Master Data Management: A Complete Guide from A to Z
According to Gartner, “Master data management (MDM) is 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...” This insight takes you through everything that exists about MDM. So, read on!
1. Data Management: The Beginnings
2. Emergence and Need for the Enterprise Master Data Management
3. What is Master Data Management (MDM)?
4. When is it required? Why is it Needed Now?
5. What is Multi-domain?
6. Who Needs Master Data Management?
7. What is Master Data Governance?
8. Key Benefits of Master Data Management?
9. What is Master Data Management for Products?
10. What is Open-Source Master Data Management?
11. How To find the Right Master Data Management Strategy?
1. Data Management: The Beginnings
One of the early facilitators of data management was Enterprise Resource Planning (ERP) software, whose beginnings can be traced back to the 1960s. It was coined as ‘ERP’ much later in the 1990s by Gartner. Aimed at attaining efficiency in business management, ERP comprises of a suite of integrated applications to gather, stock, manage, and decipher data from numerous business activities. It fuels the automation needs of the back office and front office functions and is behind collaborative initiatives like supply chain management (SCM), customer relationship management (CRM), business intelligence (BI), and a variety of e-business technologies.
Over the years with industrial systems, technology systems evolved— and the value of information and data became paramount. As data continued to grow exponentially, IT leaders who dealt with the abundance of information realized that ERP was not enough. Suddenly, CRMs fell under the ambit of the IT department due to huge advancements in sales, marketing, and customer service technology. They evolved into a significant force and provided a customer “master” record. Have a look at the diagram from Gartner to see which types of master data an organization in the manufacturing industry deals with and which software is used along the product and process life cycle.
2. Emergence and the Need for the Enterprise
Enterprise master data management looks at fixing complexities across IT landscapes owing to the usage of different applications, technologies, and systems through nipping the data quality issues in the bud. Systems such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Order Management Systems (OMS) and even Supply Chain Management (SCM) functions having their own sets of siloed master data can jeopardize operations, compromise analytics, cost enterprises dearly, sending profit margins into a tailspin. Enterprise MDM emerges as an answer to the data quality and consistency problem for enterprises, as it creates a “golden record” of data that accumulates data from numerous data entry points. It is carried out by combining the operational side of the business, along with data warehousing and business analytics.
3. What Is Master Data Management (MDM)?
According to Gartner,“Master data management (MDM) is 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.”
Master data management (MDM) is an amalgamation of sound data management practices, including applications, technologies alongside key stakeholders, partners, and business clients. It involves consolidating, cleaning as well as augmenting corporate master data and synching it with business processes, analytical tools to implement the policies, services, procedures across the enterprise infrastructure to ease the capture and integration of data in a timely, consistent and complete manner. The ultimate goal of master data management is to significantly improve operational efficiency, enhance data reporting, and help businesses make smarter decisions.
What is a Master Data Management Program?
A master data management (MDM) program sources data from multiple systems and offers a single view of it by consolidating all the data in a “golden” record. For instance, in the case of customer data, the records of the customers may not be identical at various points like- order entry, customer service, or shipping. That’s due to inconsistencies in name, residence, and other characteristics. MDM standardizes all the customer data into a unique set of master data assets that can be used in all connected systems. This not only helps organizations to remove duplicate data but do away with redundancies and incongruities so that for everyone, be it, operational staff, data analysts, or decision-making executives, there’s one and only one picture of every individual customer. An MDM program can be applied to customer, product or any other kind of data.
What is a Master Data Management System?
A master data management system promises authoritative, dependable data to enterprises, suitable to their needs and aims. It is influenced by mainly three factors a) the domain of all master data, b) the way enterprise system operates currently, c) the implementation style needed for the deployment in question.
As a general practice, the MDM system deployments do not happen in a full-fledged, full-throttle manner simultaneously for all domains of data. Instead, it takes place in phases, where initially, the scope is restricted by only focusing on the critical areas needed to ensure ROI remains free of any impediments in the shortest span-of-time. The implementation is then extended to other systems, and the scope of implementation is expanded. Likewise, more domains are included in the fold, and the method of use may evolve along the way to create business value further. An MDM system can be executed in a multidisciplinary, multi-geographical environment.
4. When MDM is Required? Why is it Needed Now?
It all starts when different departments, while making changes to data through the value chain, create their own version of it, store it in silos and reintroduce that modified data back into the system, leading to duplication, redundancies, ultimately hampering enterprise productivity and overall output. The most definite signs that you need an MDM are:
- Unclear Picture of Data – If the enterprise data is lying across disparate locations, systems, applications, and other sources, it can never give out a complete picture of the data, leading to unnecessary complications.
- Finding Data Turning Troublesome – If there are no inter-domain associations between departments (suppliers, product teams, sales, and marketing) knowing which data is the right data will only get challenging.
- Dipping Customer Satisfaction – Customer dissatisfaction can be a consequence of inconsistent, incomplete, low-quality data that can result in abandoned carts, bad reviews, and can severely hurt the brand image.
- Increasing Unstructured Data – Unstructured data doesn’t have an already defined schema or data model. It can be textual or non-textual but can be a cause of concern as it can lead to huge ambiguities.
- Suffering Business Intelligence – Incorrect or incomplete data can never be the source of accurate analysis; it can never give you the right insight into market trends, customers’ minds or tell you how to use the data to your benefit.
The primary reason why master data management is needed now is due to the meteoric rise in the amount of data. IDC has predicted that 175 zettabytes of new data will be created by the year 2025. With such proportions, data managers can only imagine how data related complexities are going to intensify. Every business will aspire to:
- Drive innovation
- Improve supplier and partner relations
- Decrease time-to-market
- Enhance compliance and risk management
- Reduce costs/Increase margins
- Improve decision making
- Increase revenue growth
- Enhance customer relations/service
- Develop internal/operational efficiencies
- Boost business process agility and outcomes
5. What is a Multi-domain MDM?
As the interest of enterprises spiked in other data domains such as digital assets, channel/partner and vendor/supplier data, geospatial and hierarchical data, the need for a multi-domain MDM came to the fore. A multi-domain, multi-vector MDM hence began to be looked at as enabler of digital businesses from collecting data in a single domain to connecting data in different domains.
Multi-domain MDM solutions assist enterprises in exploring data associations, striking connections, defining entities, and generating newer possibilities to grow.
This progression towards multi-domain MDM was validated by Gartner’s 2018 Magic Quadrant for MDM when 58% of their reference customers showed an inclination towards multi-domain MDM.
6. Who Needs Master Data Management?
It was earlier believed that master data management was only the burning need for large-scale enterprises—that were saturated with scattered data across geographies or participated in mergers and acquisitions—which need wide-ranging data integration.
However, with the surge of data and its effects on significant aspects of business, even smaller companies can no longer afford disparate data management initiatives that lead to fragmented customer experiences. Especially when the market size of MDM is likely to grow to USD 22 billion by 2023, according to a forecast by MarketsandMarkets. Therefore, MDM is today necessary for all business-critical functions like marketing, customer care, product management, sales, supply chain, finance, warehousing, among others.
a) Who’s Involved in an MDM Initiative?
At a broader level, master data management initiatives are undertaken by individuals responsible for data governance, data stewardship, and IT administrators.
- Data Governance Council: does the work of charting out data definitions, access rights, standards, quality rules, as well as executing those rules to enable proper data controls across the organization.
- Data Stewards: are the ones in charge of enforcing policies and guidelines on the instructions of data governance council. They are the ones who cleanse, fix, and manage data with the MDM solution. They hail from support function in organizations, and not necessarily from the IT.
- IT Administrators: are the ones who give suggestions on solution designing, emphasize probable limitation of the system, and extend help in managing, setting-up, and configuring the solution.
b) Who Are the Stakeholders in MDM?
It’s imperative to single out the chief stakeholders before MDM implementation begins. The size and structure of the stakeholder teams will always vary according to the scale of the data or scope of business. However, a general hierarchy could be:
- MDM Council: These are the topmost people in the enterprise, the C-suite/senior executives who create the plan, see how it fits with the IT, look at the fulfillment of future needs, and run the show. The ownership lies with them.
- MDM Steering Team: It comprises of data stewards, and a diverse group of experts from various divisions representing departments in-charge for defining processes, setting expectations, and monitoring program execution as per business needs.
- Solution Architect: Is someone who is responsible for developing the entire solution landscape, using the best technologies for various interfaces and design by following the best practices.
- Program Manager: Is someone who is entrusted with the delivery of the MDM program, by managing the complete team, including (but not limited to): leads of associated technologies; MDM consultants responsible for separate master data entities like the product, customer or vendor; development team; governance team.
c) Which Industries Are Making the Most of It?
7. What is Master Data Governance?
Master data governance is a set of decisions, policies including accountabilities and rights for data-linked processes, carried out as per a pre-approved, formalized model to handle data ownership, privacy, compliance, access, security, sensitivity, risks—to enable organizations to leverage the data as best as possible.
A good master data governance strategy must include:
- Identification of shared resources that need to be managed.
- Delegation of responsibility for resources.
- Communication of how the governance structure is laid out and how the processes will be followed.
- Allocating decision making rights in relation with access to common resources.
- Handover of roles and responsibilities to distinct areas of any implementation, including policies and processes for any common resource.
The Data Governance Hierarchy
- Executive Level: Starting from the top, the executive layer involves the top-level management who are experts in developing policies and strategies; they’re mostly the top executives who supervise the governance of data from above.
- Strategic Level: This comprises of the individuals that set the policies in practice and offer a pre-meditated path to data. They are also liable for the resolution of escalated issues from a strategic perspective.
- Tactical Level: These are the individuals who carry out tactical level data troubleshooting. They outline, monitor, and report about tactical activities and metrics. They also handle the data quality and integrity across functional areas, lines of businesses, and geographies.
- Operational Level: This level comprises people accountable for complying with data standards, effective data production to resolve data-linked issues, managing operational data governance metrics, and enforcement of data standards. These people are the data custodians, data liaison officers, application data architects, and application owners, among others.
As a start for MDM governance efforts, the program managers should comprehend the gaps between existing and the desired life cycles of master data across the enterprise. Identify business requirements, key stakeholders, roles, and actual owners of master data.
8. Key Benefits of Master Data Management
One of the most fundamental operational benefits that master data management achieves is making business departments create data once and enable its use everywhere, likewise updating data once and updating it everywhere. Enterprises can view all the actions and transactions via a structurally and semantically consistent view of master data (comprising of customers, products, vendors, etc.).
MDM enables enterprises to accomplish a variety of things like:
- Uncomplicate procurement processes
- Enhance compliance with privacy legislation
- Helps up-selling and cross-selling
- Gain accurate analytics and business intelligence
- Improve operational risk management
- Decrease overall costs
- Get high-quality data
- Improve employee productivity
- Optimize sales and marketing
- Boost customer satisfaction
- Smarter product management.
- Strengthen fraud prevention
- Decrease supplier onboarding costs.
- Streamline supply chain management
- Cost savings in mergers and acquisitions
However, on a macro level, a host of benefits can be unlocked through MDM by mixing high-quality data and smart business processes:
- Lower Total Cost of Ownership (TCO) and Faster time-to-market
With an MDM in place, adoption and implementation, along with existing maintenance and management costs, come down significantly. There is hardly any need for writing any code. The business rules, security, data stewardship functions, data model are all customizable. No matter which MDM style it is, it is supported by the same software instance. The implementation can happen on-premise, on cloud, or can be a mix of both. MDM’s ability to centralize, standardize, enrich, validate, and link master data records, is outstanding. Its adaptability to multiple regions, channels, languages without data duplication, along with automated workflows to deliver data consistently and quickly across any channel, and launch products swiftly, make faster time-to-market possible.
- Improve Enterprise-wide Efficiency and Higher ROI
MDM helps enterprises to function with amplified acceleration and agility. It boosts the association between applications, systems, and people by integrating disconnected processes to drive performance, manage workflows better, promote collaboration, and reduce risks. It converts duplicate, inconsistent, contradictory data into a single, authoritative form of truth—a golden repository of data. Efficient master data management has a direct bearing on your ROI, as all the data-oriented activities depend on the quality of your data and strong collaboration. Organizations can support vendors and employees with streamlined processes, saving time and resources; marketing and sales teams can target customers in the best possible way and keep adjusting to their expectations, hence maximizing the ROI.
- Enhance Decision-making and Great Customer Experience (CX)
MDM enables better reporting, compliance, and monitoring to drive optimal assessment of risk and opportunities. With a 360-degree view of data across the entire ecosystem, employees and decision-makers can swiftly analyze any glitches or effectiveness of the processes, helping them draw the right inferences to optimize productivity, market performance, vendor collaboration, and customers service. While on the one hand, MDM helps in offering superior, personalized, connected, consistent, and up-to-date product experiences to the customers to fuel engagement. On the other hand, the enhanced visibility into customer preferences through profiling, behavioral information, predictive analytics can not only offer them tailormade customer experience but work wonderfully for customer retention and loyalty.
9. What is Master Data Management for Products?
Master data management for products or product master data management focuses on how businesses manage master data of products by consolidating product data in a single version of truth or golden record for sharing across the enterprise. Product MDM ensures that the accuracy, transparency, and consistency of data is maintained across the entire value chain. It streamlines product data complexities, and creates competitive advantages for businesses, by bringing the right product data, at the right place, in front of the right audience.
Here are Some of the Most Obvious Advantages of Product Master Data Management:
Product master data management helps businesses by linking, synchronizing product data (or information) across heterogeneous data sources existing in multiple geographies via semantic reconciliation of product master data. It enables a single product view for the stakeholders of various business initiatives.
Product master data management improves multichannel delivery of information, increases supply-chain visibility, reduces the time-to-market, quickens new product/service introduction, improves custom analytics, increases enterprise collaboration, fuels innovation, and agility for nimble business process orchestration.
In essence, product master data management does the following for organizations:
- Fix siloed, duplicate, erroneous, doubtful product data.
- Share latest, up-to-the-minute product data across the enterprise.
- Manage products having complex attributes and hierarchies.
- Syndicate relevant, trustworthy product information across multiple customer facing sales channels.
- Get visibility into data, its disparities, relationship patterns—and make necessary rectifications.
- Easily onboard supplier data while offering automation of workflows.
- Maintain governance and regulatory compliance through appropriate audit trails and record accounts.
- Offer the ability to manage, enrich, model all facets of master records.
10. What is Open-Source Master Data Management?
While organizations have almost fully comprehended how MDM can power the fulfillment of corporate objectives, it may still appear to be a daunting undertaking for businesses as it challenges their existing data management status quo, brings architectural complexity, and demands time and expense. After all, MDM is a capital-intensive initiative.
A proprietary MDM software comes with a licensing cost, vendor lock-in, and limited scope of customization and flexibility. While large organizations with enormous resources needn’t have to worry, it’s the mid-level and relatively smaller enterprises that find themselves in a tricky situation.
That’s where open-source MDM scores a point. It decreases implementation complexity, expense, and finds solutions to your unique needs through limitless flexibility, time-to-value—comfortably steering you towards your enterprise MDM goals.
The Most Important Advantages of Open-Source MDM:
Freedom: For open-source MDM, the term ‘freedom’ acquires a lot of meanings. It means freedom from vendor’s restrictions to customize the source code, imparting users’ complete flexibility to leverage the solution as per enterprise needs. It refers to the freedom of interoperability between systems, applications by connecting diverse systems and people with the help of APIs. It also means freedom to seek help from lively, open-source communities where the best developers, MDM professionals, contribute from across the globe. Lastly, freedom means flexibility and the ability to innovate and operate at scale.
Low Cost: In open-source MDM software, the software provider doesn’t charge a penny from organizations for implementation, customization, or system maintenance. Organizations can pick and choose any IT service provider, negotiate for the service they want, or simply build an in-house team to deploy the solution. There are no extra costs for software upgrades or any hidden charges. Overall, this becomes hugely cost-effective in comparison to a proprietary MDM software, where every component in a solution has a price tag attached to it. All of this drastically brings down the total cost of ownership of an open-source MDM software.
Democratization: Open-source MDM is vastly more democratized in nature than the proprietary MDM. It is accessible to organizations irrespective of their sizes, willing to execute small to large-scale MDM projects. The open-source nature brings with it the invitation to be used in any IT environment for managing data and delivering value to any scale. Companies can adopt the advanced algorithms, high-end functionalities that are at par with industry maturity and momentum. Also, since it comes with no obligations, contracts, smaller companies too can dip their toes into it, and decide accordingly.
11. How to find the Right Master Data Management Strategy?
The most effective way to arrive at a master data strategy is by starting in a reversed order. Begin with your dream MDM scenario — your ideal solution.
Key pointers before you begin:
- Needless to say, no work can start without developing and formalizing a vision of how an MDM solution will support your enterprise.
- To achieve long-term value, your MDM initiative must have a direct link with your business drivers.
- You must carefully observe your IT project plan to ascertain which projects and programs would touch master data and bring them on the same page with your MDM initiative.
The Core of Your Strategy
For optimal results and to be advantageous for decision-makers, CIOs, CDOs, and other data and analytics leaders, first, a standard definition of what constitutes as mission-critical master data (tied to business outcomes) must be devised.
A common set of attributes for products, customers, suppliers, services, assets, employees, and locations must be arrived at. Technology is mostly not the number one issue for companies; it is to secure buy-in from top management and stakeholders.
A new mindset must be nurtured where applications that were developed and deployed in silos, data that was housed in individual systems, and legacy practices of maintaining data are discarded. High-integrity and high-confidence in master data are at the heart of every MDM strategy. It’s only around sound master data that all the major business processes and applications will functions.
In most organizations, MDM solutions broadly fall under three categories.
Operational MDM: This is a solution that focuses on handling transactional data used by operational applications. They mostly depend on integration technologies and offer real value to the organization. It is, however, devoid of the ability to impact analytics and reporting.
Analytical MDM: Here, the primary focus is to offer the best analysis and reports about how master data must be handled by business intelligence (BI) technologies and data warehouses. They also have a lot of value for businesses but have no bearing on the operational part of the system.
Enterprise Master Data Management: A combination of both operational and analytical master data is what constitutes of Enterprise Master Data Management (MDM). Operational data improves operational efficiencies, while the analytical part offers the right picture of the way business is performing.
Keeping your immediate needs in mind, you must carefully choose the solution. An MDM process involves data modeling, metadata management, mapping, and semantic reconciliation of the data so that issues are resolved between contradictory data sources, and an authoritative data source is established.
Implementation must also be carefully carried out. You may choose to adopt a single subject area (single-domain) approach, like MDM for product data to support supply-chain management (SCM) or global data synchronization. Or you may be inclined to go for an MDM led customer data application to support customer-related goals.
Read our blog article about how to find the right MDM strategy!
You may even opt for a multi-domain MDM straightaway
Various master data implementation styles can be adopted, keeping your priorities in focus.
There are four implementation styles suggested by Gartner, for faster delivery of benefits and value:
Lastly, being successful with an MDM and to measure its success—is difficult. Keep your eyes trained on business metrics, not data metrics. Do not make the mistake of taking it as a data integration project. It is a lot more than that. For a start, you can begin with small steps, analyze business outcomes, and expand over time.
Ques 1: Data Management vs Data Governance
Ans: Data management is a business practice that helps in managing, including consolidating, enriching, maintaining data, and putting it to further use in order to achieve various enterprise objectives.
On the other hand, data governance helps organizations chart out policies related to data access, handling, security, and compliance to facilitate data’s desired use. It falls under the ambit of Master Data Management.
Ques 2: Master Data Management vs Metadata Management
Ans: Master data management is a business practice that involves consolidating, cleaning, and enriching corporate master data by including various business applications, partners, people, departments, key stakeholders, and business clients on the same page to achieve organizational goals.
Metadata management refers to handling the data belonging to digital assets that are being managed, such as image names, dimensions, and tags. It facilitates searchability, file management, asset organization and management.
Ques 3: What data to manage in MDM?
Ans: Data managed in MDM pertains to essential business data objects in various applications and systems like analytical data, transactional data, hierarchical data, unstructured data, as well as data about entities, locations, things, alongside connected attributes, metadata, roles, associations, taxonomies, and descriptions within an enterprise ecosystem.
Ques 4: What is MDM data model?
Ans: MDM data model refers to master data, their features, and their relations with other existing data. An MDM data model plays a pivotal role in keeping SKUs organized by category and family, so they can be conveniently accessed across the product’s complete life cycle.
Ques 5: Can small businesses implement MDM?
Ans: Yes, MDM implementation is possible for small businesses as well. As a matter of fact, the implementation for small businesses remains similar to how it’s done for medium and large enterprises.