Gartner describe Master Data Management (MDM) as a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.
It is a detailed and complex description but there are some key points to note here
- Business & IT must work together, each with an understanding of the other’s requirements and limitations. The technology enables the enhanced management but the business must utilise this accurately, consistently and wholly
- Stewardship / accountability – the key word in Master Data Management in Management. The data must do defined, monitored and controlled There must be a person or people in place, held accountable for the quality and maintenance of the data. It is here that MDM falls into the realm of Data Governance
- Uniform identifiers of core entities – structuring of the way the data is to be held is a key requirement of the setting up of and MDM system. With data coming from potentially multiple sources (countries, companies, suppliers etc.) there must be a structure placed upon it in order to be able to manage it effectively. Defining what will be managed and how must be clearly defined.
What is Master Data?
Master data is common data about customers, suppliers, partners, products, materials, orders, accounts and other critical “entities,” that is commonly stored and replicated across IT systems. This information is highly valuable, core information that is used to support critical business processes across the enterprise. It is at the heart of every business transaction, application, report and decision. With the ability to source data from an expanding number of areas including the internet, customer direct input, machine monitoring and reporting plus a high level of meta data, companies are in a position where their “Big Data” they acquire can spiral out of control if not managed properly (or at all!). The combination of MDM and emerging big data technologies provides a 360-degree view of customers and products.
Simply put, metadata is data that describes other data. “Meta” is a prefix that means “an underlying definition or description”. Metadata summarizes basic information about data, which can make finding and working with particular instances of data easier. For example, author, date created and date modified and file size are examples of very basic document metadata. Having the ability to filter through that metadata makes it much easier for someone to locate a specific document. In addition to document files, metadata is used for images, videos, spreadsheets and web pages. Metadata can be created manually, or by automated information processing. Manual creation tends to be more accurate, allowing the user to input any information they feel is relevant or needed to help describe the file.
The overall goal of MDM is the same as any other Business Intelligence process or tool in its effort to help the user really understand the data. The first step is to get the data, in MDMs case from multiple sources and in multiple formats. It must then categorise and label it in order to be able to analyse it in a structured and accurate fashion. The data can then be turned into information, where facts can be drawn from the figures. Customer habits, manufacturing costs and competitor performance can be identified and quantified. This information can be turned into knowledge where it is analysed and applied to identify trends, upcoming opportunities or potential threats. The most important part of this process is the utilisation of the knowledge gained to make decisions that will benefit the business.
What happens if not implemented
In most organizations, operational information is duplicated and scattered across multiple systems and applications, which makes it difficult for decision-makers to achieve a unified view of operational intelligence. Disparate information also prevents customers from getting the accurate and timely information they need to make purchasing decisions. In fact, most transactional data is linked in some way to master data. So, missing data, low quality information and untrustworthy or inaccurate records have a big impact on revenue, productivity, costs, compliance, agility and decision-making. Therefore, managing this master-level information proactively as it flows through the organization is essential to improving business performance.
What are the benefits?
- All data stored in a centralised, single source of truth. This mean that data is accurate and trustworthy data that can used with confidence.
- The quality of the information deemed from the data is improved as it is easier to analyse and provides a full view of all data at once
- A single source of data can be cheaper to manage and access (once a certain level of data mass is reached e.g. little advantage to small company)
- Improved business capabilities
- Improved technical capabilities
- Enhanced security is provided as data is stored in a single, controllable and secure location. Access can be controlled, managed and monitored to prevent unauthorised access. Even with a single source of data, access can be granted to parts of data on a principle of least privilege basis.
Key factors in a successful MDM
In order for a successful MDM system, a number of key factors need to be taken into consideration.
- As stated earlier, and MDM is a business project not an IT one. The business owners must be involved throughout the processes, providing input and defining expected outputs. In a 2011 study carried out by PWC on MDM, they found that 70% of those they interviewed said that it was the revised governance and good management that provided the most benefit Vs only 27% considered the state of the art IT to be the key success factor.
- For it to be effective, all departments, locations, business units etc. must be equally committed. Any siloed information removes the core benefits of and MDM as it prevents visibility of the whole picture. However, this should not stop the rollout of the MDM being done in phases. Easily accessed data or controlled departments should be focused on from the start to provide immediate affect and show the MDM to be beneficial to the potentially more difficult areas.
- MDM must be considered as a cultural choice of a business. It is not a one-off project with one off results. It needs to be open to development and expansion as the business grows its product list, its locations or customers.
- The rollout of an MDM needs to be driven by C-Level business owners. Sponsorship of a project is nice to have but for a successful MDM, it requires top management to “want” the information that it is capable of providing.
Domino’s Pizza, which has 12,000 stores worldwide spread over 75 countries has always tried to stay at the forefront of the technology market. However, in 2014 CIO Kevin Visconi decided the time was right to implement a MDM to even better understand their customers. A tender resulted in Profisee’s Mastreo platform being implemented with steps taken to gather and cleanse 550 million unique customers. Analysing their buying habits allowed Domino’s to identify 100 million “golden” customers to carry out focused marketing on. With the MDM in place, Domino’s continues to expand its understanding of its customers habits and is looking at rolling out the MDM to manage its suppliers and products.
There are 5 core steps in the implementation of an MDM
- Discovery – Documenting and modelling essential business data and processes for utilizing common data, identifying all data sources and defining metadata. In this step, start with the most important subject area and define it. Additionally, in this step an IT architect should design the MDM architecture based on the organization’s planned approach and goals for managing master data and in conjunction with the existing enterprise architecture.
- Analysis – this involves identifying the main sources of the data, evaluating data flows and transformation rules, defining the meta data definitions and data quality requirements. In this step, it’s essential to have the participation of representatives from an established data governance program. This is the most challenging step, since it is iterative and requires participation from a variety of role
- Construction of the MDM in line with the architecture you’ve identified
- Implementation of the database, populating it with the master data, assigning administrative and access right, defining change management processes and assessing the quality of the data.
- Sustainment – continuing to rollout the MDM across the business, managing and maintaining it on an ongoing basis and controlling change management.
In the growing world of Big Data, Master Data Management needs to be a core component in any business’s long term strategic plans (as it is definitely in their competitors). It can be a big undertaking but if the proper steps are followed it can provide the ability to obtain knowledge from data that the business already owns. However, in order for it to be truly successful, it needs to be supported and driven from the top down and become part of the company’s ethos. However, given the proper respect and consideration from both business owners and IT alike, it can provide a wealth of information that can transform how the company sees itself and the world around it.