To successfully realize the vision of becoming a data-driven enterprise, companies need to invest in a well-defined master data strategy. The importance of data, and especially the quality of data, is increasingly coming to the fore. But how to achieve this data excellence, what to consider and what it can mean trying to achieve data excellence without an appropriate strategy, will be pointed out in the following article.
Definition and Building Blocks of a Master Data Strategy
To define a master data strategy, you first need to know what you are aiming for. Thus, a vision where your company needs to be in the next three, four, ten years in terms of master data management (MDM) is critical.
Accordingly, the strategy describes the path, defined in work packages, to the vision.
Based on our experience, it is of utmost importance to develop a holistic strategy. The CAMELOT MDM House shows the building blocks that are considered for a master data strategy definition ensuring this holistic view.
Graphic 1: The MDM House shows the dimensions considered for a master data strategy definition.
In the first big building block called processes, data processes and architecture as well as blueprints and technical specifications are looked at, and a process evaluation is carried out. In addition to roles and responsibilities, the building block “organization” also looks at structures and processes that are required for the global and local master data organization. The building block “master data” covers topics such as information and data model, nomenclature and semantics, and the integration of external services. The fourth major building block, IT systems, looks at IT architecture, including workflows, user interfaces and integration. The last building block, stretching horizontally across all pillars, deals with the cross-thematic area of operation and support, more specific with professional and technical support, data quality monitoring, and training and knowledge management.
Four Phases in Realizing Your MDM Vision
Our MDM Strategy Assessment approach takes all previously mentioned building blocks into account and guides you to your MDM vision in four phases.
Graphic 2: Four phases towards your MDM vision.
From Status Quo Analysis to Envisioning
The main project work starts with the second phase, the status quo analysis. The status quo analysis is the baseline for each strategy project. In this phase, the as-is situation is gathered, analyzed, and evaluated via
- review of existing documentation,
- focus interviews with knowledgeable stakeholders,
- online questionnaires, and
- deep dive workshops.
There are a few points that are important to note here. For instance, focus interviews and online questionnaires should be conducted across all functions (i.e., procurement, sales, supply chain, …). Furthermore, a bottom-up approach is recommended as the users who work closely with the data usually have deeper insights into issues and gaps than the management does. Here, familiarity with a different level of detail plays a major role. It also is important to get examples for certain pain points or problems to make the topics more tangible and help to justify efforts for respective solutions.
However, management support for the topic is as crucial as input from the operational level. Without sufficient management support, the likelihood of initiation is considerably lower as the impact of master data initiative can go cross the entire organization, affecting all kind of areas (e.g., IT, logistics, etc.).
The outcome of the status quo analysis is
- insights about the current state and first requirements for a future solution, and
- a maturity map indicating prioritized fields of action (including potential low hanging fruits).
At the end of the status quo analysis, priorities should be set for the upcoming concept phase as these can differ case by case. For example, some companies need to focus on E3E processes, other more on building up a governance model. Still, even when setting priorities, it needs to be considered, that all building blocks do have interdependencies. Changing the architecture, for example, affects processes and organizations; changing the governance can have impact on processes and data.
From Envisioning to Roadmap
Starting with an envisioning workshop, the goals of MDM are defined, and future relevant aspects and possibilities are presented and discussed.
In more detailed to-be workshops, a future state for each of the building blocks is defined jointly with main stakeholders. Within the workshops, best practice and theoretical models are mapped against the current settings, and a best-fit design is developed.
The outcome of the to-be workshops is a defined target state for each dimension of the MDM House including potential interim scenarios for each of the solutions. Based on this, work packages are defined with a rough effort estimation and prioritization.
The key decisions and the to-be concepts are combined in a roadmap that details relevant steps to achieving the goal. The roadmap shows all defined work packages on a timeline, divided into the already known areas of organization and governance, processes, data and data quality, and IT-architecture. Detailed specifications of the defined work packages guide towards the vision and indicate what prerequisites need to be fulfilled to start and achieve the task.
Once the strategy is set up, both vision and strategy should be continuously reviewed and adapted to current developments.
“Slice the elephant” – Tips from Projects
A tip from one of our MDM strategy experts: “Slice the elephant” regarding your MDM vision, your MDM goals. You should challenge your organization to achieve data excellence, but you should also not overwhelm your organization and adapt too much (at once). Always keep in mind, that the work packages normally are coming on top of the operational work. If too much is to be achieved at once, you will risk frustrated employees and losing momentum.
A second learning: As a foundation, a clearly defined MDM organization with clear processes and responsibilities is inevitable for success. However, it is not only important that the MDM organization follows a clear structure and rules, but that it is supported by the entire organization, both management and departments. Only in this way the measures can be implemented in daily business on the one hand, and organization-wide on the other.
Benefits of a Master Data Strategy
Data excellence is the foundation of data-driven business models. It relies on your master data strategy. To make sure you do not think “phew, that sounds like a lot of work, let us not do that”, we want to give you a few reasons why you and your company should put in the effort:
- You have a clear path forward on how to reach your companies MDM vision.
- Based on the strategy, you can plan tasks, resources, budget.
- People working with data (especially operationally) see progress and feel heard.
- With quick wins, achievements can be presented, getting buy-in from stakeholders.
Key Elements
A well-defined master data strategy is the basis for achieving data excellence. It identifies pain points, good practices, action areas in the current organization, implements functioning processes and a clearly structured MDM organization with explicit roles and responsibilities. The resulting roadmap not only shows your organization’s way to the vision, but also outlines exactly in work packages what should be done and when. It is important to consider that definition, design, and implementation is supported across the organization. Higher costs and maintenance efforts as well as a lack of reporting are just some of the aspects you will have to live with if you do not address your master data strategy.
The above examples are just a few and the list of benefits can go on and on. If you would like to read more, feel free to check out previous articles on the subject Master Data Strategy here.
