Best Practices in MDM

There are six best practices to keep in mind for an MDM initiative to be successful.

Align with company goals

Start with aligning the MDM goals with the company goals, versus implementing MDM for the sake of it. Investing all this money in order to implement MDM and change the infrastructure, the way people handle data within the company, it's not going to be successful if your MDM goals are not aligned with the company goals. The only way your executive leadership team will see MDM as a successful initiative is if your MDM is correlated with the company goals in order to ensure that the MDM initiative and implementation is directly contributing to the business outcomes.

Before you think about implementing an MDM initiative, have a look at the company mission and goals. Where does the company want to go in a few years? What's going to be the biggest focus of the company? Next, start thinking of how an initiative can help the company with these goals. Then you're going to have much easier time explaining to the leadership team why they need improvements and how it's going to help them.

Simplicity & Scalability

A lot of companies make an MDM solution too complex and not easy to scale. Ideally, you should have someone on the team that's a real expert in the MDM solution, because they will know every single feature and how to use them.

When you are implementing an MDM solution, you should think about how easy is this solution going to be for the team to use. Can you adapt the solution if we see major business changes in the future. For instance, we might have new data sources coming in. Can we handle the new data sources with the MDM solution that we are going to roll out? Can we adapt fast or are we putting ourselves in the corner by implementing this specific MDM solution?

A simplistic solution is intuitive and user friendly. A simple solution will mean that you will have better user adoption and you're going to reduce the learning curve. When you're in discussion with the team on what solution to go with, think about whether this solution is going to be easy enough for everyone to use. You need to understand whether this solution is going to be fit for purpose for most people in the company.

Finally, is the solution scalable? If we saw increased master data volumes or we need to onboard additional data sources, can we do that easily?

Data Governance

Data governance really is essential for maintaining the data quality and consistency. It helps you to ensure the data is accurate, complete and up to date. It also helps with establishing common data definitions across the different systems where the data is coming from, business rules and data validation processes.

Data privacy and security are equally important with the advent of GDPR in 2018. 83(4) GDPR, which stands for General Data Protection Regulation, sets forth fines of up to 10 million euros, or, in the case of an undertaking, up to 2% of its entire global turnover of the preceding fiscal year, whichever is higher.

The US relies on a "combination of legislation, regulation and self-regulation" rather than government intervention alone. There are approximately 20 industry- or sector-specific federal laws, and more than 100 privacy laws at the state level. In California, the California Consumer Privacy Act of 2018 (CCPA) gives consumers control over the personal information that businesses collect about them. If your company does not comply with these data privacy regulations, your company can be in big trouble. Some of the biggest names in the industry, Amazon and Meta/Facebook, have encountered fines in the billions of dollars. Data privacy and security is not only about the fines, it is also impact the company's brand and reputation.

Data governance is about having better, more accurate data that's up to date, which you can use in order to make better business decisions. It is about protecting data about your employees, partners, and customers.

Think about it as bringing data under control and keeping it secure and consistent.

You should spend a good amount of time thinking about how you're going to govern the data that you're going to feed into your MDM solution. Make sure that you involved your data governance manager along the process of the MDM. The data architect and any other data experts within the company must be active participants in the sessions where you define how the data governance is going to look.

Business Involvement

Business stakeholders should be involved in any MDM initiative that you are undertaking within your company. There are two main reasons why should involve business stakeholders.

1. The business stakeholders are actually the experts in the domain that they are leading. If you're going to have business stakeholders from merchandising, ecommerce, marketing, operations, supply chain or HR, these different business stakeholders will know their data inside out. Involving these business areas means that you have the necessary information in order to make the right decisions on how to proceed with specific features around MDM. It will also mean that you are actually solving the needs of the business. You need the business stakeholders for their knowledge in order to make the right decisions for the company.
2. Once you involve business stakeholders, they're going to feel more engaged and they're going to feel as part of the initiative. This will translate into easier user adoption of the new solution. If you think about any major change that was done over the last few years at your company, there was likely pushback from different teams. The main reason why you have this pushback is that people don't feel included in these changes. They feel like someone somewhere within the company made this decisions for them, and these decisions are impacting their day-to-day jobs. Even when the impact is positive, they may resist the change because they simply didn't feel included. If you involve these business stakeholders along the process of the MDM initiative, from the initial setup, the decision making and rollout, you will have users that will actually promote what you're doing instead of fighting the change.

Involving business stakeholders is critical.

Latest Trends

The next best practice is to always keep watch on the latest trends within the MDM space. MDM is going through constant evolution and innovation. You need to stay informed of the latest trends, advancements, and best practices in MDM in order to adapt and optimize your process. This is important because the new advancements, especially for example, in artificial intelligence and machine learning (AI/ML), will probably change the way we deal with MDM in the next few years. This is going to present for us a lot of opportunities to improve the way we are handling data and enhance our capabilities.

In the next few years, AI and ML techniques will help us to automate a lot of the processes within MDM around data cleansing, data matching, and data enrichment. All of this is going to make our data much more valuable. Therefore, you need to stay informed of what's the latest around MDM in order to make sure that you can apply some of the newest best practices and capabilities.

Now, these changes should not keep you from implementing in its current state. In most cases, these changes will actually be integrated into the existing tools. So it's going to be a matter of starting to use some of these new features that you're going to see appear in the solutions that you're currently deploying. Do not wait another few years before you implement because you think it is going to be completely different. It's not going to be completely different in a few years. It's just going to use some of the latest trends in AI in order to improve some of the tools features.

If you keep watch on what's latest in the tools that you're using for MDM, you are going to be able to take advantage of this new features.

Ownership and Accountability

Some companies spend a lot of time thinking about what the company needs are and what is the solution that's going to fit these needs. What they are missing is assigning clear ownership and accountability for the long term on who's going to be responsible for what. It's really critical to have these roles clearly defined for different domains in order to maintain the data quality and integrity. Therefore, you need to assign data stewards or data owners who are going to be responsible for the specific data domains. Make sure that these same people are fully accountable for the data accuracy, consistency and compliance. If something goes wrong, you know who is going to be the subject matter expert that is going to be able to help you resolve the issue; otherwise, if you don't do that, it's going to be a matter of everyone pointing the responsibility to a different team.

There are a few reasons why you need clear data ownership and accountability.

1. You need to make sure that you have dedicated experts who have knowledge and authority to manage and maintain data quality standards. By making sure that you have data stewards that define and enforce the data standards and resolve any data quality issues, you are going to promote a culture of data responsibility, and in the long term, this is going to make sure that your data stays accurate and up to date.
2. Having clear data ownership will mean that you're going to have effective data governance. Once you have the responsibility of the data clearly defined, everyone does responsible for certain piece of the puzzle is going to feel like their data is their own. They're going to govern the data and making sure all the policies are followed. This is going to be key for the data governance within your company.

A the end of the day, if you have clear data ownership and accountability, you're going to have better data, which means you're going to have better data driven decision making within the company. Do not make the mistake of going through all the efforts to implement an MDM initiative without assigning clear ownership and accountability.

Conclusion

If you do not follow these best practice, in the longer term, your MDM initiative will end up as one of those projects within the company that started with good intentions.

The solution gets rolled out, but then 1 to 2 years after the rollout, things will not be looking good. No one is using the data. The data is not good anymore. Maybe nNobody has kept up with the maintenance of the data All the effort was for nothing and you do not really see good return on investment for all the work that was done.