A W H I T E P A P E R S E R I E S
MASTER DATA MANAGEMENT ENSURES THAT THE ORGANIZATION MAINTAINS CRITICAL DATA IN SYSTEMATIZED ORDER TO AVOID DUPLICATION AND INCONSISTENCY. LARGE ORGANIZATIONS RESORT TO TOOLS THAT INCORPORATE RULES TO ELIMINATE AMBIGUITY.
Increasing Efficiency across the Value Chain with Master Data Management
A P P L I C A T I O N S
Successful Business Intelligence implementation for Data Centric operations with Master
Data Management
Master Data Management (MDM) constitutes a critical component of the technology that is
being integrated with both operational and analytical systems to enable a high degree of trust in the
underlying data being fed. MDM aims to synchronize critical and disparate pieces of data related
to customers or products or any subject, for which master data plays a significant role across the
enterprise data. This paper showcases Syntel’s experience and the expertise demonstrated in the
development and support of custom MDM solutions across the value chain of some leading
manu-facturing firms, while simultaneously catering to specific requirements and compliance guidelines.
TABLE OF CONTENTS
EXECUTIVE SUMMARY
THE IMPORTANCE OF MASTER DATA MANAGEMENT
NOT ONE-SIZE-FITS-ALL
START SMALL, BUT DESIGN FOR FUTURE
MDM EXPERTISE ACROSS THE VALUE CHAIN
CONCLUSION
© 2 0 1 3 S Y N T E L , I N C .1
2
3
4
5
6
Executive Summary
TABLE OF CONTENTS
EXECUTIVE SUMMARY
THE IMPORTANCE OF MASTER DATA MANAGEMENT
NOT ONE-SIZE-FITS-ALL
START SMALL, BUT DESIGN FOR FUTURE
MDM EXPERTISE ACROSS THE VALUE CHAIN
CONCLUSION
2. The importance of Master Data Management
The most significant asset possessed by companies, namely information, need meticulous attention coupled with sound, seamless processes and technologies so that it can support business decision-making. But information, necessarily and invariably, always originates from multiple and varied sources; therefore, there is an imperative need to ensure that it is coherently integrated and unified, while simultaneously complying with rules, regulations and policies, both internal and external. Master Data Management or MDM does just that - gathering, organizing, arranging, controlling, applying and using information with the objective to improve and augment efficiency across the value chain, and deliver an enhanced value proposition.
The funding prioritization for MDM projects should ideally align itself to the organization’s most critical business drivers, which are broadly divided into four categories, as depicted in the above figure.
The scenarios described above are only a few among many and with thousands of important transactions; critical factors of business require integra-tion, harmony, coherence, and coordination.
In this scenario, businesses are considering MDM implementation as the top priority for acquiring, procuring, and managing superior-quality data to streamline operations across the value chain.
3. Not One-size-fits-all
Complete, comprehensive, consistent, and accurate Master Data is sought across all enterprise systems. ERP suites might not be able to cater to the specific requirements of Master Data Management, as they have to meet industry-specific compliance requirements and organization-specific audit requirements. Hence, most organizations prefer creating customized web-based solutions that can be integrated with the existing ERP and other applications requiring the updated master data.
4. Start Small, but Design for Future
For organizations plagued by disruptive technologies, having a sound MDM strategy in place is emerging as an imperative. Big Data, Cloud, Mobile, and data exchanges with partners have drastically changed the Data Management landscape. This transformation calls for a consistent and forward-looking MDM strategy, which can be flexible enough for the ever-changing and dynamic technological needs.
Strategy to implement Customer Master DataTop Line Initiatives –
Bottomline Efficiencies – Product Master
Data and Supplier Master Data Strategic Initiatives
A new government regulation has been passed for products to meet certain requirements which needs change in product specifications.
The marketing team has changed a long time back the product description for products, which fall under this regulatory compliance, during its campaign.
The data, which the manufacturing units have, still refers to old product descriptions.
Customer data had been entered into the system wrongly and was later corrected by the shipping team in their product shipment application. None of the other fulfillment databases updated this corrected data.
The sales person of a supplier has moved out of the organization few months back and the company keeps sending latest guidelines and initiatives to the said sales person.
A new social campaign that the organization ran on facebook could collect lot of new customers with details of their subscriptions, check-ins and interests.
However it failed in consolidating the customer data as various departments had custom formats and synchronization was nearly impossible.
"Data quality and MDM are highly critical components not only for a successful BI implementation … but also for data-centric
operational processes such as order management and customer service" – Forrester Research, Inc., Drive Business Insight With
Effective BI Strategy by Boris Evelson and Anjali Yakkundi, May 14, 2013
When one considers SOA (Service Oriented Architecture) and SaaS (Software as a service), numerous challenges are thrown up, for which MDM implementation becomes critical:
• For SOA, what kind of customer view service can we generate when we have our customer data stored in five databases with three differ-ent addresses and two differdiffer-ent phone numbers?
• For SaaS, any subscription to a CRM service provider would require a list of customers for its database.
5. MDM Expertise across the Value Chain
Syntel has gained deep domain expertise in structured and well-regulated MDM implementation right from positioning the strategy, setting the data standards and prototyping, to releasing the application in phases - based on the priority of business-critical requirements.
We have displayed in the above figure a snapshot of our expertise in MDM implementation, eliminating several pain points in the value chain faced daily by customers.
As depicted in Figure 3 below, one of our customers has chosen a phased build approach to release the application initially for production business units, which later extended to non-production units for maintaining the supplier master data. The initiative that started with supplier on-boarding services included supplier surveys, assessments, contact data processing, and hierarchy information, among others - transforming itself into a single source for supplier master data across the enterprise.
· On-boarding services · Evaluation
· Hierarchy maintenance · Contact Data and Supplier Information Management · Lifecycle Management
· Data Code Processing · Multi application workflow · Product Hierarchy · Model Maintenance · Multi User accessibility
· Claims Management · Dealer Management · Warranty Analytics · Supplier Recovery
· Service Lifecycle Management
Features
Magnitude
· 350,000 Suppliers · 12 Systems interfaced · 144 Countries · 18 Business Processes · 330,000 Product data codes · 2,800 modules · 40,000 Suppliers · 4,000 Dealers · 220 InterfacesSupplier
Product
Customer
MDM Expertise across the Value Chain
· Vision & Roadmap · Sponsorship · Data Governance
MDM Strategy
· MDM Maturity Assessment· Data Profiling · Assessment Reports · Data Standards
Assessment
· Business Requirements. · Business Rules and Validations
· Process Flow · Global Data Flow
Design
· MD Creation Process Activities
· MDM Operations
· Partner Systems Integration Readiness
Deploy
Phased Approach for MDM Implementation
"In fact, it is often not that there are no standards in place for data management, but rather those that do exist don’t meet the needs of
the business or data managers." Without Data Management Standards – Anarchy! Forrester Research, Inc., Blog Posted by Michele
Goetz on April 2, 2013"
"MDM creates context for big data. The nature of big data is that it is often unstructured and maintained in an uncontrolled
environment. To obtain value from these sources, data architects must understand how to interpret the data within the context of
the business." – Forrester Research, Inc., Market Overview: Master Data Management, Q2 2013 by Michele Goetz, April 23, 2013"
6. Conclusion
Depending on an organization's MDM maturity, disparity of data sources, sensitivity to cost and business drivers, various tools can be chosen. MDM has to be practiced as a strategy rather than a data integration project in order to garner the maximum ROI from your business. MDM is the first step that an organization has to take if it possesses a roadmap towards big data, BI implementations, and strategies to reap the maximum out of social networking as well as low cost and effective business operations. MDM implementation has come of age and has acquired a matured, profile-oriented approach where users can capture the unstructured, multi-source data to support data integration and cost reduction.
SYNTEL:
about
v i s i t S y n t e l ' s w e b s i t e a t w w w . s y n t e l i n c . c o m
