Hi Mullapudi,
CAPABILITIES OF MASTER DATA MANAGEMENT
1. Synchronization:
An MDM layer improves the ability to share, consolidate, and analyze business information quickly, both globally and regionally. And it makes it possible to rapidly assemble new, composite applications (software that combines the elements of a business activity in a coordinated application and user interface) out of accurate master information and reusable business processes. For e.g. helps to maintain Universal dimensional structures for multi-organizational regulatory compliance.
2. Reduced time to market.
MDM provides a single system for creating and maintaining product information, promotions, and rich, accurate consumer communications via online and traditional channels. One major retailer used MDM to reduce the time to introduce a new product from four weeks to one day.
3. Supply chain improvements.
A single, accurate and agreed upon definition of products and suppliers, made possible by MDM, eliminates duplication, increases buying power, and provides insight into supplier relationships. A major European retailer is combining MDM with Web Sphere Portal to allow more than 30,000 suppliers to manage their own master information
4. Revenue increases.
MDM contributes to better customer relationships. By creating a single 360 degree view of customers, MDM helps sales, marketing, and service teams better anticipate customer needs, provide targeted offers, and improve customer service. Revenue is increased by, for example, from providing more accurate and comprehensive information to the right customers at point of sale.
5. Realize Internal Efficiency
An MDM layer enables companies to realize internal efficiencies by reducing the cost and complexity of processes that use master data (through fewer code clashes, less data duplication, better control over business processes, and so on). It reduces manual translation and analysis to improve repeatability and speed to insight.
6. Finance and Operations:
MDM can lead to more accurate reporting and BI, more efficient planning and budgeting and enhanced ability for regulatory compliance.
7. Sales and Marketing:
MDM can provide a "single view of the customer," leading to greater customer satisfaction. For example – Spend Analysis is one the trend that is used in Sales and Marketing with the help of MDM, say for example if we say HCL supplies computers to Wipro Technologies and also to Wipro InfoTech, now if HCL uses MDM than the data would be consolidated that is it would see that in total how many computers are being send to Wipro and accordingly HCL can plan out its business with Wipro like how much discount it has to give and all other business related issues.
8. Procurement and Supply Chain Management:
MDM could mean reduced delays in shipment and fewer errors in item coding. For Example – Say we need to deliver a product to out Customer and if we have reliable and correct data, then we can send the Right Product to Right Place and at Right Time, because of which our delay in shipment would be less and it would also reduce the extra cost which the company has to pay for wrong shipment.
9. Product developers:
MDM means faster product launches and a better handle on customer feedback. For Example- We have heard about the companies like Maruti which deals in Cars, now say if Maruti comes out with a new Car Model, then it generally happens that many Customers do the Advance booking for that particular car, now if the data is not maintained properly then Company would not be able to release the product in Time and also this would affect its image in the Market, however if the Product launches on time we will always
get good Customer feedback which would in turn increase the reputation of our Company.
10. Customer Data Integration (CDI):
It is the process of consolidating and managing customer information from all available sources, including his contact details, customer valuation data, and information gathered through interactions such as direct marketing. CDI ensures that all the relevant departments of the company have access to the most current and complete view of customer information available.
11. Interpretability:
Interpretability, or the ability to recognize and understand data, is one of the biggest challenges to implementing successful data quality programs. In many cases, stored data exists in a wide variety of formats. Also, the individual elements within a given record have not been separated or may contain misleading data. Before any data quality processes can be implemented, the source data must be parsed into the appropriate components so that it can be understood by data quality systems. In addition, certain data elements may need to be moved, deleted or formatted.
12. MDM maximizes the return on SOA Investments:
Much of the hype and attention in service-oriented architecture (SOA) deployments has gone into web service creation and deployment and management standards and technologies. However, if data is inconsistent across applications, composite applications and business processes that cut across multiple systems and departments will not yield the desired results and benefits. For example, a composite application in a large multi-channel financial service institution that calculates a customer’s global credit risk will only work if that customer is described in a consistent manner across retail banking, brokerage, mortgage, and credit card systems. In a retail environment, a composite application that gets a customer’s order history from a data warehouse and recommends a related product requires consistent product and customer information across all the relevant systems. On a smaller scale, even business services to update an address or provision a service require semantic consistency of master data across customer relationship management (CRM), billing, and product systems.
Effective MDM helps organizations realize the full ROI potential from their SOA and business process management (BPM) investments.
13. High-end scalability:
Scalability is the growth capacity of a system. If a company’s data volumes, transaction-processing rates and number of simultaneous users are modest and static, then most products should be able to meet those scalability requirements. But that is not the typical description of an MDM candidate.
Global enterprises with immense amounts of master data, millions of records, tens to hundreds of client business applications and thousands to tens of thousands of users are more the norm. Master data management is a large task. Smaller vendors with lesser-known products simply will not have the capability to support the capacities required.
14. Customer satisfaction due to effective and efficient supply chain management.
15. High productivity due to effective inters operability in company.
16. Effective formulation of policies in company in respect of vendors and customers.
17. In the absence of an MDM function, each system, application, and even department within the enterprise collects its own version of key business entities. Like the collection of customer data. Key attributes such as customer name and address information are collected repeatedly throughout the enterprise. Unfortunately, it is rare that this gathering process produces the same or consistent data about customers. This leads to the critical difficulty (aside from the storage costs of it) with such redundant data – poor data quality. According to a report from The Data Warehousing Institute, “Data Quality and the Bottom Line” by Wayne Ackerson, corporations lose more than $600 billion a year due to poor data quality, and most of that cost can be attributed to redundant and low quality master data.
18. Organizations constant changes as new products and services are introduced and withdrawn, companies are acquired and sold, and new technologies appear and reach maturity. this changes can be managed effectively in MDM and can be reflected every where in the company data.
19. Organizations can build a complete view of their customers, products, and other critical business entities.
Regards
Richa
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