11 August 2022

Top 9 Data Governance Best Practices

Data Governance Best Practices

With the increasing amount of data generated by enterprises, unless there is order by means of a data governance strategy and tools to execute the framework, there could be total chaos! According to a report by Forbes, IDC has forecasted that by 2025, 175 trillion gigabytes of data will be created globally.

Lack of data governance could lead to challenges in operations, security, decision-making, and several other issues as your organizational data increases. So, while you could be new to data governance or are looking for a starting point to work on your data governance strategy, we share some tried and tested best practices.

At To-Increase, we are a passionate bunch of people, working on resolving customer problems and over the years have built a master data management solution – MDM Studio, a data quality solution – Data Quality Studio, and recently launched a data entry solution – Data Entry Workflow.

We started with Connectivity Studio for data integration and distribution of data over 15 years ago. Connectivity Studio is the base for MDM Studio and offers more functionality for controlling when master data will be released by data stewards. In recent years, we have worked towards strengthening our master data story with MDM Studio and we have spoken to several prospects, customers, and partners regarding their data governance strategies, and data challenges, and gathered their requirements.

Therefore, we share some best practices to inspire you when you decide to work on your data governance strategy. We understand that every organization has different business requirements and that you would have to work on your data governance strategies based on those. Through this blog, we hope to give you a starting point for your data governance framework.

Before we dive into the best practices, let’s look at the meaning of a data governance framework.

What is a data governance framework?

A data governance framework is a set of guidelines and policies around the various data lifecycle stages from creation and storage to publishing. The framework chalks out the data rules, processes, and people involved at each stage of the lifecycle and their delegated functions. This process ensures data compliance and the consistency, accuracy, and quality of the data.

Implementing a data governance framework will impact all your business functions and how you make business decisions. As data governance and data management are hand in glove, the framework will dictate a strategy for all your data management processes.

Once you are ready to move forward toward working on your data governance framework, it is time to look at what other organizations are doing in your industry. Look for industry-specific templates available in the market that can help you understand what other companies in your sector are currently working with. 

Top Data Governance Best Practices

We understand that every organization will need to tailor its data governance framework, so we share some best practices that are relevant across industries that will help you take that first step toward a well-defined data governance framework.  

1) Ask all your stakeholders for their buy-in on a budget

Before you can get started on hiring people or working out a process, you need approval on your plans from the management. Once you have the buy-in from your leadership team, it will be easier to move ahead, and the budget will not be a stumbling block. Also, factor in that you will need a budget for the tools you implement to execute your data governance framework.

To get this buy-in, you need to build a business case and outline the benefits the organization can expect to witness after the data governance framework is drawn out and put into practice. The results will be seen over time, but the organization can expect to make better business decisions that can drive revenue, improve operational efficiency, and ensure happier customers.

Consider sharing a return on investment as that will be a valid expectation from the management. This can include the price of poor data (which is a hidden cost), the licensing costs of tools, resources, and other cost-savings that you can expect. You can share this return on investment and project an estimated before and after scenario for clarity.

2) Assemble the right team

Although ‘data governance involves the strategy, the people, the tools, and processes involved in data management,’ without the right people, the other pillars of data governance are redundant.

Therefore, the first crucial step is to assemble the perfect team that includes people from the IT team along with data owners from other business functions. If you do not have visibility into each business function, it will be difficult to have data rules and guidelines drafted out. If you do not have resources and decide to outsource or hire new talent and expect them to work on a strategy, there could be a big gap in expectations versus what is delivered. New hires might have experience with MDM, but every organization has its own processes, culture, and different challenges.

New team members or external members will not have had any experience working within business functions and will have no idea about the challenges your teams face on a daily basis. Therefore, it is important for an organization to own the processes, the people, and the tools and ensure that the strategy is based on knowledge gathered in-house.

It would be wise to hire or look for a team prior to working on any processes but ensure that your data governance team has a mix of experienced and new members so that they understand the challenges and business processes. Once you have the data governance team working on a charter to build the policies, then you could move on to building processes to ensure data governance is followed across the organization.

3) Keep processes as simple as possible

Once you have the right team to move forward, it is time to work on the processes. However, it is important to ensure you make the processes look simple and have everything outlined clearly for every data stage and every business function.

Although the technology specialist will be governing and overseeing all the data, organizations should ensure that the software they bring onboard is user-friendly and easy to implement. Also, the content available should be relevant to the business function only. A software solution like Data Entry Workflow, for example, ensures that people from multiple disciplines can create or change data on a workflow step that shows only relevant fields for their business area. This way you can have multiple people across business functions collaborating to deliver a single master data record.

It is important for systems to be easy to manage and govern. For example, a platform such as MDM Studio can help you maintain a single source of truth and there will be no need to manually copy data from the ERP or business systems and you can distribute data to other legal entities to simplify processes.

4) Set up checkpoints to stay on course

Setting up checkpoints and keeping an eye on the metrics will help you stay on course and move toward your goal. And it is important to set up a channel of communication to ensure your strategy can be executed effectively and any stumbling blocks can be flagged and then resolved along the way.

Ensure that you have a baseline of these metrics before you start introducing any data policies. Keep track of these metrics at regular intervals to gauge improvements periodically. And if you think you’ve taken a misstep or gone off-course, the numbers will raise an alarm. You can then step back, analyze what is not working, and then make amends.

This will help you justify your business case when it is time to explain to the leadership what is working and what has been improved. Also, if you need to compare these to projected numbers, this will give you an inkling of whether you’ve over or under-projected the results at the start. There will be some metrics you might not be able to measure, but as an organization, these changes will trickle down to every business function.

5) Segregate duties

While you set data policies and processes, you will also have to ensure limited editing and viewing access to certain data sets to only relevant business functions. However, this might have to change over time based on how it impacts the organization. For example, our newly launched Data Entry Workflow tool can give specific data owners access to fill in data sets meant for their eyes only.

Some of the data owners will be responsible for working on processes, while some could be responsible for overseeing how things are going, while others could be responsible for measuring the progress and reporting back to the management.

6) Be flexible and open to change

Keeping an open mindset is important and you cannot continue working using the old processes. So, this has to be communicated across business functions for the data governance strategy to be successful. The change management will not stop after you implement your data strategy. As your data grows or business evolves, you will have to make changes and might needs more tools, or different rules to manage your data.

For example, let’s look at a retail company that is used to transacting in a B2C model and decides to expand and trade with B2B as well. They have to change some data quality rules regarding B2B customers as they will not work with advanced payments. In this case, you might need to set new rules for a maximum credit limit.

7) Identify people and assign roles and responsibilities

Data governance is about the processes, the policies, and the people. After you have the first two in place, it is important to assign roles and responsibilities across departments. To define data rules, for example, you need to understand all the data requirements for each business function. So, besides the IT team, it is important to include people from several business functions.

Next, the data governance strategy team needs to identify what are the levels of ownership, who will be the data owners, data managers, and data stewards, and you will also need an overarching committee (a data governance council) supervising that process. The various people involved need to work together as a team and communicate so that the data governance council can understand if certain data processes or policies need to be updated or removed.

8) Set up a monitoring system

It is easy to blame tools when things go wrong, but the right thing to do is to analyze processes, policies, and the people involved when certain benchmarks are not met. Besides adding rules and policies, it is also important to regularly conduct data audits to ensure consistency, accuracy, and validity to ensure you’re not making any data quality mistakes.

Add definitions and clarifications for each business function for data entry, data use and data transfer, and individual instructions for data owners, data stewards, and data managers so that their roles are defined.

9) Lastly, data governance takes time

Data governance is a practice. It takes time to assemble a team, work on processes for the organization, assign roles to the people involved, and then you need to track improvements in data and see what is working and might have to start over. It’s a long-term, continuous process. Keeping this in mind you need to set realistic goals for your organization in the short and long term.

Since data governance is a massive undertaking, you can start with small steps and keep adding processes, and tools and make changes based on the organization’s data, metrics you track, and feedback you receive.

Is your organization ready to work on a customized data governance strategy?

Every organization will have to tailor its data governance strategy depending on the types of data they have to process day-to-day. Whatever your data strategy, our recommendation would be to start small, build on your data strategy policies and have your roles assigned with definitions in place and then look for tools that fit into your data strategy.

Once you are ready to take that step, you can assess whether our data governance solutions for Microsoft Dynamics 365 fit your data governance strategy. Our solutions for master data management, application integration, data quality management, and data entry can help you synchronize data across systems, manage data quality, improve data entry inputs, and manage master data distribution from a central or decentral system. If you would like to know more about these solutions, do download our MDM Suite factsheet from the link below.

Start your data transformation journey with our Data Governance solutions

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André Arnaud de Calavon,
André Arnaud de Calavon,
Product Manager

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