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Master Data Management often, but wrongly, has the reputation of being a monolithic and challenging cost center. The truth is different today: MDM is more accessible, pays for
Master Data Management often, but wrongly, has the reputation of being a monolithic and challenging cost center. The truth is different today: MDM is more accessible, pays for itself and serves as the basis for a data strategy in which AI can play an important role.
AI is the hype of the moment, but no AI without high-quality and correct data. Master Data Management (MDM) is the name for a strategy that ensures consistency, accuracy and traceability of all company data across systems. Such a data strategy with a single source of truth for all critical data is essential for any company with AI ambitions. Even without AI, MDM has many concrete advantages, especially in acquisitions or divestments:
Reason enough to choose MDM, even if there is still a perception problem today. “MDM had a reputation for being monolithic and costly,” said Sumit Jha, associate director and data management lead at Cognizant. “Organizations view master data management as a complex project that involves many changes and is very slow to deliver concrete values.”
Jha acknowledges that the situation used to be like this, but points out an important development: “MDM projects only delivered value after months or even years. However, everything has changed with the introduction of SaaS. MDM is part of a larger whole and the monolithic aspect has disappeared. With SaaS, you can start an MDM project small and reap the benefits in just a few weeks.”
“If you have a 500-page book with all sorts of information, MDM is the index of that book,” says Jha. “If you need something, you know where to go.” He illustrates the role of master data management in a modern company. “You need MDM if you have more than one system, such as a customer database, an ERP system and a CRM. This creates different records pointing to the same entity. The first step is to standardize this so that there is only one data set for all systems.”
A simple example makes it clear: ITdaily could be a customer record, a partner for a marketing department, and a supplier in a CRM system, all from the same company. By merging these data sets so that instead of ITdaily marketing, ITdaily customer And Daily deliveries only ITdaily exists and the three domains are linked together, you get a single source of truth. All data about it ITdailyThe records are now in one place, with correct spelling, addresses, phone numbers… “But that’s not MDM yet,” warns Jha.
He continues: “True master data management begins when you take the right steps to ensure the data set is never split again and the data remains accurate across all systems.” As a marketer, the address of the headquarters of ITdaily This means that this must also be visible in the protocol ITdaily in the CRM system.
“The migration to SAP S/4 HANA has accelerated the adoption of MDM,” believes Jha. “In S/4 it is important to keep the data clean. Additionally, migrations run more smoothly when the data is cleaned. The manufacturing industry relies heavily on SAP, and this approach helps.”
But technology is only a small part of an MDM strategy. “A small part of the work is technically complex,” estimates Jha. “The rest is about people. MDM is not about an app that you roll out, but about people and processes. You need active participation from the company. There has to be enthusiasm.” He estimates that a mature MDM strategy will result in a two percent increase in sales thanks to better insights and greater efficiency.
MDM is extremely relevant in takeovers and mergers.
Sumit Jha, associate director and head of data management at Cognizant
“MDM is extremely relevant for acquisitions and mergers,” emphasizes Jha. “When merging multiple ERP systems, CRMs and other databases, master data is a must. This also applies in the event of a company split. You need to know what data you have, where it is and who is responsible for it.”
Getting started with MDM is easier today than it used to be. “You have to do your homework,” says Jha. “Where do you want to introduce MDM first? And why? Only when you have answers to these questions is it time to develop a strategy. Also, measure the problems before you start the rollout, otherwise you will have a hard time explaining the benefits to management.”
If you have a recording ITdaily and another with a typo such as: ITdialy, which contain similar information, it is certainly useful to group them together under the correct name, but this action alone is difficult to translate into value. Jha: “Examine how much time is wasted, how many customers are not properly addressed, or how much unnecessary work is being done.” With MDM you can improve these parameters.”
Jha gives another example. “Manufacturing companies have to correctly identify their products and have problems with this. However, correct data about products has a big impact. When a customer calls the help desk with a problem, the help desk agent, who has access to all relevant information about a product and a customer through MDM, may be able to resolve the issue over the phone. Without MDM and access to the data, the alternative is to send a technician. It’s clear which method saves the most time and money.”
A good data strategy without silos and duplicates opens the door to further applications. Jha: “Products also need to be classified correctly in the manufacturing industry. It is important whether they are green or not, for example, or whether they fall under certain regulations. This is typically a manual and time-consuming process, but anyone who gets their affairs in order with MDM can apply generative AI to the data, saving a lot of time and effort.”
Master data management is not an end in itself, but a means. A large organization without MDM is like a giant room with stacks of books in it. With MDM, this room becomes a library. Furthermore, MDM is never finished: it is an endless process. If you give up managing the library completely, you’ll soon end up with a messy stack of books where you can’t find anything.
If you stop when your data is good, it will take a year until twenty percent of your data is bad again.
Sumit Jha, associate director and head of data management at Cognizant
“If you stop when your data is good, it will take a year for twenty percent of your data to become bad again,” Jha estimates. “After two years, that’s more than 60 percent.” The quality of your data quickly deteriorates. You have to maintain MDM.”
That shouldn’t scare you off either. In fact, MDM is simply a tactic for creating order out of chaos and structurally maintaining that order. It is therefore not surprising that companies operating in the life sciences have a very high level of maturity. “Everything is organized by MDM,” says Jha. “Right down to employee salaries. When you’re subject to a lot of regulations, you need to be able to track everything. You can imagine how valuable master data is in such a case.”
“There are many companies that are just starting out with MDM today,” reiterates Jha. “Start small and expand. Pause if you don’t see added value and expand the project if the added value is there. And don’t drive too fast,” he concludes.
Source: IT Daily
As an experienced journalist and author, Mary has been reporting on the latest news and trends for over 5 years. With a passion for uncovering the stories behind the headlines, Mary has earned a reputation as a trusted voice in the world of journalism. Her writing style is insightful, engaging and thought-provoking, as she takes a deep dive into the most pressing issues of our time.
