering an Organization’s Maturity
When it comes to data governance readiness, there are two types of organization: mature and fledgling. Building a business case for data governance cannot start before understanding the organization’s current state of maturity.
To discover the organization’s data maturity, start with identifying any existing data processes. Then, assess how data is used. If the organization uses data for analysis and making important business decisions based on these results, this is a mature organization.
However, if there is no data warehousing / business intelligence technology and the organization has not achieved any data-driven growth, it is likely a fledgling organization.
So, how does an organization determine its data management maturity level?
Mature organizations will use one or more data warehouses and large data stacks. Often, they will have a complex reporting system, with business intelligence capabilities.
A fledgling organization may not have a data warehouse system or use business intelligence / analytics but may be committed to launching a data-driven initiative.
Stage One: Building the Value Driver for Data Governance
The first step to build a business case for data governance is to evaluate all existing data initiatives and establish the organization’s data goals. At this stage, recognize the likely use cases of the data in the organization before committing to an investment. How effectively is data used now? Does it contribute to decision-making, or is it only used for operations?
A mature organization can quantify the role and effectiveness of a data governance strategy by establishing how it could increase the data-related efficiency of existing data initiatives.
A mature organization will have formulated a business case before rolling out a series of data actions, like data warehousing, while a fledgling organization must evaluate the potential value of these initiatives first.
The next step for a mature organization is to ask whether it has hit the defined targets, and if it did not it needs to establish the reasons for missing the goals.
Some users operating in a mature organization will find it challenging to develop a comprehensive business case for data governance because so many data-focused processes are running. The most important thing for mature organizations, is to catalog all established business cases noting whether they have achieved their objectives. Fledgling organizations should identify the possible business cases based on current state and desired goals for data-related processes.
Next, identify any problems that may have been revealed and create a business case that focuses on tackling them. Determine which existing obstacles, regardless of previous methods, will be corrected with a significant data governance program.
Joining Data Governance Objectives with Business Goals
To get the most from a data governance program, align it with key business goals, like increasing revenue by 50% or reducing operating costs. Having made this link, it is easier to build a better business case for either a mature or fledgling organization.
Business cases can be built from three core areas. These include revenue generation, operational efficiency, and risk reduction.
In revenue generation, data is utilized to enable business growth. For example, a marketing team could present a business case for revenue generation because they could use data governance to target campaigns with more accurate data. The company will realize more conversions and greater profits from using accurate data based on organizational data policies.
For improving a company’s operational efficiency, data can play a very important role. Through data governance, analysts can quickly see where savings could be made, and processes could be enhanced.
For example, an electricity provider could use data to monitor certain internal components. Downtime is required for maintenance on these components, and the company loses money. If maintenance were highly efficient and based on exact data points surrounding the overall health of the machines, these periods of downtime could decrease.
Finally, businesses can use data governance to reduce the risk of violating data privacy laws. Access to PII and confidential information can be regulated along with other compliance concerns with an effective enterprise data governance program. Ultimately, organizations could avoid the massive fines associated with data privacy laws, such as the EU’s General Data Protection Regulation (GDPR).
Stage Two: Identifying and Acknowledging Pain Points
Various pain points prohibit data initiatives in a mature organization from realizing their ultimate goals. Even if these pain points are known to individual users, they might not be well documented across the enterprise. Fledgling organizations will have data-related issues as well, but they may not be recognized easily.
So, stage two for all organizations is for users to find and document the issues and share the benefits of addressing these pain points through a data governance program with their colleagues.
First, interview employees from each department that deals with data, including all business departments and relevant staff from Information Technology. Document all the problems and issues (and successes) discovered by the interviews.
There are a variety of business case / requirements formats available as templates. Using one can reduce the time and difficulty of managing the interview process and the results. Regular problems include business-oriented issues like an incomplete data-driven growth strategy. There might be technical problems like system lag when large queries are requested. Or problems with existing procedures could be identified, like users unable to access data because they don’t know its location or its definition.
After documenting problems during the interview process, an organization should focus its efforts on discovering where most progress could be made, by department or other criteria. Many companies do not have a data management team, so enlisting help from outside is common.
The interview template can support including potential problems into the business case for data governance. To discover the value of a data governance program, meet various IT and business leaders, document their goals and potential value of these goals, and quantify the scope of the current state and the value of improvements.
Stage Three: Develop a Solution and Cost-Benefit Analysis
The final stage in the process is to create an individual solution based on the findings. The basis for this solution may include improvements to data literacy, data access management, and data quality improvement.
A mature organization has a core responsibility to find the right approach to support existing data analysis processes. A fledgling organization should launch a program that includes analytics after starting data governance.
Data governance and data analytics are aligned and constitute parts of an enterprise data management program. A full data governance solution usually involves building and implementing a scalable set of data tools, defining roles and responsibilities, classifying data, defining data access policies, defining steps for increased data literacy, standardizing terms, and improving data quality and trustworthiness.
A comprehensive data governance program requires a suite of dedicated software and tools to support the effort. Each organization’s choice of tools will differ, but all should be based on business requirements and the technology platform.
Conclusion
A solid business case is an essential part of starting a data governance program. Remember that to be successful, a data governance strategy must include other areas of enterprise data management, such as analytics and data privacy.
