Master Data Management Best Practices and Strategy for Enterprises

What is Master Data?

Master data is a core data that refer to the business information shared across the organization. It consists of the structural and hierarchical reference, which is essential for a specific business. Eventually, it remains constant, but we need it to update regularly. Now a day’s data is valuable. When information is managed correctly, it supports a company to achieve a specific set of goals as master data represents all data, which in return provides quick and accurate access to the data.This article will give an overview of Master Data Management best practices .The information present in the master data varies from industry to industry. Master data comprises three types:

  • Reference Data
  • Enterprise Master Data
  • Market Master Data

Reference Data

Reference data are classified schemas (data that is organized in the form of classes or groups) refer to applications, data stores and processes.It includes code list, status, codes, flags and product hierarchy.

Enterprise Master Data

It represents the single source of market data used across multiple system applications and processes regardless of locations.

Market Master Data

In contrast to the enterprise master data, the single source of market data used across market places regardless of locations.

Usually, master data is non-transactional, but in some cases, information is contained in the form of orders, and receipts it is considered as master data.


Also, the fact is that several financial and banking services companies are under regulatory severe, for example, banks, which are directed to the Beneficial Ownership Rule.

Also, the fact is that several financial and banking services companies are under regulatory severe, for example, banks, which are directed to the Beneficial Ownership Rule.

Master Data Management

Master data management is the core process, which is used to Management, centralize and organize data according to the business marketing and operations. It regulates your master data over the company. Master, data management solution, helps in creating a combined ecosystem for the company and IT to work in sync with each other.

A company with master data management solution assures consistency, correctness, capability required for all company operations significant to suppliers, clients, partners, prospects, and employees.

Why do we need master data Management:

Many organizations face data-related issues as they grow and prepare for data insights. And it results in inefficiency if the prepared data is error-prone.

Master data management consists of tools and Management that co-ordinate and organize data across the enterprise, which helps to access accurate information in the organization.

It helps in managing the critical portion of the data and provides data integration as a single source.

It may be stored in a single repo, but when accessed by multiple functions, data is stored at various places in the organization.

As master data is accessed by multiple applications error in one form can also cause failure in all another application that access master data.

Who should be involved in MDM program:

Data Governance – Individuals who drive the definition, specifications, and solution. These users help administrators to understand what to create, and data stewards know what to control and how to control it.

Best Practices for Big Data Governance From the Article: Big Data Governance Tools and Best Practices

Data governance users talk to data stewards how data should be handled, including the methods for doing so, and then check the data agents responsible for serving those requirements. Data governance users also need to maintain a feedback loop from the MDM software to ensure everything is working as expected.

Administrators – Individuals in IT who are responsible for setting up and configuring the solution.

Data Stewards – Individuals responsible for fixing, cleaning and managing the data directly within the solution. Ideally, data supervisors/Stewards come from departments across the company, such as banking and marketing. Typically, the projects that data supervisors take on within the MDM program are set by data governance users.

The architecture of Master Data Management

Master Data Management follows broadly follows three frameworks which are:

Registry Architecture

In this type of structure, the system is granted only read-only access which states that unwanted persons cannot modify the master data in any way. This framework is beneficial in finding redundancies in the data.

Hybrid Architecture

In this, the system has the power to change or modify the master data. This speciality helps to achieve quick access and also as the order is having the capability of modifying the data, then the quality of information also gets improved.

This type of framework allows both the Master Data Management system and application system to work in collaboration. Its downside is, the cost for supporting this type of structure is sometimes high as it is not very easy to change and modify the master data. Its main aim is to centralize the master data and achieve consistency.

Repository Architecture

This type of framework assures stable consistency, accuracy, and efficiency. In this, there is no overhead of application system as all the functioning is done by Master Data Management itself, thus reducing the time taken.

Functions of Master Data Management

There could be numerous functionalities of a particular root management system, but the following are the main essential ground functionalities for every master data management system are:

1) Content – it is undoubtedly all about the material because everything is inter-connected between the source data and the system trying to fetch, i.e., the content accessed should be distinct, and there should be no duplicity.

2) Relationship – this is a crucial functionality because we are trying to get access to any data in the system it should have some type of relationship hierarchy with any other parent branches of that part of the system.

3) Access – it is another primary function of master data management since we must have a clarity about the type of data we’re looking for and also the knowledge of who gets access to it is also a trusted member of the organization, not following this and allowing a non-privileged member to access this might lead to the misguidance of the data.

4) Change Management – this means that whatever data you are looking to operate on should be readily available for change under authorised regulations and the change should be managed efficiently every time prompted.

5) Processing – this can be evaluated as the back end flow of the data by which we mean the flow of processing of data in either way accessing or injecting should be quite smooth and error-prone and are used for measuring the certain constraints such as quality clinical care, cost incurring, patient safety etc.

Master Data Management Strategy

There are numerous best practices your enterprise should respect when developing and executing a master data management strategy

MDM isn’t one and done – Master Data Management strategy should be made into the foundation of your company. If data alignment is considered only a one-time occurrence, you will find the same data mismanagement problems frequently.

Buy – in at the top: For MDM to be successful, leaders within all company units must be involved in the development of the strategy, as well as continuously connected in continuous governance discussions.

Knowledge is crucial – On the other hand, all employees and departments must be trained and regularly retrained on how to format, enter the store, and access data.

Humble start, but think big – When operating out a new MDM strategy, you want to first focus on a smaller data set that may be effecting some current business pain. Done this way, you can confirm buy-in for a more significant rollout.

Don’t neglect to modernize– Your MDM strategy must include daily, synchronized updates to assure that your unique source of data has the most accurate information.

Benefits of Master Data Management

]It offers data consistency (changes that should be made on data) and eliminates redundancy for better and consistent workflows.

Practical data analysis to discover useful information to prepare a better business model regarding what type of data goes into the Management for beneficial business needs.

Providing multiple platforms, as business run on various platforms, should be accessible to the data, it can be done, and data is made available on physical, online and cloud.

Master data management comes with efficient backup options to retrieve the lost or corrupted data during the times of disaster.

Eliminates the sparse quality data by making the data more securable and can be accessed from a single point.

Cutting edge methods are used in defining and managing the crucial data of the organization to provide integrity to the data. And also removes the unaccessible and unreadable stored data.

The core features of data management include cloud, BPM(business process management and data integrity.

Different applications, information and process, can be integrated at a faster rate.

Master, data management is essential for large and complex business which continuously expand over the time, as it makes sure’s to keep the business data up to date and making it accessible by storing it in a single source.

Master data Management in the Supply Chain

Master data management lies at the core of the supply chain, as the consistency of data and performance being the primary factors, Industries with the supply chain are looking for master data management.

As master data management can help supply chain to achieve data consistency, supply chains are highly dependent on MDM to track and manage the supply chain data.

Master data management helps in cleansing and validation of the data and also provides an exact image of customers products and suppliers.

It provides homogeneity and transparency, and it acts as a bridge between the supplier systems.

The better the transparency, the more convenient to cut the costs and in result, better revenue generation.

Helps with cross-organizational data alignment and achieve reliable data for the enterprises.

Master Data Management in the Banking Sector

Methods and practices that collectively provide a company’s critical data elements enabling the whole organization to perform effectively. Well implemented, an MDM solution synchronizes master data secured within applications across the enterprise, distinguishing the essential data that accurately represents business entities such as clients, employees, products, and facilities.
For banks looking to improve their (KYC) Know Your Customer abilities to handle risk as well as to improve customer service. MDM provides a single, reliable repository for managing and sharing accurate customer data for all lines of business and different systems.

Over the past two decades, businesses have become more reliant on data to streamline processes and compete more effectively. Considering the kind of business intelligence (BI), analytics and AI results depend on the quality of data; master data management can help by –

  • Eliminating incorrect data
  • Removing redundant data
  • Combining data from various data sources
  • Normalizing different data so the data can be used more effectively
  • Allowing a single source of reference (single repository)

Read more on MDM in Banking Sector here.

Master Data Management in Data Lake

The most extensive cultured use of Master Data Management is to grant a much-needed meaning for big data.

Approaches to deal with master data in a big data lake:

  • Supply mastered data into the Data lake from the MDM hub
  • Acquire data in the data lake itself

In the first method, organizations use an MDM hub to master the data. The MDM hub makes the quality of core data better that is supplied into the data lake. The standard approach to do so is that we create a customer hub that performs as the single entrusted source for the complete customer data, which includes the relationships among customer accounts and contacts.

Trusted data goes from the hub to the data lake as well as every software and consumer touchpoint and combustibles the Total Customer Experience initiative that’s at the heart of EMC’s operations.

But the organizations have another way as well, which is by mastering the data in the data lake itself. This allows data scientists to spend more time traversing and analyzing and less time trying to resolve data issues, such as multiple copies of customer records. It also helps data scientists understand the relationships between the data.

How is MDM beneficial in a data lake?

Enterprise Centric

Provides flawless information across multiple channels – Genuine product data is essential to adapt your business and provide customers with a progressive and personalized shopping experience. MDM supports companies manage product data from varied sources. It creates a master record of validated, high-quality individual product data for effective distribution to all sales channels, whether data is structured or unstructured.

Unites the Data Attributes

MDM’s data integration tool helps to link data with various format attributes from diverse data sources. It delivers a single consolidated view of all your data instead of data in silos.


Enhanced customer service –  MDM provides an earlier unavailable event to interact with your customers during every step of the transaction, process and develop your performance based on real-time feedback by dropping variances and errors that impact product delivery from first app interaction in shipping, transportation, and feedback.

Gains trust in data – Bad quality data can produce negative repercussions on customer relationships, enterprise choice-making, and forecasting. MDM system gives high-quality data for quality decision making.

Supply Centric

Faster Deployment – MDM data repository supporting development units, apps and increases speed through the delivery pipeline incomparably faster. This means MDM results unearthed today can potentially be put to work in software today, rather than after some extensive review and recode process.

Filter Data with Data enrichment – Data enrichment leads to the mechanisms and processes that enhance the quality of data coming from any amount of different inbound roots.
As an outcome, you’ll have more robust customer and product records, giving you better insight that will allow you to make proactive business decisions that have a higher degree of success.

Challenges in Data Lake

Data swamps – A data lake receives any data, without overlooking or governance. Without describing metadata and a mechanism to maintain it, the data lake risks changing into a data swamp. Organizations have aimed to use data lakes as more than just near-endless repositories of data. The outcome is that they end up keeping data in data lakes. The data ends up just sitting in the data lake and is seldom used.

Unproductive data – Data can fester in data lakes. As a result, the method to obtain signals from it is cumbersome and the data is never new enough or related in real-time, to be put into production. So, the data in the data lake remains in navigator mode.

Shortage of business impact – The problems that businesses have encountered with data lakes is an imbalance between the meaningful investment they’ve made in data lakes and the corresponding lack of impact that data from the data lake is having on business settlements. Corporations must authorize managers to act and make decisions based on analytics from the data lake.
Master Data Management in the manufacturing sector

Benefits of using MDM in the Manufacturing Sector

Enterprise Gathering effective information

Master Data Management helps manufacturers to get know about what is in trend, or you could say what technology could be used to get out the best product and what needs to be done to fight against the competitors.

Increase in partnership

Master Data Management helps manufacturers to increase the interaction needed between them and the vendors. As vendors play a crucial part because once after creating a product, it is upon vendors who will pass it forward into the market.

Supplier Customer Satisfaction

If you have a quality product, then you don’t have to push on anything else because just by having quality in your product you are gaining their trust or more broadly increasing your reliability.

Increased Interaction

If you are supplying a proper and effective product, then there is a high chance of creating or earning new customers and taking reviews from them and passing them to the manufacturers to look into them.

Customer Quality Assurance

Quality is getting assured if Master Data Management is getting used in that product because Master Data Management is keeping a check from the first that there should not be any compromise with the quality of the product.


In the end, it is all about gaining the trust of the customer. If you are successful in doing that, then you don’t have to worry about your customers till you are providing them with a quality product which in comparison to your competitors is better.

Master Data Management in the Insurance Sector.

Master knowledge management is meant to confirm that enterprise knowledge has been de-duplicated, cleansed, organized, secured, and assessed for quality.

Every state within the United States maintains its own distinct insurance rules, and these laws and policies vary widely across totally different insurance lines of business.

Additionally, many states maintain regulations that govern nearly every side of insurance company operations, including the number of economic reserves an organization must conserve, what quantity brokers and agents will charge for his or her services. The way corporations will market their merchandise. Moreover, most states impose strict reportage necessities by that insurers should often document their compliance with the varied statutes.

Smart insurance firm leaders are currently seeking technology investments to establish sensible governance models that may successively make it easier to document and maintain restrictive compliance and lower operational risk. Master information management is precisely this type of investment.

It ensures that relevant information is validated as complete, correct, and consistent once it is circulated for consumption by internal or external business processes, applications, or users. MDM is an efficient answer to compliance challenges as a result of it will give insurers with consistent, complete, and correct information on customers, products, operations, and financials—even once it is captured and keep in several systems.

In effect, MDM makes it possible for insurance companies to beat error-laden, redundant, or siloed information to make one correct version of the “truth. The static insurance industry is transforming with Data Analytics.

Clients need a sure advisor who will facilitate them get the insurance they want. Data Analytics will assist brokers in fulfilling that role. Intelligent insurance management platforms are currently permitting agents to use technology that provides them with unjust insights supported client knowledge.

Intelligent management platforms feature sensible dashboards that you will simply access as an agent to get a complete summary of every client’s portfolio. If one of your clients has a gap in coverage, the system can automatically warn you and provides you with a chance to bring added worth to your client.

Rather than blindly cold-calling, you simply decision once you grasp your consumer is truly missing one thing they have. Not solely will this cause the consumer to desire you’re looking for him? However, you furthermore may get additional sales?

Data Analytics helps brokers and agents to automate the process of smart recommendations to the customer at the same moment when they are buying the new policy or making the changes to n existing one system.

  • How beneficial their book of business is?
  • Data Analytics can figure out insurance businesses
  • Change deals practices to improve those benefits
  • The wasted time is reduced around with policyholders
  • The profitability of agent and customer gets increased
  • Expand complete performance

A Data Management Approach

Master Data Management helps Enterprises to Eliminate poor quality data, Improved decision, enabling Superior regulatory compliance.To know more data management we recommend taking the following steps –

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