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, and who is best placed to take the lead.
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 storage solutions are finalized. The most important step to deciding which approach you should take is to determine the kind of organization you represent.
Which Category Does Your Company Fall Into?
When it comes to data governance preparedness, there are two types of organization: mature and fledgling. You can’t begin to build a business case for data governance before you understand which category your company falls into.
The first step is to identify any existing data processes you have in place at your organization. Once you establish this, you can rate your data preparedness. If you are using data for analysis and, in turn, making important business decisions based on these results, then you are likely operating in a mature organization. However, if you don’t use any data warehousing technology and haven’t achieved any data-driven growth, your company is better classified as a fledgling organization.
So, what are the steps to determining which category you fall into?
Mature organizations will already be using several data warehouses along with large data stacks, like Snowflake. They will also have a complex reporting system, such as Qlik, running alongside their data storage facilities.
A fledgling organization will have few, if any, data warehouse systems, but will be committed to launching a data-driven initiative. Fledgling organizations are usually relatively young and reliant on platforms like Salesforce and Enterprise Resource Planning (ERP) software.
Stage 1: Building the Value Driver
The first thing you need to do to build a business case for data governance is to evaluate your existing data initiatives and establish your data goals. At this stage, you must recognize the likely use cases of the data in your organization before committing to an investment.
In a mature organization, you can quantify the role and effectiveness of a data governance strategy by establishing how it could increase the efficiency of your 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 is required to weigh up the potential value of these initiatives first.
So, the next step for a mature organization is to ask whether or not it has hit the targets it set out to. And if it didn’t, it needs to establish why it hasn’t.
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 already running. The most important thing to do is to catalog these established business cases noting whether or not they have achieved their objectives.
Next, you need to hone in on any problems that may have been revealed and create a business case that focuses on tackling them. The number one goal of a mature organization is to determine which existing obstacles, regardless of previous methods, will be remedied with a significant data governance program.
Joining Governance Objectives with Business Goals
To get the most from a data governance program you need to align it with key business goals, like increasing revenue by 50% or reducing operating costs. Once you’ve made this link, you can build a better business case.
In a fledgling organization, the goal is to develop a completely new business case that focuses on the benefits of data analytics and the necessary data governance required to support it. However, in a mature organization, as we mentioned earlier, your role is more retrospective.
New business cases can be built from three core areas. These include revenue generation, operational efficiency, and risk reduction.
In terms of revenue generation, data is utilized in a fashion that enables a business to grow. For example, a marketing team could present a business case for revenue generation because they could use data governance to target campaigns. In turn, the company will realize more conversions and greater profits.
When it comes to 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 tweaked.
For example, an electricity provider could use data to monitor certain internal components. When maintenance is required on these components, there is inevitable downtime, and the company loses money. If maintenance was 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 easily regulated along with other compliance concerns. Ultimately, organizations could avoid the massive fines associated with falling foul of privacy laws, such as the EU’s General Data Protection Regulation (GDPR).
Stage 2: Identifying and Acknowledging Pain Points
Various pain points prohibit data initiatives in a mature organization from realizing their ultimate goals. The trouble is, even if these pain points are known to individual users, they might not be so well documented enterprise wide.
So, the most important part of stage two for mature organizations is for users to find and document the issues and share the benefits of addressing these pain points with their colleagues.
There is a tried and tested method to find these issues in a mature organization, but fledgling organizations will do things slightly differently. First, you need to interview employees from each department that deals with data initiatives, such as data warehousing.
It doesn’t matter who conducts these interviews– you, a data governance officer, or even an external data management consultant– but before you focus in on specific areas, you need to document all of the problems your company faces.
The best way to do this is using a pre-built template, like our Business Case Builder. Using a spreadsheet like this, you can compete for this phase incredibly quickly.
You can distribute the sheet as an email attachment and then collect the answers at the end. 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 you might discover problems with existing procedures, like users unable to access data because they don’t know how to do it.
Instead of looking for problems during the interview process, a fledgling organization will need to focus its efforts on discovering where most progress could be made, and in which department. It’s unlikely that the organization will have a data team in place, so instead, it’s important to approach other staff members.
You don’t need to find any existing problems, instead, you just need to develop a business case based on the three areas we discussed earlier: revenue generation, increased efficiency, and risk reduction.
That said, a spreadsheet will still come in handy if you need to include potential problems into your business cause proposition. To find out how a data initiative will benefit your organization, you need to meet various IT and business leaders, document their goals and potential value of these goals, and quantify the scope of this value.
Stage 3: Develop a Solution and Cost-Benefit Analysis
The final stage in the process is to create an individual solution based on your findings. The basis for this solution will be data literacy, data access management, and data quality improvement.
If you are a mature organization, your core responsibility is to find the right toolset to support existing data analysis processes. A fledgling organization should launch a program that includes analytics alongside governance.
Although data governance differs from data analytics, they work in unison and together constitute a full data program. A 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.
If you commit to starting a comprehensive data governance program, you must have a suite of dedicated software and tools at your disposal. Only with this preplanning can you move ahead with a bespoke solution for your organization.
Follow the steps listed in this article and you’ll likely have a case for data governance prepared. However, it’s important to remember that your strategy must include both governance and analytics. You can’t have either if one is missing.