Getting Started with Master Data Management
Gartner defines master data as a consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise.
This places master data at the heart of every business process within an organisation and because it’s used in multiple systems and processes, bad master data will have huge effects in the business processes.
The negative impacts of unmanaged master data can easily be identified across almost multiple levels of an organization’s activities, impairing the decision-making processes, hence impacting performance, customer, and product profitability, but also with impacts at an operational level, reducing productivity and efficiency, but also leading to compliance risks.
Master Data Quality
In the usual siloed ecosystem, the most common scenario in most organizations, master data is scattered across multiple systems, governed by multiple rules, managed by multiple processes, collected, and updated under different conditions and replicated trough ad-hoc processes, unavoidably leading to lack of quality.
By definition — data that lacks quality is unfit for use. Considering the master data is a critical part of most of an organization’s processes, from the factory floor to the board, it’s easy to conclude how broad the impacts of bad master data are.
Traditionally data quality is evaluated using six dimensions: completeness, accuracy, consistency, validity, uniqueness, and integrity.
Considering customer data — the most common master data domain, and usually the priority when starting a master data management initiative — under the perspective of each of these dimensions, we can have an overall image of how unmanaged master data looks.
This dimension measures if the data is sufficient for its purpose
Each of the business areas (or systems) within an organization has its own business purposes and its own data requirements, this means that not all customer information will be necessary in its multiple applications across the organization. From a detailed perspective probably some of these systems will have a complete set of customer data that fits its needs, from a broader, corporate perspective this is most likely not true. This usually becomes noticeable when there are compliance requirements that don’t completely overlap the business requirements.
This dimension measures the level of adherence data has to the real-world entity.
As with the previous dimension, with different requirements, also the needs and rules under which customer data is collected will be different for each of the systems, leading to different degrees of accuracy and impacting the next dimension, consistency.
This dimension measures the level to which the same data matches at multiple instances.
Consistency is probably the major issue related with siloed architectures, even with the best built integration processes, and real time replication of data, the existing differences between the multiple systems (attributes, formats, etc.) will prevent the existence of a consistent version of customer data within the organization.
This dimension measures the level of compliance with specific domains or requirements.
With the different needs and requirements, inevitable the rules governing customer data will differ for each of the systems, data that is valid in one context will be unusable in a different one.
This dimension measures the level to which a single instance of data is being used.
Duplicates are one of the most common issues when addressing customer data — either by the creation of multiple instances in single systems over time or by creating those instances in different systems that are then replicated — this can only be addressed from a global perspective, to ensure that single instances exist.
This dimension measures the level to which the relations between attributes are correctly maintained.
The data elements that compose an entity, in this case a customer, are related and change over time, and the entity is considered complete and valid when all its elements, in all its instances respect these quality dimensions and business requirements. Again, in a siloed environment, integrity is a hard dimension to maintain, with multiple collection and update points, different rules, and replication processes.
Without wanting to be exhaustive about the impacts of unmanaged master data, I hope I was able to give an idea of the importance of have master data — that is after all the core business data for any organisation — properly managed.
What Data is Master Data?
Master data is usually defined as the set of core entities within an organization, depending on the industry it may include customers, prospects, suppliers, sites, hierarchies etc.
Master data management should cover the process of collecting the data that is for each of these domains and provide it to all relevant systems and stakeholders.
The purpose of managing this data is to assure a consistent definition of these business entities and data about them across the organization’s multiple systems, establishing a standard definition for business-critical data that represents a single source of truth.
Master data is the most valuable data that an organization owns, used across all the organizations’ units, processes, and systems to keep the organization working.
Without consolidating this data, each business process is forced to rely on locally available data too often incomplete, obsolete, redundant, and low quality, with substantial negative impacts on business performance.
In the absence of a master data management solution, the most usual approach has been to bridge these siloed systems by building point-to-point interfaces, leading to complex and expensive IT solutions that fall short on the initial objectives.
On the other hand, Master Data Management solution will offer the capability to merge, cleanse, optimise and manage all master data in an organization, integrating different data sources at a central location — that will become single source of truth — and to distribute it to different target systems (Core, CRM, BPM, Portals, etc.), ensuring cross system data consistency, removing the data redundancies and inconsistencies that reduce business performance.
Master Data Management plays an important role in this solution and a pivotal role in the organizations’ ambitions in a competitive marketplace.
Master Data Management is crucial as the foundation in this process:
- Providing customers, the personal experience they demand.
- Providing the core business data processes with accurate, reliable, and timely information.
- Synchronizing digital and physical channels, accelerate time-to-market and increase up-sell and cross-sell conversions, with the range of products, business partners, and the organizations’ structure with its branches, warehouses, stores, and other sales channels and fully available and consistent.
- Managing the relationship between the organization and its vendors.
- Maintaining high-quality privacy-compliant consumer data.
The purpose here is not to give a detailed perspective of all the features a master data management can offer but the critical impact it has on the performance of the most critical business processes that are using this data.
Information on how to develop a Master Data Management solution is quite abundant, and it quite easy to find a few frameworks that can easily be adapted to the tool of choice.
What makes it so difficult? — even when you’re considering if your organization needs to adapt an MDM solution, or you’ve already decided it, but you don’t know where to start, if you already have some MDM capabilities implemented but need to step up, or worse you already have a failure in your hands — How can it be achieved?
One of the frequent causes for failure of master data management failures, or at least some lack of traction is usually appointed to some lack of leadership buy-in and commitment — something I tend to relate to the absence of a strong and clear vision for master data.
The role of top management it’s not simply to sanction the solution.
The priority must be to establish the business vision. What’s the role of Master Data in the overall business strategy, and consequently in data strategy?
It is essential that they communicate a vision of what and why is to be achieved — a strong purpose — to demonstrate that the Master Data Management solution is an unquestionable priority, making other leaders accountable, and making it harder to back-track.
These processes need buy-in from all levels of the organization, it may start with strong executive sponsorship but needs the commitment of all the other stakeholders in the organization.
Clear, ambitious (business) objectives
Data’s purpose is to create value, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives — Data strategy is business strategy. The same is true for master data, maybe even more considering the critical role this data plays in the most critical processes within an organization.
The drivers for a Master Data Management solution can be many, depending on the objectives of the organization. They might be oriented towards, customer centricity, product excellence, regulatory compliance objectives or operational efficiency.
It doesn’t matter how clear the benefits are, the bottom line is that the business value of a Master Data Management solution must be very clear to all the stakeholders.
Whatever the driver behind the initiative it is essential that clear, ambitious objectives are set from the beginning — objectives that can be clearly related to business objectives and evaluated by the business value they generate.
Plan with the end in mind
Building a Master Data Management solution can be a long process, it needs careful planning.
Creating a roadmap is an essential tool. Mapping all the initiatives needed to complete the Master Data Management objectives, identifying which data domains to address, which data to include in those domains, which systems and processes will be involved, identifying the existing gaps between the current situation and the future situation, and most important the existing gap between business and the existing IT ecosystem.
It is critical to reinforce the role of business in this process, to allow that all initiatives are driven and oriented by the business units. Master Data Management is mostly a technical initiative, but some of its most critical components are a business responsibility, as it is the business who better knows what their problems and objectives are.
Planning the roadmap must also create the conditions to ensure early efforts to thrive and gain traction. Choosing carefully on which initiatives to start with and support them with the necessary resources.
The priorities identified in the roadmap allow to define the business cases to address first — business cases not use cases.
In an early stage, for effectiveness purposes, there should not be more than five business cases running in parallel, all with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.
All these cases have a business owner, and it’s essential to identify the owners of these cases that have their processes impacted by low quality master data. Define what these problems are costing the organization, and how much it can save by solving them.
Select small, targeted initiatives, where the impact and value of master data can be clearly identified, with business stakeholders that can passionately and effectively articulate the impacts of master data in their business processes and that will be eager to defend the project.
Focus on success and on gaining traction. Look for initiatives that can be framed within a reasonable funding model, that are targeted, with focused effort, within short timeframes, able to increase internal engagement, and that can deliver targeted returns on short timeframes.
Apply an agile development mindset to all this process, start with a minimum viable solution and iterate, allow those visible results are presented in short time lapses.
Promoting agile product development, test-and-learn methods, and cross-functional teams that bring together specific types of expertise, will enhance the capabilities to create the business values necessary to build momentum and a sequence of successful data initiatives.
This agile mindset combined with the ongoing initiatives where the business stakeholders have an active role will accelerate the process of quickly move from the findings to specific actions.
Build on success stories
A success story, even at a small scale will create the awareness within the organization and act as a motor to leverage the replication of that story in other business units.
When these initiatives are successful and deliver the intended benefits, business leaders will be encouraged to push to achieve more, not only focusing on what works well, but also on letting go of what doesn’t work.
Additionally, this focus on the involvement of business stakeholders driving these processes where visible business value is generated, will turn potential detractors in to change evangelists, even if only by sheer peer pressure.