What is master data management? Benefits, components, key strategies

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Modern enterprises have to maintain a number of systems and applications to manage various products and data of millions of customers. This makes data management inherently complex and fragmented.

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Important business data can change over time, all of which can be examples of diversity in the system. For example, if a sales representative adds an incorrect customer address for a new order, incorrect information will pass through multiple systems of the order fulfillment process until some level, such as the accounting department, introduces corrections. This will leave two data versions for the same sequence throughout the system – one true and the other false.

With thousands of transactions happening every day, acquisitions happening and customers updating their information, the problem has multiplied, causing a large chunk of data to be synced and a way to determine which version is true, false or outdated. Not at all. Using this data for analysis can lead to wrong decision making. This is where Master Data Management (MDM) comes in.

What is the main data?

Also known as the Golden Record, Master Data is the core organizational data asset that contains the most up-to-date and accurate information for day-to-day business operations. It supports transactional, non-transactional or analytical data and is generally distributed across departments to help employees analyze and make decisions about service, sales, marketing and other areas. Master data serves as a source of general data and often includes application-specific metadata and corporate dimensional data (which directly participates in transactions such as customer ID or department ID), although the type of data may vary depending on the organization and its needs. Is.

In general, master data should have three main qualities – less instability, more complexity and mission-critical. Also, it should be used frequently and reused. A good example is the contact numbers collected from customers. This type of data is less volatile, mission-critical and can be very complex, meaning that a small error in numbers can result in loss of business opportunity.

Master Data Management

The ongoing practice of creating and managing a master dataset for all critical information is called master data management (MDM). It encompasses all the technologies, processes, and people that help organizations integrate, refine, de-duplicate, organize, classify, localize, and enhance master data as a source of truth and then integrate it with all business processes, applications, and analytics platforms. Is. Use throughout the organization. When MDM is fully executed, the main data transmitted is synchronized, highly accurate and reliable.

The various domains of Master Data Management include:

  • B2B and B2C customer master data management
  • Product-specific master data management
  • Supplier-specific master data management
  • Location Master Data Management
  • Asset Master Data Management
  • Employee Master Data Management

In the early stages of MDM, each type of master data was required to have its own unique data store and business logic. For example, companies seeking to optimize their employee performance focus their MDM strategy only on the employee master data domain. However, these “single-domain” approaches are not as effective today as the data has become more complex and interconnected than ever before. Currently, in order to obtain complete customer information, an enterprise needs not only customer master data (demographics, etc.) but also product-specific master data, which may indicate their purchase preferences. This is where the “multi-domain” MDM, which manages all different types of master data in one repository, comes into play.

According to Aberdeen’s study, companies taking advantage of multi-domain MDM have seen better results than single-domain MDM in terms of data completeness and accuracy.

Importance and benefits of master data management

Drives efficiency

Efficient master data management gives the organization a single place for an authentic view of information, which in turn eliminates the costly redundancy that occurs when organizations rely on multiple versions of the data distributed in slid systems. For example, if a customer’s information has changed, the organization will update key data with new information and will not turn to sales and marketing efforts using old data points present in other systems.

Better data quality

With MDM, organizations also get access to better data quality that is more current and suitable for analysis. Discipline makes the data format more consistent and consistent, making it more useful for a variety of business processes and easier to answer basic questions, such as “What services did customers use during the last quarter?” Or “Who were the top buyers during the same period?”

Revenue growth, improved customer experience

MDM integrates complete and reliable customer data in one place, so organizations can use it to better understand their target audience as well as the connection channels of their choice. Then, using that insight, they can make a personal sale or cross-sale offer to the right person at the right time. This will not only help reduce revenue but also reduce costs on unnecessary customer acquisition methods. Problems such as sending emails to the wrong customers or calling about issues resolved can also be avoided.

Easy to backup

Centralized copies of MDM’s business-critical data also make it much easier for organizations to create backups. Backing up Silos is a costly affair, but MDM simplifies the process, giving organizations an easy way to recover their data in the event of a disaster or unexpected loss of information.

Instant product launch

Enterprise can accelerate their time-to-market with MDM. In particular, product and supply chain master data management organizations can help refine and enrich almost every aspect of the information needed to set and meet product deadlines. A study by Stebo Systems found that 58% of organizations reduced their TTM from months to weeks using their MDM solution and 36% reduced it from week to day.

Regulatory compliance

A well-executed MDM strategy, which facilitates the handling of customer information, can also help organizations comply with regulatory as well as privacy laws. Central management of customer master data will be especially useful in the case of the General Data Protection Regulation (GDPR), which gives customers the option to delete their data or, if necessary, port to another company providing similar services.

Master Data Management Framework: Key Components

  • Search: This aspect of MDM practice revolves around identifying where the master data resides. Organizations need to examine their entire data landscape to see which systems have pieces of business mission-critical data on-premises or in the cloud, the type of data and its quality.
  • Acquired: After finding the data sources, the organizations have to get all the relevant information. This is done by connecting the data distributed across all applications to the central repository – from the ERP to the CRM system.
  • Cleaning: After editing, all information collected from source systems is cleared, its overall quality is improved. To do this, organizations have to deal with data inaccuracies and make the data format more compatible for downstream use. Effective cleaning is important to make key data reliable.
  • Enricher: In this step, the master data profile created by connecting to third-party sources of trusted data such as Dunn and Bradstreet and Axiom is enriched. Prosperity sources can address system data imperfections.
  • Matching: After cleaning and enriching, the master data is matched to check asset duplication. Here, certain tools are used to take advantage of established business rules and to identify different records for the same item / person.
  • Merging: Duplicates identified by matching are later merged into single version or Golden Record. In essence, the results of the previous step are driven by the solution process, where the rules of survival are used to select the most accurate and relevant piece of information for the person or object concerned. If there is uncertainty around any data point, this process creates a set of tasks for the data stewards who can then merge manually.
  • Relative: At this stage the master data is ready, but it is not enough to use it. It should therefore relate to data points from other domains, such as supply chain, product or employee system information. This phase creates a multidomain MDM, giving organizations a 360-degree view of the data to work on.
  • Security: The next step revolves around implementing security measures to ensure that key data, including business-critical information on consumers, products and other areas, is protected from unauthorized access. To do this, organizations often cover up their sensitive data and limit who or what system has access to key data.
  • Delivery: Finally, MDM practitioners oversee the delivery of reliable, relevant, and secure master data from central repositories to appropriate applications. This ultimately helps people make analytics and other data based decisions.

Top Master Data Management Strategies for 2022

Groundwork for search

To ensure master search is complete, you need to do basic work to make the entire enterprise data landscape accessible. This includes granting access permissions to the MDM team and flagging any obscure sources of data that may not be directly available.

In addition, a significant portion of the time should also be spent on data profiling and usage patterns so that only relevant pieces of information are taken into the master data repository. Consultation with subject matter experts in business can also come in handy here.

See Multi-Domain Approach

Instead of creating and managing master data for a single domain, try moving to a multi-domain MDM approach. All of these functions will combine hidden data points – from product to supply chain – and provide a more holistic view of information to bring better results.

Execute with transparency

After deciding to switch to MDM, it is important to ensure that people who are asked to use Master Data are made aware of the changes that are coming and why they are being made. Implementation should not come as a sudden change. In addition, users of the main data should be given sufficient time to adjust to the changes and be given the option to share their feedback, ask questions and identify distances (if any).

Training is important

In addition to transparency, all employees and departments entering the Master Data Repository should be trained and re-trained on various aspects of formatting and data usage. There should be workshops to educate employees on how they can take advantage of master data to accomplish set business goals.

Observation and measurement are important

Institutions should implement MDM in phases instead of going all at once. Once the process is complete, project managers should continue to work with management, staff, and other stakeholders to discuss feedback and suggestions aimed at improving the system for better business outcomes. At the same time, they should measure the ROI of the entire post-implementation process, starting with the business case formulation, and regularly audit critical aspects such as installation, configuration, data model, and warning queues to avoid systemic delays when new business requirements and challenges arise.

Triage data issues and priorities

When conducting an audit, there is a good chance of participating in one or more data issues. Project managers need to anticipate this and be prepared to handle whatever comes their way. This can be done by implementing appropriate procedures for the triad, where the problem can be assessed and addressed depending on how urgent or complex it is.

If the problem is minor, it can be addressed immediately. However, if it requires a lot of manual work it can be shut down until launch. In short, organizations should have a set structure to address data quality and governance issues.

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