Master Data Management (MDM), is a set of tools and methods for managing a company’s data pertaining to its business needs, such as sales, marketing, and operational strategies. The function of a MDM is to ensure the integrity of a company’s data repository, so that all services can access accurate, relevant, and up-to-date data at all times, from a single point of access.
Today, all businesses have access to large volumes of disparate and dispersed data sets and information. These are either generated by internal software or external sources, such as Big Data.
Companies realize that they need to control, sort, and evaluate all this information to be able to exploit it with confidence.
According to experts, the implementation of a MDM is an economic and strategic advantage:
- A survey by Gartner reveals that organizations poor data quality is responsible for an average loss of $15 million per year,
- An IBM report to ABERDEEN Group, a company specializing in international market research, states: “Companies that use Master Data Management (MDM) are twice as satisfied with the quality of the data and the speed of delivery.”
In this article, we’ll see how Master Data Management simplifies business data management through the following topics:
- What is a company’s reference data?
- What are the functions of a MDM?
- Why implement a MDM?
- MDM and Big Data?
- Enterprise Application Integration (EAI), a tool for sharing and exchanging reference data.
What is a company’s reference data?
In computer science, data is classified as basic coded information: a customer ID, a postcode, an order number, an order date, etc.
A definition of reference data for Master Data Management
This reference data varies depending on the user, or the need for it. For an operator, for instance,it involves structuring business information: a customer account for a sales manager, an accounting account for a chief accountant, and so on.
For a business project manager, reference data is the information shared across several business processes. For example, a customer account is accessed by sales, marketing, and accounting services.
In the caseof an IT project manager, reference data is the data set used in various software solutions, such as Customer Relationship Management (CRM), Commercial Management, and Accounting.
The three main types of baseline data are:
- Master data, which contains key business objects: customer, supplier, commodity, employee, etc.
- Consecutive data that complements the master data: a customer’s address (street, zip code, country, etc.); the features of a commodity (color, dimensions, photograph, etc.).
- Value tables or billings: VAT rates, currency codes, country identifiers, postcodes, etc.
Within the company, all this data must meet established quality criteria, such as uniqueness, accuracy, completeness, compliance, consistency and integrity. But the data must also respond to operational issues. That is to say: to be up-to-date, easy to access, relevant, and understandable. Data records must also comply with security rules of the country in which the enterprise is based. In fact, they must be accessible only through authorized services and personnel. The company must also implement the logging of access and changes.
An example: Customer data
Let’s consider customer data, which is shared and used by CRM, Business Management and Accounting.
These three systems retain and use common information–such as, names and addresses–which complements the client entity. They also hold and update business information specific to their functions:
- CRM: exchange dates with the sales department, the name of the account’s salesman,
- Business Management: purchase history, pricing conditions,
- Accounting: financial accounts, outstanding payments, date of last payment.
The difficulty with this constantly changing information is to retain an accurate record of customers’ situations at all times:
- Who is responsible for updating customers’ addresses?
- How can we replicate an address change reliably, and in real time so that all departments have access to the updated record?
- How can we maintain the accuracy of common information between potentially different applications in different departments?
- Under what conditions do CRM[EP3] and Accounting Management have access to the clients’ outstanding payments?
The functions of Master Data Management?
Master Data Management is a set of tools, methods, and also rules and best practices that allow a company to manage its data.
A MDM has all the means to create a repository for the company’s structuring data. The scope of a MDM’s intervention is, therefore, enormous, as the MDM will centralize all functions:
- Data acquisition: direct entry, internal sources (application processing, connected objects, IOT), and external sources (databanks),
- Data validation according to common syntax rules, business management rules, and consistency rules,
- Quality treatments, such as research and removal of duplicate information, ISO standards, etc.,
- Identification of the data through a single client ID,
- Data security (a change of address which does not involve a third party[EP5] ),
- Data description: metadata updates,
- Transcodification: reorganization of the data between applications.
Depending on the type of data within the system, these functions will be performed directly by a MDM tool, an application that owns [EP6] the data, and grants access to users:
- A list of municipalities is imported from the The National Institute of Statistics and Economic Studies (INSEE) database and made available to applications that need it,
- Customers are created in the CRM and disseminated to Commercial Management and Accounting services,
- Any outstanding payments are updated in Accounting and also accessible by authorized personnel within the company.
Master Data Management will also be responsible for ensuring compliance with data management and retention times in accordance with existing data protection and financial regulations, such as the RGPD (General Data Protection Regulation) for personal data and for accounting and financial data, IFRS (International Financial Reporting Standards).
Why implement a Master Data Management tool?
Centralized management of reference data is good for the company.
In the absence of the centralized management of reference data, the company retains separate data archives. This is problematic because the data is likely to be in different formats, not always synchronized, and introduces the risk of false, inaccurate, and inconsistent data which costs the company time and money.
The principles of setting up a MDM:
- A unique and identified data source,
- The dissemination and provision of accurate data to data “consuming” applications (ERP, CRM, WMS, BI, and so forth).
The benefits of a MDM for your business:
- Operating gains from having access to quality data,
- Time savings to access accurate data,
- A complete overview of the company’s data set,
- Greater ease in implementing Business Intelligence analytics tools.
Master Data Management and Big Data?
The implementation of Master Data Management will also allow the company to exploit Big Data to its benefit.
Today with improved technology, enormous volumes of accessible data (Big Data) are of growing importance. This structured and unstructured data comes from the Web, Open Data (freely available to everyone), IOTs, etc.
However, Big Data is not in usable condition by the company, because it’s too numerous, most of it is redundant, available in multiple formats, and of unknown origin.
The idea, therefore, is to use an ETL (Extract Information Load) which consolidates the information contained in a “Data Lake” into a destination system that can be used as reference data. For example, the hashtag #MDM if the company wants to process and analyze posts on social media about MDM.
This information is processed, qualified by removing duplicates, inconsistencies are dealt with, the data is made understandable and interpretable, and can then be linked to company reference data to be used by marketing and sales departments, track customer behavior, measure brand awareness, make predictions, etc.
The EAI, a tool for sharing and exchanging reference data
The deployment of an Enterprise Application Integration (EAI) will allow the dissemination of reference data to be organized by linking applications within an organization together.
One of the main goals of an EAI is that all connected applications share the same data source. For each piece of data a data application source is defined. The EAI then disseminates the data via the EAI bus which allows different systems to communicate through a shared set of interfaces to the applications that request it.
Data transport and transformation provided by the EAI (ETL):
- Transformed into a pivotal format,
- Transported between different applications that provide/input the data and the applications that consume it.
The EAI will thus make it possible to define a single-entry point for the reference data.
The EAI also implements a “virtual repository” of reference data that is shared with all connected business applications.
Connection of the EAI bus to other tools/applications
So that users can share information, through this EAI bus it is also possible to connect:
- The company’s websites,
- Middleware, Web services for mobile applications,
- The Data Lake (a repository of data stored in its natural/raw format)
The implementation of an EAI in a company is therefore an important step in the implementation of a MDM project. It will both identify data, and set up the processing and loading stages. But what are the benefits for the company?
What are the immediate benefits of EAI for the company?
Setting up an EAI in an information system is tactical and strategic for the company. These benefits are characterized below.
- Obtain quality baseline data quickly,
- Speed up data delivery to consumer applications,
- Delete point-to-point interfaces and batches of data updates in different applications,
- Simplify the architecture of the existing Information System (IS), and end spaghetti syndrome with EAI flows.
- Gain greater agility to evolve the company’s IS,
- Simplify the integration of new applications,
- Facilitate access to external data sources (Open Data, Big Data),
- Enable the consolidation of subsidiaries and acquisitions.
In short, Master Data Management (MDM) offers many advantages. Beyond centralizing and updating data through the company’s data repository, the MDM ensures the uniqueness of the data that is collected, and eliminates the risk of duplicated data in your IS.
For more than thirty years, Tenor has been supporting its customers in managing their data flow through specially crafted EDI solutions and software, EAI, and dematerialization. If you liked this article feel free to check out the one on the definition of the EAI available on the Tenor Blog.