Improve Business by Using Master Data Management
Hello. My name is Nason. I am part of Data Management team in CIMB Niaga and my role is Data Architect. In this opportunity, I would like to share about “Master Data Management”.
There is no doubt in anyone’s mind that data is the most valuable thing an organization has. Aside from that, it isn’t enough to just store, process, analyze, and distribute data.
Information would have a major impact on decision-making when it is easily accessible, can be retrieved quickly, and is utilized in a practical way. This is where the idea of Master Data Management, often known as MDM, comes into the picture.
An Example Business Case to Justify the Significance of MDM.
Consider the case of a company that now operates in 10 cities but has plans to grow into more locations.
Initial product launches in the additional markets will be limited to the company’s top five products for consumers.
Then, a simple question arises: how will the business access all of this data?
To make good decisions, they need accurate and detailed information about their current geographical presence, performance analysis across geographies (sales volume, future demand, revenue generated), best-selling products for the masses in different geographic locations, and forecasts of new sales.
It is totally dependent on ability to obtain accurate, timely, thorough, and efficient data in order to determine whether or not the growth choice will be financially and accurately feasible going forward.
The only thing that can satisfy these key needs in such circumstances is MDM.
Why Master Data Management is Important?
When it comes to business, MDM has a profound impact on practically every key area of the organization’s operations.
The most important benefit of master data for organizations and their enterprise-wide information systems is that it helps them to identify the many components involved in their day-to-day operations.
For all business-critical procedures, this information must be processed and documented in order to preserve a single source of truth.
Businesses’ decision support systems and organizational members benefit from MDM because it allows them to make quick judgments based on objectively exact information.
To do this, every element’s identity must be specified and agreed upon across the whole organization.
This guarantees consistency in the identification of these essential parts throughout routine operations and helps to reduce misunderstanding and mistakes as much as possible.
Organizational processes may also be accelerated and optimized through the use of data that is consistent across all platforms.That way, we can ensure that communications are understood and acted upon as soon after they are sent.
Another critical reason for the need of MDM, particularly in large enterprises, is to guarantee compliance with applicable rules.
Government agencies have enacted special legislation to regulate how businesses handle and process data.
Following and developing standardized MDM practices can assist businesses in aligning their processes with the requirements of these regulations.
What Common Data Problem Can be Answered with MDM?
There are several data-related problems that can be addressed by implementing master data management.
They are as follows:
- An inability to establish cross-domain relationships — Domain masters (such as customers, suppliers, products, and so on) frequently require information on the relationships between them.
It makes it difficult for business users to receive operational intelligence, and as a result, it makes it difficult for businesses to manage interconnected business processes. - Lack of reliable data throughout the organization chain — Organizational data that exists in numerous versions and is spread across multiple locations, functions, and systems makes it difficult to maintain a single version of the truth.
This occurs as a result of a lack of an integrated strategy to information management, which is prevalent today. - The absence of data governance and process orchestration — Organizations are struggling to preserve data security and integrity as a result of a lack of collaborative data authorization, according to the CIA.
When business users try to manage and approve information in accordance with organization standards and processes, they run into difficulties. - Validity of data modification — Keeping track of and keeping logs for prior versions of data is a constant issue for organizations, and it has the potential to negatively influence the authenticity of business operations significantly.
Organizations may easily alleviate all of these issues by properly maintaining their master data records.
It synchronizes all external and internal systems in order to keep the master data clean and consistent throughout the business, as well as between departments.
Master Data Management Implementation Style
In general, Master Data Management implementation follows to one of four styles, which are as follows:
1. Registry Style
The first style is Registry style, which is commonly used to discover data duplication by running cleansing and matching algorithms across a variety of different source systems.s.
This style does not return data back to the source systems, updates to master data are still done through the current source systems.
That data is cleaned and matched using the cross-referenced identifiers instead of relying on a source system to regulate the quality of its own data.
It enables low-cost, rapid data integration while also incurring the least amount of disruption to application processes.
It is typically adopted as an index to master data that is written in a distributed way and continues to exist in a scattered state across distributed systems.
2. The Consolidation Style
The next style we’ll look at is Consolidation. Consolidation techniques generally combine master data from various sources in the hub to generate a single version of truth, or “golden record.” Any changes made to the master data, on the other hand, are applied to the original sources.
The master data from a number of different systems are consolidated into a single controlled MDM hub, then this data may be cleansed, matched, and combined to create a single record for master data domains.
It is cheap and easy to set up, facilitating enterprise-wide reporting.
This style is utilized for analysis, providing a reliable data source for reporting and analytics.
3. The Coexistence Style
Coexistence style similar to Consolidation style. However, unlike Consolidation, MDM is maintained in the central MDM system, which is then updated in the source systems.
Coexistence can be more expensive to implement than Consolidation since master data updates might occur in both the MDM and application systems.
This approach manages data in source systems and synchronizes it with the MDM hub, so data may coexist and present a single version of the truth.
This technique enhances master data quality and access, where master data is distributed or returned to the source system, but a “golden copy” is retained centrally in a hub that is accessible to all parties involved.
4. Repository Style
Master data for a company can be kept in a single database in an Enterprise, Centralized, or Transactional Style implementation. All of the attributes required by all applications that use master data are stored in a single database, all of which are saved in a single database.
Over time, this kind of framework assures precision, consistency, and efficiency. Because MDM performs all of the functions, there is no need for the application system to be involved, which reduces the amount of time required.
The primary advantage of this style is that our master data is always accurate and consistent, and the Transaction style hub can manage data attribute-level security and visibility policies. Consequently, a centralized data repository containing master data for multiple domains is made accessible to the user.
The Transaction style usually develops from the Consolidation or Coexistence styles.
MDM Challenges and Solutions in Banking Industry
Banking involves various data and complex business procedures, and they create, use, store, and access more data each year.
With the growing of new digital banking such as, internet banking and mobile banking, this causes companies exponentially retrieve large amounts of data and more various type of data including unstructured data.
However these data are fragmented, handled by various stakeholders, and monitored at a departmental level, not an organizational one.
This causing combining data from Retail, Loans, Deposits, Credit, Card, Payment, etc. systems is difficult and slowdown company decision-making.
In the other hand, technology and communication improvements have changed customer dynamics, making finance a customer centrist business that focuses on client interaction.
But, here the questions:
• Do Banks “know” their customers?
• Are banks providing a “multi-channel” interaction to the customer?
• Are banks offering relevant product to the customer?
The other challenge is that banks must comply with BASEL and Local Financial Services Authority. Bank and Financial institutions that can’t meet these data criteria incur significant fines. Without a trusted master data repository, data can’t keep up with requirements.
These are how MDM helps banks to deal with those challenges:
- Build trusted customer master data. By merging data from several source systems, MDM assists banks in creating a major central repository of client data.
This facilitates obtaining a comprehensive picture of customers’ actions, purchases, and so on with the bank, hence boosting client authorities’ ability to nurture customer relationships.
MDM integrates and de-duplicates client and product data to provide a single source of truth, providing for dependable and high-quality data. - Detect Fraud Early. Despite all of its benefits, the digital realm presents a significant challenge for banks in dealing with fraud and scams.
MDM assists banks in understanding client spending habits, customer abnormalities, and so on, allowing them to detect fraud at an early stage by cross-verifying changes.
Through MDM, banks may gain a better knowledge of their clients’ behavior, avoiding fraud and establishing regulatory compliance. - Remove Compliance Risks. MDM helps businesses to learn and reduce compliance risks by assisting enterprises in centrally maintaining data quality.
Each MDM solution on the market has capabilities that enable enterprises to identify and resolve data quality concerns.
This effectively guarantees that clean and correct data is delivered to inspection teams on a constant basis, reducing regulatory fines. - Boost Business Profitability. An MDM solution meets the objective of increasing revenue and margins by serving as a central repository for all data.
MDM assists in understanding particular client demands in order to give better services to current clients, as well as tailored services in order to fulfill new consumer requests.
MDM enables marketing teams to optimize cross-sell, up-sell, and product bundling offerings, assisting banks in improving customer acquisition, increasing customer income, lowering acquisition and retention expenses, reducing customer attrition, and increasing product sales.
To sum up, a MDM platform may improve business and encourage long-term cost reduction with providing organization with a structured, complete picture of the master data while also organizing and streamlining internal operations.
A well-defined MDM approach, along with improved data management technologies, gives a thorough understanding of the customer, fostering strong partnerships that contribute to agile and responsive operations.
References:
