1. Info-Tech Research Group1 Implement a Master Data Management Strategy Manage the truth, the whole truth, and nothing but the truth. Info-Tech's products and services combine actionable insight and relevant advice with ready-to-use tools and templates that cover the full spectrum of IT concerns.© 1997 - 2012 Info-Tech Research Group

2. Info-Tech Research Group2 Without a clear cut strategy for reconciling disparate data sources and normalizing databases, conflicting data can lead to problems for reporting and analysis. Use Master Data Management to create a central, single version of the truth. Introduction CIOs, IT Managers, Infrastructure Managers, Data Stewards, and Data Integration (DI) Specialists who are involved in, overseeing, or are responsible for the implementation of a Master Data Management (MDM) solution. Identify what MDM is. Plan for an MDM implementation. Select the appropriate MDM architecture for the organization. Define an MDM strategy. Implement an MDM strategy and optimize it to reap the benefits. This Research Is Designed For:This Research Will Help You:

3. Info-Tech Research Group3 Executive Summary Understand MDM Implement and Maintain MDM The goal of an MDM solution is to put all an organization’s myriad data sources into one complete database to create a central, single version of the truth. MDM uses both processes and technology to enhance data quality by cleansing, reconciling, and consolidating various data sources across the organization. Use a phased approach to implement a functional MDM solution that delivers value early. Begin by adopting an architecture that is acceptable, but strive towards the ideal transactional hub MDM architecture. Address the various data requirements of user groups. Create Data Steward roles and assign key implementation responsibilities to stakeholders. Achieve a steady state of MDM, and maintain data quality. Sustain the data governance process. Expand and refine the MDM process for future phases. Plan and Design an MDM Initiative Plan the MDM initiative in phases; secure executive buy-in, identify data producers and consumers, clean up your data, refine processes, and establish a data governance program to manage organizational change. Incorporate big data, business intelligence (BI), and cloud computing in your MDM initiative based on the organization’s overall strategy. Select an appropriate architecture for the effective implementation of master data management. Decide if the organization is going to build the MDM solution internally or purchase a vendor offering.

4. Info-Tech Research Group4 MDM is no longer optional for organizations implementing Big Data analytics, BI, or moving to the Cloud The concept of Master Data Management has been around for quite some time, but as a practice it only really appeared in the early 2000’s. Traditionally, MDM was thought of as a housekeeping tool for data quality and maintenance, but recent emphasis on compliance, SOA, data integration, SaaS, and mergers and acquisitions has made the creation and maintenance of an accurate and complete set of master data a business necessity. The MDM space has evolved in recent years to address the data integration problems resulting from corporate growth. Multiple data systems and lines of business have created disparate data sets that must be reconciled to create a single version of the truth. Today, MDM is in the spotlight because of its ability to deliver the benefits of better data consistency and quality, which directly impacts accurate reporting and data analysis, a reduction in process delays, and data standards. MDM is rapidly moving up the chain of importance as Big Data, business intelligence (BI), and Cloud computing become high on the priority list within IT departments. Organizations have realized that data is their biggest asset and are now developing complex BI strategies around it. A comprehensive MDM initiative that not only improves data quality, but also assists in connecting relationships across various data sets must be employed. In this Big Data era, organizations must significantly expand their MDM initiative in order to manage and make sense of the massive amounts of information that will be generated. The transition to Cloud computing platforms produces the potential for data quality issues. An MDM solution is required to keep data sets synchronized, clean, and governed appropriately. How MDM got here Where MDM is going

5. Info-Tech Research Group5 Using MDM promotes data quality across the enterprise by reducing data redundancy and inconsistency Redundant Data: A classic example of redundant data is when an organization has two copies of address information for one customer within the same system. Consider an enterprise that has decided to run a marketing mail-out using the customer data from both the ERP and CRM systems. If steps have not been taken to account for the presence of redundant data across systems, the company will send out multiple copies of marketing material to the same customer. The net consequence is that the enterprise will incur additional costs for every duplicate record and potentially annoy the customer. Why do Enterprises need MDM? The primary pain points that motivate enterprises to adopt an MDM strategy are the consequences of data redundancy and inconsistencies in duplicate copies of data. Inconsistent Data: The problem gets even worse if a company has inconsistent address information across multiple data sets. A typical example of this is when a customer moves and a client representative only updates the CRM record. In this case, there are still duplicate copies but only one copy is correct. Since the ERP system will only send bills to the address it has stored, the client will fail to receive bills. The result is that the client could go into default on their account with the potential result that the enterprise loses that client and others due to poor public relations. Accurate and timely customer contact information is essential for the operations of all organizations, large and small. Poor data quality, in the form of data redundancy, data inconsistency, and data duplication, undermines the effectiveness of email, phone, and mail communications.

6. Info-Tech Research Group6 MDM is essential when regulatory constraints, SOA, SaaS, cross-LOB views, and mergers/acquisitions are in play Complying with regulatory constraints: Moving to a Service Oriented Architecture (SOA): Moving to a Software-as-a- Service (SaaS) Vendor: Getting a single picture of an enterprise with multiple lines of business (LOB): Mergers and acquisitions: Some regulatory bodies demand consistent reporting from enterprises (e.g. Sarbanes-Oxley). MDM provides better control over enterprise data to enable consistent reporting for regulatory purposes. To create a single business service that functions appropriately, it is essential that the information the service accesses is accurate and up to date. For example, if customer information is stored in multiple databases, a decision will have to be made about which will be the record of source. SaaS vendors can require a copy of the company database, as is the case when moving to a CRM system like salesforce.com. If there are multiple databases, a decision will have to be made about which one is the most accurate. With an MDM program in place, this task is made much easier. LOBs typically have their own distinctive view of core business information, such as customers, accounts, and products. In order to get a clear and comprehensive view of the organization, these different perspectives, realized as disparate data sets, need to be reconciled. The merging or acquiring of another business can cause serious problems with data replication. Each organization that is part of the process will have its own set of master data records. If efforts are not taken to reconcile master data, the enterprise may not be able to leverage the data effectively. There are also additional costs that can be incurred as a result.

7. Info-Tech Research Group7 Data Integration with the Cloud The growing adoption of Cloud-based services has created a wide range of DI and quality issues. This solution set will help you take advantage of Cloud agility while retaining data quality. Follow the Info-Tech Data Integration Roadmap for additional Data Integration guidance & advice Customer Service Strategy Move From Chaos to a Realistic Data Integration Strategy This solution set will provide a straight-forward mechanism to rate the magnitude of the DI challenges you face and help form strategies and tactics for immediate improvement. Before a DI project can even be considered, the mess of current integrations must be defined, understood, and mapped. Hand-coding may seem like the best integration method, but tool- created integrations are easier for future developers to adapt and manipulate, putting the organization on a path to standardization. Vendor Landscape Plus: Data Integration Tools This solution set will help quantify the cost of internal development, costs of building vs. buying DI tools, and help select the right tool. Tackle Data Quality Issues The five types of data quality issues are: data duplication, stale data, incomplete data, invalid data, and data conflicts. Each problem requires a combination of corrective and preventative measures instituted by both the business and IT. DI Strategy Implement a Master Data Management Strategy

8. Info-Tech Research Group8 What’s in this Section:Sections: MDM Overview MDM Strategy and Planning Design Master Data Processes and Technology Implement MDM Processes and Technology Maintain MDM through Governance and Optimization What is Master Data? Addressing Data Quality Info-Tech’s MDM Lifecycle Navigate Around Common MDM Roadblocks

9. Info-Tech Research Group9 Master data is a single, accurate set of data populated across multiple systems, and is essential to enterprise operations. What is Master Data? Master data is a compilation of information that describes four core categories of business entities: people, places, things, and abstract objects. Each category can be further subdivided into domain areas (e.g. customer is a domain area for the category people). Business Category: PeoplePlacesThingsAbstract Objects (Things) Examples of Domain Areas: Customer Employee Salesperson Office Locations Customer Locations Product Store Asset Contract License Warranty The goal of MDM is to coordinate master data across the same categories: For example, the ERP and CRM customer masters. Master data management ensures better data integration and provides a consistent view of people, places, and things. Master data does not necessarily refer to a single source of enterprise data. It is common to conceive of master data in terms of much more narrow categories. For example, in a typical retail company ERP system, it is common for there to be a master- data list corresponding to multiple business entities (e.g. a customer master, a product master, and an account master).

10. Info-Tech Research Group10 This example shows how an MDM can be used to gain a single view of the same customer that exists in the CRM, ERP and POS systems. Use an MDM system to reconcile the same data object across multiple systems MDM ERPCRM POS CRM Customer First Name Last Name Address Phone Number Email Address Products Offered Products Sold Products Rejected CRM Customer First Name Last Name Address Phone Number Email Address Products Offered Products Sold Products Rejected POS Customer Phone Number Receipts Tax Applied Returns POS Customer Phone Number Receipts Tax Applied Returns ERP Customer Last Name First Name Company Address Account Number Account Receivables Account Payables ERP Customer Last Name First Name Company Address Account Number Account Receivables Account Payables MDM Customer Last Name, First Name Company Account Number Phone Number Postal Code Credits Debits MDM Customer Last Name, First Name Company Account Number Phone Number Postal Code Credits Debits

11. Info-Tech Research Group11 MDM uses both processes and technology to enhance data quality by cleansing, reconciling, and consolidating various data sources across the organization. Create a single version of the truth by implementing an MDM solution MDM Implementations Depending on enterprise data needs and existing infrastructure, actual implementations of MDM will differ from one IT environment to the next. MDM strategies can also differ with regards to the particular domain of data they focus on (e.g. customer information vs. financial information). The goal of an MDM implementation is to set up rules and standards to surrounding enterprise data to ensure it is complete, accurate, and up-to- date across all data sources. Biggest benefits of MDM An MDM solution puts all the myriad data sources into a single complete database to create a central, single version of the truth for that data domain. Once data is centralized, duplicate records will be resolved, relationships between data will be detected and declared, and data will be made available to the applications, people, and processes that need it. The Positive Impacts of Implementing MDM Source: Info-Tech Research Group; N = 18 MDM technology isn't a simple out-of-the-box solution you can buy, install, and forget about. It's an interconnected group of technologies and processes that work together to create and maintain reliable, accurate, and dependable data. Info-Tech Insight

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