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Master Data Management (MDM)

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Presentation on theme: "Master Data Management (MDM)"— Presentation transcript:

1 Master Data Management (MDM)
ARKANSAS BLUE CROSS BLUE SHIELD (501) Tuesday 9/25, 8:40 - 9:20

2 Speaker at national conferences for Oracle, Fidelity, and MDM-CDI.
Robert Fox - Data Architect, Arkansas Blue Cross Blue Shield 17 years of data architecture and warehousing experience in the finance, telecom, and health insurance industries. Installed over 50 data warehouses worldwide, some loading more than 4.5 terabytes of new transactional data per day. Guest lecturer in information management masters degree programs at 3 universities. Speaker at national conferences for Oracle, Fidelity, and MDM-CDI.

3 Abstract – MDM is bigger than you think
Master Data Management (MDM) is critical to effective managing the information assets of your company. Unfortunately, however, the meaning of the term Master Data Management has become more limited in scope over time. To understand the full scope of MDM, you must ask yourself two questions: What is “master data”? What all is involved in “managing” it? How you answer these two questions determines whether your MDM program is a departmental or enterprise solution, and whether you are managing the information or managing a process. This presentation will propose a framework for MDM that is broad in terms of both data and functionality.

4 Why is Information Important? The Evolution of Competition
The Land Grab Acquiring unclaimed customers Investment in infrastructure, name recognition, and geographic territory The Killer App Stealing customers from the competition by offering new and better products and services Investment in software development The Information War Stealing and retaining customers from the competition by knowing more about the customer and giving them individually customized treatment Investment in business intelligence, customizable offerings We are now in the age of information If your nephew was going to college to study “computers,” you would tell him to…

5 First: What is “Master Data”?
Application-specific, non-duplicated, non-shared data Data duplicated in multiple apps App A App B App C App Z Other Data shared with enterprise There seem to be three different definitions: Master Data is all the data, everywhere. Master Data is just the data that is duplicated in multiple applications. Master Data is any data that is exposed outside of an application, whether it is duplicated or not. There has never been 100% consensus on what “Master Data” really is. In the early days of MDM, most people tended to think along the lines of definition 1 above. This proved, however, to be too far reaching, both for the companies who were attempting to control data at this level, and for the consultants and tool vendors trying to provide products and services. In an effort to reduce the scope to something more manageable, the companies, consultants, and tool vendors narrowed the definition of master data dramatically, so that the result was a well defined, manageable scope of data problems – the problem of having the same data (typically customer names and addresses) stored and independently maintained in multiple applications. As products and services became available to address this problem, the definition of Master Data became synonymous with this reduced scope. “We can help you with this master data problem” quickly changed to “we can help you with the master data management problem.” The solutions themselves were not bad things. Synchronizing duplicate data is a very pressing need in almost every business, and a very complex problem to solve. If you are not solving this problem within your infrastructure, you are not doing a good job of supporting your business. The problem with these products was not in the products themselves, but in the danger of thinking that solving this problem was the extend of master data management. There are some consulting companies and tool vendors out there who still cast MDM as composed entirely of solving the customer name and address synchronization problem. After all, if that’s the tool they have in their toolbelt, then it’s to their advantage to describe MDM in this way. They can then say they have a complete solution. But this is not a complete solution. Most professionals these days have broadened the definition of master data back out to definition three, striking a healthy balance between too broad a scope (definition 1) and too narrow a scope (definition 2). Part of the driver cam from business, having solved the immediate problem of name and address synchronization, realized that there were still many other data management issues to address. Another driver of the expansion of the defintion of master data came from vendors who were developing products and services for these other data management issues. Gartner, Forrester, Information Management Magazine, the CDI-MDM group, and more advanced constultants and vendors such as Oracle and IBM now agree on definition 3). Internal data that is never exposed outside of an application is not considered master data, but any information that is exposed outside of the source application is master data, an enterprise asset that must be managed. If a particular data element is needed outside of an aplication for reporting, for pricing, for billing, for forecasting, for risk analysis, for commissions, or for any other purpose, then the data is an enterprise asset, not an application-specific asset.

6 Second: What does it mean to “manage” data?
If someone asked you to describe your organization, you would know, because you leaned it in kindergarten, that to completely answer the question, you would have to address the who, what, where, when, why, and how, right. All of these questions can’t be answered in a single picture. There are, however, standard modeling techniques to depict the answers to each of those questions. To answer the “where and how,” you would use an environment diagram showing your various platforms and how data flows between them. The “why” question should be in your mission statement. The “who and what” questions, you would begin with a functional area diagram like the one shown here. This is a very simplified version of our functional areas. I cut out a lot, and it’s still probably too small to read. But that isn’t really important. What’s important is that you document all the functions your organization performs. When you “manage” data, you are doing far more than just synchronizing it across multiple platforms. You also “manage” the security of the data, the quality of the data, the delivery of the data, the meta data describing the business data, and many many more management functions. This is a very useful diagram. When you do your strategic planning, you need to pull out this functional area diagram and make sure you have a strategy for ALL the functional areas in your organization. When you are assigning roles and responsibilities, you need to make sure everyone knows what they are and are not responsible for. And when it comes to master data management, you need to make sure that you are managing ALL of these aspects of data. This conference is called the IM symposium. IM – Information Management. This diagram describes the breadth of what it means to manage information. If you don’t have this clearly documented somewhere, then I suggest that you literally do not know what you are doing. So, now we’ve discussed what “master data” is, and what it means to “manage” data. We are now ready to discuss “master data management.”

7 First Generation IT Architecture
Application A Application B Application Z Customer Product Reporting Customer Product Reporting Customer Product Reporting Data Data Data Business Logic Business Logic Business Logic User Interface User Interface User Interface Monolithic applications with proprietary, embedded data Custom point-to-point integration Very little ability to establish enterprise architecture Data “owned” by application. No such thing as enterprise data, data standards, standard integration interfaces, etc. Lots of “re-inventing the wheel” in each applicatioin. Integration Very expensive to add new application to mix Very expensive to test Very difficult to manage at enterprise level

8 The Evolution of Data Architecture
Modular DB Data Access Business Logic Presentation DB Server 3 Tier Client/Server App Server DB Data Access Business Logic Client Presentation Client 5 Tier SOA Web Server Presentation DB Server App Server DB Data Access Services Business Services Integration Bus Monolithic Custom Data Custom Application Modular – tired of re-inventing the wheel in each application, standard tools were developed. Proprietary data stores were replaced by database APIs. Proprietary reporting engines were replaced by reporting API’s. Proprietary user interfaces were replaced by standard GUI API’s. And so on. But applications were still relatively monolithic, just built from standard components that were “compiled” into the application. Client Server – at this point, the embedded, compiled API’s for the various subsystems were replaced with discrete components, coupled together by interfaces. These interfaces were initially vendor specific, but over time standards emerged. ODBC to connect database servers to app servers. HTML to connect app servers to front end applications. SOA – interfaces continued to standardize until all components at all layers communicate with the same standard interface – XML services. This standardization effectively decoupled data from applications (Data as a Service, or DaaS). Data can now be read directly by the presentation layer, or by other applications. Applications can even share the same data stores. Instead of each application having its own copy of customer names and addresses, there can theoretically be one single storage domain for customer name and address, existing independent of application business logic. Monolithic architecture has evolved into loosely coupled functional specialty areas. Unfortunately, IT department organization charts have not kept pace with this architectural evolution. Early on in this evolution, IT departments developed into Application Development groups and Technology/Infrastructure groups. Application Development did the software development and configuration, and the Technology groups handled all the hardware and the networking. At this point in the evolution of IT architecture, data management was buried within application development

9 Effective MDM cannot be a component of application development
Traditional Location of Information Management Responsibility Emerging Location of Information Management Responsibility IT IT Application Development Technology Systems Information Management Application Development Technology Systems Information Management Application developers have an application-centric view. If they think about data at all, it is in the context of an application, not in the context of a separately manageable enterprise asset. Even if the application development manager “gets” MDM, when conflicts arise, their ultimate priority is to put out an application, not an enterprise data infrastructure. Recognizing the emerging importance of information as a competitive differentiator and corporate asset requires recognizing the importance of information management. Information management is an industry-recognized discipline, responsible for the collection, organization, quality, and delivery of a company’s information assets. The scope of responsibility is not limited to a development project or to a particular department’s information. It’s the whole enterprise. 2 Make sure your MDM program has the authority to be effective

10 Three legs on the IT “stool”
Financial View Profitability Now Investment for the Future $ $ Business View Customer Value Operational Efficiency Strategic Planning Increase Revenue Reduce Expense Grow the Company IT View Remember the evolution of competitiveness? Infrastructure – Feature/Function – Information? These competitive differentiators still exist within your IT departments. All three are necessary in order to support the business's goals. If you don’t have information management working at this level of your organization, not only is your organization chart not aligned with your architecture, but you are not allowing your business to effectively compete on information. You may have noticed that I’m no longer talking about MDM in the limited scope of an enterprise data warehouse. Ideally, MDM is not limited to analytical copies of data. A responsible IT organization is going to have a master data management program that manages ALL the enterprise information assets, not just the analytical information. That said, information management almost always begins first with analytical data. Probably because that’s the one area of the company who’s main focus is on information, not on applications or technology. If you are here representing the MDM program that is limited in scope to analytical information, and your company doesn’t have an enterprise MDM program, your should be thinking about setting up your program in such a way that, once you’ve proven your ability to manage information assets in the analytical space, you can expand your MDM program to the operational space without completely throwing it away and starting over. Application Information Technology (How?) (What?) (Where?) The goal of IT is to support the business. The goal of the business is to make money.

11 MDM Components OK, so now that we’ve defined what master data is and what it means to manage it, and now that we’ve stressed the need for MDM to exist outside of individual projects and outside of just analytical repositories, we can begin to discuss some of the components of MDM. We won’t have time to discuss all of them, of course, so I’m just going to pick a few that I think are particularly interesting or misunderstood.

12 Customer Data Integration (CDI)
2. Synchronization CDI App 3. System of Record CDI App A CDI App B App C 1. Analytical CDI First, I do want to discuss customer data integration, or CDI. CDI is the tools and processes that address the issue of names and addresses being independently updated within the data stores of multiple independent applications. Whether right or wrong, most companies initially implement CDI for analytic purposes only. The duplications and missing or innacurate data in the source systems is left unchanged, but the names and addresses are imported into the enterprise data warehouse where they are cleansed, de-duplicated and merged into a single golden copy of the name and address of each unique individual for analytic purposes. Once the value of this cleansed data is realized, companies often see the next logical step being the pushing of the golden copy of the name and address back to each application. This usually begins as a batch process, but there is great value in performing CDI functions immediately as part of every name and address insert and update. The cost of going from batch updates to real time updates is less in the vendor solution, and more a factor of the effort it takes to add the real-time capability within the application. In many cases, however, the CDI tools can now interface directly with the database via triggers or change log capture. Of course, in order to provide this “golden copy” functionality, these CDI tools usually are configured in a way that implements a name and address database for matching and merging purposes. Since these tools generally have the capability to provide real-time synchronization services, they can often be accessed directly by applications and front end interfaces as the system of record for name and address information. This leads to the final development in CDI architecture implementation, where you begin developing new applications without a name and address repository of their own, and gradually begin the process of decommissioning your application-local copies of name and address, and redirecting the applications to the CDI hub as the system of record. I’ve said that, in my experience, this is the way CDI typically rolls out. That doesn’t mean that this is the path every institution takes, or even that it is the best path to take. There is a cost in all these iterations that can be avoided if you have the foresight and wherewithal to jump directly over some of the intermediate steps. Your path may look different, but one way or the other, you need to solve this problem. I should also say that vendor solutions vary. Some, rather than implementing a local “golden copy” database in the CDI solution, implement a federated solution instead, where the data remains spread across all of the source systems. I admit to a bias against this approach. I feel that it is too dependant on all of the connections being avaliable all the time, I’ve found they don’t perform as well as the centralized solutions, and I feel that a federated solution can never become the final system of record. WH

13 MDM Components: Enterprise Logical Data Model
Party Claim Product DB Design SOA Services Mapping Sources Defining System of Record Define Stewards Global Dictionary Standard Terms Document Bus. Relationships Member Claim Drug Name Header Demographic ServiceLine Risk Adjustment Provider Payment NPI Base it on business view of data, not application view Useful for: Designing a normalized warehouse distribution tier Designing SOA services Mapping existing app db’s to a common schema Defining system of Record Defining Data Stewards Creating an enterprise data dictionary Agreeing on terminology, standardizing names Agreeing on business concepts An ELDM is not a database. It is a conceptual model from which databases and services are derived. Specialty Diagnosis Clinic Procedure Location

14 Data Standards No duplicate data
SoA same as SoR unless proven untenable Only one warehouse All data is owned by the enterprise, not individuals or departments Data storage and transmission must comply with security standards Services used by multiple applications must use data structures derived from the enterprise data model All data structures must be documented All new data structures must be derived from enterprise data model Data quality should be addressed at the point of entry into corporate network Information should be as accurate and current as possible Data access should always be directed to the SoA The data model should drive the choice and architecture of applications, and should be based in turn on business requirements Any exceptions should go through Data Governance

15 Dimensions of Data Quality for an Analytical Repository
INTEGRITY – does the data accurately reflect the source system? VALIDATION – is the data valid for the field? VERIFICATION – is the data actually true? BALANCING – does the data compare to other systems (i.e. GL)? AUDITING – Are there outlying values? There is no universally agreed upon list of the various types of data quality functions. Do a google search on “data quality dimensions” and look at each of the results returned on the first page. Not one of them will return the same list, or even a list of the same length. I’ve seen lists that defined three dimensions, and lists that defined 25 dimensions, and just about everything in between. I personally think that all but the most esoteric suggestions, though, can be grouped into these five areas While a data quality manager may define the standards for all of these dimensions, some of these are best implemented by development staff, i.e. batch integrity checks for completeness of file transmission. At the other end of the spectrum, auditing is a statistical analysis process best performed outside the batch process by data quality professionals.

16 Software Development Life Cycle (SDLC)
Project Pipeline Business Requirements Escalation Process Data Quality Project Supervisors Senior Leadership Team Executive Sponsor Architectural Approach Resource Assignment Technical Design Project Process Design Review Waiver Process Development Standards Review Board Standards Committee Executive Sponsor Develop a system that has minimal overhead necessary for administrative oversight Make it as easy to use as possible – invest in tools See Agile Development presentation for a discussion of Waterfall vs Agile development methodologies Escalation – when team members cannot agree on which approach is best Waiver – when team members agree, but approach violates standards Your SDLC may have more than one project path (strategic vs fasttrac) Unit Testing/ QA Testing Code Review System/User Testing Implementation 4 Develop an SDLC and infrastructure to support it

17 Business Intelligence: Closing the loop
Customers, accounts, inventory, payments, activity, products… DATA Profitability, Churn Propensity, Customer segment… INFORMATION BUSINESS INTELLIGENCE KNOWLEDGE RESPONSES OODA Loop = Observation, Orientation, Decision, Action, Feedback Recommend product X, sign up for ePay, Offer free upgrade… DECISION/ACTION

18 Maturity Model For Business Intelligence
5 Real-Time Decisioning 4 Batch Campaigns 3 Enriching with 3rd-party data 2 Modeling, Scoring, and Segmentation 1 Enterprise Reporting

19 Figure out what “Master Data Management” means in your organization
Summary Figure out what “Master Data Management” means in your organization Think larger than the warehouse. Make sure your program is empowered with authority to be effective within the enterprise. Develop a functional area framework and assign responsibilities Develop efficient MDM strategies for each functional area: Software development process, standards and waivers, aging and archiving, data quality, data delivery, security, etc. Always remember that you work for the business!

20 Questions?

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