Master data is one of the core elements of businesses today. Automation opportunities, business intelligence and efficient business processes – all of these require a reliable master data basis. But how can this be achieved?
In our previous article, we have described which pitfalls bad data entails. Apart from the challenge of keeping up with the latest Robotic Process Automation (RPA) and Artificial Intelligence (AI) opportunities, crucial business processes can be impeded through loss of capacities, loss of time, loss of sales revenues, loss of cost saving opportunities and compliance issues. Hence, ensuring resilient master data has become a vital factor to enhance business processes.
The MDM House
To establish a reliable master data basis, there are six topics which need to be addressed. It all starts with a master data management (MDM) strategy, continues with processes, organization, data, and IT systems, and finishes with the support and operations organization. This approach can be visualized in a house. The strategy is the roof, the vision, which all the other components support. The pillars of the house are the processes, the organization, the data as well as the IT systems, which create and maintain high quality master data. The operations and support are the foundation which render the entire construction resilient and sustainable.
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Seven Steps towards MDM
At CAMELOT, we have developed a holistic approach to prepare and accompany businesses on their path towards high quality master data. With seven steps towards master data management, we have helped many companies to drastically increase their master data quality and maintain this high level even after the initial project.
- Strategy
- As-is analysis
- To-be design
- Data cleansing
- Implementation and migration
- Training
- Hand-over
Strategy
First, a strategy must be defined. At CAMELOT, the strategy assessment is usually an individual project. It includes a rough assessment of the current master data situation followed by workshops to derive where the master data journey should go. This helps to establish a vision and mission and derive a roadmap. To ensure the strategy is not simply a pretty presentation but can really be achieved, we then move on to accompanying the realization of this strategy.
As-Is Analysis
Making a first move towards realizing a strategy is to understand the current situation. Which processes are currently in place? Which data is currently maintained and what is their usage in business processes? Who is currently part of master data maintenance? Are there any special cases to be considered? These questions help to derive the requirements for the to-be design. Furthermore, they help to ensure no exceptions and special situations are missed when creating a to-be picture. In addition, the as-is analysis will, in a later stage, be compared to the to-be design. This is to understand how big the change is that the MDM organization is going through. This way, a tailored approach for change management and training is enabled.
To-Be Design
The third step towards a new MDM is to develop a to-be design. What should the future MDM organization look like? Which processes are needed and where are standardization and harmonization opportunities? Which data really needs to be maintained to enable business processes? Based on the requirements from the as-is analysis and new requirements, the new master data management structures are elaborated.
Data Cleansing
Before moving the existing master data to a new system, data needs to be cleansed. This way, only relevant and correct data is moved to the new system. The new MDM era is started with a set of high-quality data. We at CAMELOT support the cleansing with tools to identify data which needs cleansing. The actual cleansing is done by subject matter experts (SMEs) from the client who have the knowledge to decide on the correct data values.
Implementation and Migration
Once the to-be design is defined, the new structures can be implemented from a technical and an organizational perspective. The technical implementation is taken over by developers who are in a constant feedback-loop with the client to iterate the new system. All developments are accompanied by tests to ensure their functionality. From an organizational perspective, we focus on change management activities, such as developing a communication plan to take all stakeholders identified in a stakeholder analysis upon the journey or managing the nomination of employees to take up the newly designed roles.
For the migration, a migration landscape is set up and used for trial loads to test the migration before the productive load during the go-live. After each trial load, errors are analyzed and solved. The report created during analysis is handed to the client’s data cleansing team who can use the report as basis for their activities. Before go-live, we conduct the productive load.
Training
Before the go-live of the new master data management tool, the participants of the new MDM organization are trained. The trainings on the operational level comprise of end-user trainings in the new MDM tool as well as understanding the new processes, their role, and their responsibilities. Trainings on a tactical and strategic level focus more on the understanding of the new tasks and responsibilities. Together, first KPIs are developed to set a starting point for the new data quality management.
Go-Live and Hand-Over
After the go-live of the new MDM, there is a phase of hypercare. If consultants or external partners implemented the system, there are several additional steps that ensure a smooth hand-over. The team who realized the new structures takes over the first level support. They gather all documentation created during the implementation project and consolidate it for the client’s reference. In addition, the new maintenance team is trained from a functional as well as from a technical perspective and starts handling end-user questions or bug fixes together with the external partner. That way, on the day of handover from the consulting company to the client, the new maintenance team is already used to managing the system support and have a smooth entry to their new role.
Conclusion
With these seven steps, a new era of master data management can be achieved. Data quality is brought to a high level in a very short time and the foundation is built to stay on this high level. Together with the client, we pave the way for next level MDM, which includes RPA and AI solutions.
One topic a lot of companies still struggle with is to understand which data and which data attributes are actually used in which business process. This is vital to understand which attributes must be filled for each data object and which ones should be equipped with a special governance (e.g., a four-eye-principle) as they are steering certain business processes. CAMELOT currently develops a template to derive this knowledge and provide experience from companies in a similar business sector to simplify this derivation/understanding. This will be explained in a following article.
