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The more your business grows, the more your data grows and the more you need to consolidate.


FinancialPharmaceuticalInsuranceManufacturingMetadata Management

    A Public Manufacturing Company in New York


The Client, a manufacturing public company based in upstate New York is involved in diverse business such as Glass-Tube Manufacturing for LCD TV, Life Science, Cabling system, etc and has branches, subsidiary, distribution offices throughout the United States and has huge presence in 17 countries globally (Japan, Austria, Germany, UK, France, India, Korea, etc).

The company started off as Glass manufacturer and began diversifying and acquired multiple lines of business when it became a Public company. A cash-rich company so involved in the expansion started making new customers and acquired several existing customers from the newly formed subsidiaries or acquired companies.


Owing to this rapid expansion and huge customer growth, the Client inherited multiple disparate systems and applications and their Sales, Marketing and IT departments exploded with new hires with newer opportunities.

Technology Stack:

v  Cable Systems: 17 SAP dissimilar environments (4 versions)

v  Life Science: Oracle and home-grown applications

v  Glass Mfgr: Peoplesoft ERP, SCM, CRM

v  Corporate: Peoplesoft ERP, CRM, SCM, & EPM


Problem #1:

The Client had established credits to their customer organizations and had put a cap on the credit limit based on the Customers worth and history as obtained from 3rd party data providers like D&B and Experian. Though the credit establishment was done at the Client corporate level, the client branches and subsidiaries were allowed to make their decisions on their own and there was no direct communication link between the Parent corporate and the Child Branch/Subsidiary offices. When the Customer of the Client itself was operating in multiple locations, the gap multiplied and resulted in disarray and haphazard business dealing which involved each location having the max credit limit which never got consolidated at the parent customer level. For example, if ACME Company has 3 locations and with a credit limit of $10 million dollars, each of the location received $10 million dollars as against distribution of the said credit. This was due to the fact that operations at each client locations were silo-ed in nature that it resulted in loss of communication with the Client corporate function and credit information was not balanced on time. This practice created havoc as the corporate finance department did not understand the type of problem situation they are in and started having incorrect invoicing and billing. The other problem that creeped in was customers started defaulting on their credit and at some point in time, some customers even returned the merchandize that was sent because of overlimit.

Problem #2:

The Client was unable to track their customers properly and some of these customers were also suppliers to the Client such as Verizon who were customers because they purchased cabling equipment and were suppliers because they sold telephone equipment and service.

Apart from this, there were duplicate Customer records and record had their unique customer identification. Owing to this, the Client was unable to understand who their true customers were and lost key statistics on their profile and buying behavior.

So a complete customer perspective was lost due to this situation.


AVS stepped in after a disastrous attempt by a leading (top 5) consulting firm and performed key evaluation of the present situation.

AVS proposed a Master Data Management solution with emphasis on Customer, Supplier, Accounts, and Products. The solutions involved collecting data from various disparate systems, consolidate and integrate them after rigorous data quality checks such as name and address cleansing, standardization, de-duping, validate and merge records, suspect and unmerged records set up for data stewardship approval process under a data governance umbrella. A J2EE user-interface allowed maintenance of the Master data and distribution to various downstream and upstream systems were designed and developed with SOA architecture in mind.

Master Data Management (MDM)

Master Data Management is all about collaboratively managing and controlling Master Data in a convenient workflow rich process environment. The intent is to create a “Golden Copy” a.k.a. “Single version of the truth” for the enterprise for consumption by downstream and upstream applications, for regulatory and compliance reporting, and for management to have a global perspective (a 360° view) of their Master Data. While a global perspective of the Master Data is very important for the enterprise, there will be cases where multiple views of the same Master have to be supported for Corporate vs. Local views. The reason to support these are when local government regulates new rules it inhibits changes to the Master Data at the Local level and thus forces supporting the local Master Data.

Master Data Definition

Every business has elements of core business reference data which are used in multiple types of applications and business processes. Often this is the most important data that the business has, since it represents the business’ understanding of its customers, suppliers, employees, products, inventory, bills of materials, or parts. This type of data is called Master Data, and it is one of the most important assets that a company owns, although it is often not treated that way.


Now, the Client enjoys the following resulting in millions of dollars in savings and has a consistent, accurate and complete Master data at their disposal:

Business Value

v  Reduced business, operational and resource risks through improved data quality control, timeliness, data coverage, data standards, business rules, and optimal resource management.

v  Increase satisfaction provide data view access to users and quicker response times for requests, also increased understanding of data across the enterprise

v  Costs reduction through elimination of duplicated data collection and maintenance efforts.  It also reduces data consolidation efforts for systems integration

v  Improved operational efficiency by eliminating redundant data management infrastructures, improving processes, and minimizing exception handling in downstream operations caused by information inaccuracies

v  Improved decision making through access to quality data

v  Oversight and coordination across numerous dependent portfolio projects to drive standardization

“Delivering Measurable Performance. Always!”

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