In this blog post, we have compiled some of the most important dos and dont's for ensuring that MDM becomes a sustainable success in your company.
To begin with, we would like to highlight a few important conclusions based on our previous posts in the series:
- Insight, measurement, and improvement of data quality are essential factors for building trust in the data and analysis that are widely available within the organization. However, companies often lack awareness of any existing quality issues and do not have a solid set of measurements that can be tracked over time. Numbers speak for themselves, so remember to create insight and awareness of any quality issues as a starting point. This can also help set the level of ambition and goals for your MDM program overall.
- Master data is created and used extensively within companies, which is why MDM becomes a cross-functional task involving data sources, departments, and business areas. It is therefore important that you establish governance in terms of ownership and responsibility for master data, as well as create a collaborative form and operating model that encourages the involvement of process, IT, and business experts. Your operating model should ensure a shared focus on all elements of MDM, including strategy, processes, organization, governance, technology, content, and quality, to realize the benefits of MDM.
Dos and dont's
There are many experiences and opinions on what to do, when, and how, and ultimately, it will be up to each company's situation and ambitions to decide.
We have compiled some of the most important dos and dont's below:
DO: Define your requirements for master data and data quality needs before purchasing a master data platform
A platform alone does not solve your challenges. You should be aware of data domains, systems, integrations, and understand how data quality impacts your business and growth before choosing the technical solution. Otherwise, you risk the solution not being able to handle the most urgent challenges.
DON'T: Implement a platform or master data organization without clear objectives and a business case
If you don't have a strategy for what you want to achieve or have established goals for success, along with support and funding for MDM supported by a business case, it can lead to a lack of proper prioritization and organizational support, resulting in the failure of MDM initiatives. Master data is crucial for the entire company, and implementing a good solution involves many stakeholders across the organization. To succeed with MDM, it is therefore essential that all stakeholders are aware of the purpose, priorities, and objectives of the initiatives, and understand the significance of their efforts for both their work and the entire organization.
DO: Think scalability
A data platform should be able to grow with your company. Perhaps there are plans for market expansion, a more complex product hierarchy than you currently have, or something else that will make the coherence and rules of your master data more complex in the future. Even if you don't know it today, try to think in generic terms and consider expansion possibilities when choosing a master data solution. If your company is starting with just one group of master data, such as customer data, you will likely continue to build on it and add more over time.
DON'T: Choose the technology first and the resources later
As mentioned in a previous blog post, new technology does not solve all your challenges. By primarily focusing on the technical solutions to your master data challenges, you risk making MDM primarily about IT rather than business value. To achieve success, your employees need to be engaged. Engagement is a result of the right collaboration model and the presence of a governance organization with data owners, Data Stewards, etc., who focus on resolving master data challenges and understanding the business value.
DO – Develop master data definitions and data quality rules in a cross-functional team
It is important that from the beginning, you develop your master data definitions with representatives from multiple departments. This is both to gain everyone's perspective on challenges and solutions and to reduce the risk of disagreements about rules and definitions in the future. There are often different opinions on what constitutes good data quality. Therefore, it is important to achieve agreement across departments to ensure a successful implementation.
DO – Implement MDM governance within the existing organizational structure as much as possible at the beginning
New role concepts, responsibilities, and ways of working can take time to adapt to. If MDM governance is entirely new to the organization, it can be a good idea to initially place the new roles within the existing organizational structure. This way, new roles are gradually introduced in a familiar environment, making role assignment and integration easier. By adapting to existing work practices and gradually implementing the new MDM operating model, there is a greater chance of successful integration across the organization.
DO – Always implement KPI's
You need to know your starting point and your goals for where you want to go using numbers and timelines. This makes it easier to gain support from the entire company when you can quantify how data is improving and how it affects your business and ROI. It's not enough to simply explain that master data is important. Putting numbers on aspects such as data quality, how it improves over time, and what it means for revenue, profitability, etc., helps the involved parties feel more accountable for improving and ensuring the quality of master data.
DON'T – Limit governance to just assigning roles and responsibilities
To implement your new governance and move from theory to practice, you should ensure a solid operating model and practical ways of working. Creating an organizational chart and developing job descriptions alone doesn't guarantee automatic improvement in the quality and integrity of data. It is your operating model with well-defined processes that ensure the proper use of tools and techniques to adhere to agreed-upon data policies, which helps you in your day-to-day operations.