Modeling the MDM Blueprint – Part 4
digg this | 
del.icio.us | 
Reddit | 
Stumble It!
In Part 2 and Part 3 of this series, we discussed the Common Information and Canonical Models. Because MDM is a business project, we need to establish of a common set of models that can be referenced independently of the technical infrastructure or patterns we plan on using. Now it is time to introduce the Operating Model to communicate how the solution will actually be deployed and used to realize the expected benefits.
This is the most important set of models you will undertake. And sadly, not widely accounted for “in the wild”, meaning rarely seen, much less achieved. This effort describes how the organization will govern, create, maintain, use, and analyze consistent, complete, contextual, and accurate data values for all stakeholders.
There are a couple of ways to do this. One interesting approach I’ve seen is to use the Galbraith Star Model as an organizational design framework. The model is developed within this framework to understand what design policies and guidelines will be needed to align organizational decision making and behavior within the MDM initiative.
The Star model includes the following five categories:
Strategy: Determine direction through goals, objectives, values and mission. It defines the criteria for selecting an organizational structure (for example functional or balanced matrix). The strategy defines the ways of making the best trade-off between alternatives.
Structure: Determines the location of decision making power. Structure policies can be subdivided into:
- specialization: type and number of job specialties;
- shape: the span of control at each level in the hierarchy;
- distribution of power: the level of centralization versus decentralization;
- departmentalization: the basis to form departments (function, product, process, market or geography).
In our case, this will really help when it comes time to designing the entitlement and data steward functions.
Processes: The flow of information and decision processes across the proposed organization’s structure. Processes can be either vertical through planning and budgeting, or horizontal through lateral relationships (matrix).
Reward Systems: Influence the motivation of organization members to align employee goals with the organization’s objectives.
People and Policies: Influence and define employee’s mindsets and skills through recruitment, promotion, rotation, training and development.
Now before your eyes glaze over, I’m only suggesting this be used as a starting point. We’re not originating much of this thought capital, only examining the impact the adoption of MDM will have on the operating model within this framework. And more importantly, identifying how any gaps uncovered will be addressed to ensure this model remains internally consistent. After all, we do want to enable the kind of behavior we expect in order to be effective, right?
A typical design sequence starts with an understanding of the strategy as defined. This in turns drives the organizational structure. Processes are based on the organization’s structure. Structure and Processes define the implementation of reward systems and people policies.
The preferred sequence in this design process is composed in the following order: (a) strategy; (b) structure; (c) key processes; (d) key people; (e) roles and responsibilities; (f) information systems (supporting and ancillary); (g) performance measures and rewards; (h) training and development; (i) career paths.
The design process can be accomplished using a variety of tools and techniques. I have used IDEF, BPMN or other process management methods and tools (including RASIC charts describing roles and responsibilities, for example). What ever tools you elect to use, they should effectively communicate intent and be used to validate changes with the stakeholders, who must be engaged in this process.
Armed with a clear understanding of how the Star model works we can turn our attention to specific MDM model elements to include:
Master Data Life Cycle Management processes
- Process used to standardize the way the asset (data) is used across an enterprise
- Process to coordinate and manage the lifecycle of master data
- How to understand and model the lifecycle of each business object using state machines (UML)
- Process to externalize business rules locked in proprietary applications (ERP) for use with Business Rules Management Systems (BRMS) (if you’re lucky enough to have one )
- Operating Unit interaction
- Stewardship (Governance Model)
- Version and variant management, permission management, approval processes
- Context (languages, countries, channels, organizations, etc.) and inheritance of reference data values between contexts
- Hierarchy management
- Lineage (historical), auditability, traceability
I know this seems like a lot of work. Ensuring success and widespread adoption of Master Data Management mandates this kind of clear understanding and shared vision among all stakeholders. We do this to communicate how the solution will actually be deployed and used to realize the benefits we expect.
In many respects, this is the business equivalent to the Technical Debt concept Ward Cunningham developed (we’ll address this in the next part on Reference Architecture) to help us think about this problem. Recall this metaphor means doing things the quick and dirty way sets us up with a technical debt, which is similar to a financial debt. Like a financial debt, the technical debt incurs interest payments, which come in the form of the extra effort we have to do in future development because of the quick and dirty design choices we have made. The same concept applies to this effort. The most elegant technical design may be the worst possible fit for the business. The interest due in a case like this is, well, unthinkable.
Take the time to get this right. You will be rewarded with enthusiastic and supportive sponsors who will welcome your efforts to achieve success within an operating model they understand.
Modeling the MDM Blueprint – Part 3
digg this | del.icio.us | Reddit | Stumble It!
In Part 2 of this series we discussed the Common Information Model. Because MDM is a business project, we need to establish of a common set of models that can be referenced independently of the technical infrastructure or patterns we plan on using. The essential elements should include:
- Common Information Model
- Canonical Model
- Operating Model, and
- Reference Architecture (e.g. 4+1 views, viewpoints and perspectives).
We will now turn our attention to the second element, the Canonical Model.
The Canonical Model (business rules and format specification) describes how the extraction of business rules from the software portfolio are managed and shared 
among other applications. In addition to externalizing business rules locked in proprietary applications (for example, ERP or CRM), we also use design patterns defined here to communicate between different data formats. Instead of writing translators between each and every format (with potential for a combinatorial explosion), use this in combination with the CIM to write a translator between each format and the canonical format using rules to guide the effort. See the Open Applications Group Integration Specification (OAGIS) as example of an integration architecture that is based on a canonical data model. Implicit (and emerging now as generally accepted practice) is the use of rules (rules engines like iLOG for example) to handle reference data that must be shared across systems beyond software packages in our portfolio. OAGIS uses XML as the common protocol for defining business messages and processes (scenarios) to enable business applications to communicate among one another in a standard manner. Not only the most complete set of XML business messages currently available (there are others several others, see the eXtensible Business Reporting Language (XBRL) for example), it also accommodates specific industries by collaborating with vertical industry groups to add and extend additional requirements as needed. For another real working example in the Product Information Management (PIM) space see GS1 Global Data Synchronization Network and the standards that make this possible.
Nick Malik over at Inside Architecture has written an exceptional post about this. We may not agree on all aspects (mostly semantics), but I think he has summed up well what this set of models should address in the blueprint. His post addresses the essential elements a complete modeling effort would produce. These products would typically include:
Canonical Message Schema - describes how when passing messages from one application to another we pass a set of data between applications where both the sender and the receiver have a shared understanding of what the values are: (a) data type, (b) range of values, and (c) semantic meaning.
Event Driven Perspective (Views) - a style of architecture characterized by a set of relatively independent actors who communicate events amongst themselves in order to achieve a coordinated goal. This can be done at the application level, the distributed system level, the enterprise level, and the inter-enterprise level (B2B and EDI). Although we disagree on where this effort belongs (see Part IV of this series on reference architecture development), the logical view will have its origins here.
Business Event Ontology - This ontology includes a list of business events, usually in a hierarchy, that represents the points in the overall business process where two or more objects (entities) need to communicate or share the same data values and intent (semantics). And this, as Nick states is “is not the same as a process step. An event may trigger a process step, but the event itself is strictly speaking simply a “notification of something that has occurred,” not the name of the process. Ontology development is a pretty exciting technology I have watched mature from simple lab exercises (toys really), to something far more useful. For more on this see Part II (The Common Information Model) or my post at Essential Analytics about the Protege ontology editor.
Business Rules - The last modeling effort is the collection (identification and grouping) of the rules used to define the behavior of the elements we have already referred to. Typically buried in application code, (if you are not lucky enough to have a Business Rules engine <g>), this model describes the business rules, protocol, and default behavior expected when the model elements interact with each other (especially useful when exceptions occur or logical constraints are violated). Not a common artifact I find; I wish more of us would take the time and effort to accomplish this task. For another real world reference, see the GDSN Package Measurement Rules (issue 1.9.2) for the global definition of nominal measurement attributes of product packaging or the GDSN Validation Rules.
As I stated in Part 2, this is hard challenging work. The key differentiator and difference between success and failure on your MDM journey will be taking the time to model the blueprint and sharing this work early and often with the business. We will be discussing the third (and most important element) of the MDM blueprint, the Operating model in part 4. I encourage you to participate and share your experience, as we can all learn from each other.
Modeling the MDM Blueprint – Part 2
digg this | del.icio.us | Reddit | Stumble It!
In Part 1 of this series, we discussed what essential elements should be included in an MDM blueprint. The important thing to remember is that MDM is a business project that requires establishing a common set of models that can be referenced independently of the technical infrastructure or patterns you plan on using. The blueprint should remain computation and platform independent until the models are completed (and accepted by the business) to support and ensure the business intent. The essential elements should include:
- Common Information Model
- Canonical Model
- Operating Model, and
- Reference Architecture (e.g. 4+1 views, viewpoints and perspectives).
We will now turn our attention to the first element, the Common Information Model.
A Common Information Model (CIM) is defined using relational, object, hierarchical, and semantic modeling methods. What we are really developing here is rich semantic data architecture in selected business domains using:
- Object Oriented modeling: reusable data types, inheritance, operations for validating data
- Relational: manage referential integrity constraints (primary keys, foreign keys)
- Hierarchical: nested data types and facets for declaring behaviors on data (e.g. think XML schemas)
- Semantic models: ontologies defined through RDF, RDFS and OWL
I believe (others may not) that MDM truly represents the intersection of Relational, Object, Hierarchical, and Semantic modeling methods to achieve a rich expression of the reality
in which the organization operates. Expressed in business terms, this model represents a “foundation principal” or theme we can pivot around to understand each facet in the proper context. This is not easy to pull off, but will provide a fighting chance to resolve semantic differences in a way that helps focus the business on the real matters at hand. This is especially important when developing the Canonical model introduced in the next step.
If you want to see what one of these looks like visit the MDM Alliance Group (MAG). MAG is a community that Pierre Bonnet founded to share MDM Modeling procedures and pre-built data models. The MDM Alliance Group publishes a set of pre-built data models that include the usual suspects (Location, Asset, Party, Party Relationship, Party Role, Event, Period [Date, Time, Condition]) downloadable from the website. And some more interesting models like Classification (Taxonomy) and Thesaurus organized across three domains. Although we may disagree about the “semantics”, I do agree with him that adopting this approach can help us avoid setting up siloed reference databases “…unfortunately often noted when using specific functional approaches such as PIM (Product Information Management) and CDI (Customer Data Integration) modeling”. How true. And an issue I encounter often.
Another good example is the CIM developed over the years at the Distributed Management Task Force (DMTF). You can get the CIM V2.20 Schema MOF, PDF and UML at their web site and take a look for yourself. While this is not what most of us think of as MDM, they are solving for some of the same problems and challenges we face.
Even more interesting is what is happening in semantic technology. Building semantic models (ontologies) includes many of the same concepts found in the other modeling methods we’ve already discussed but further extend the expressive quality we often need to fully communicate intent. For example:
- Ontologies can be used at run time (queried and reasoned over).
- Relationships are first-class constructs.
- Classes and attributes (properties) are set-based and dynamic.
- Business rules are encoded and organized using axioms.
- XML schemas are graphs not trees, and used for reasoning.
If you haven’t been exposed to ontology development, I encourage you to grab the open source Protege Ontology Editor and discover for yourself what this all about. And while you are there see the Protégé Wiki and grab the Federal Enterprise Architecture Reference Model Ontology (FEA-RMO) for an example of its use in the EA world. Or see the set of tools found at the Essential project. The project uses this tool to enter model content, based on a model pre-built for Protégé. While you are at the Protégé Wiki, grab some of the ontologies developed for use with this tool for other examples, such as the SWEET Ontologies (A Semantic Web for Earth and Environmental Terminology. Source: Jet Propulsion Laboratory). For more on this, see my post on this tool at Essential Analytics. This is an interesting and especially useful modeling method to be aware of and an important tool to have at your disposal.
This is hard challenging work. Doing anything worthwhile usually is. A key differentiator and the difference between success and failure on your MDM journey will be taking the time to model the blueprint and sharing this work early and often with the business. We will be discussing the second element of the MDM blueprint, the Canonical model in Part 3. I encourage you to participate and share your professional experience via the comments here.
Siperian Solutions Day
digg this | del.icio.us | Reddit | Stumble It!
Last week, I attended Siperian’s Solutions Day event in Princeton, NJ. I’ve been to several Siperian events in the past and always found them to be well done, with interesting and educational sessions. Nancy Ellickson and the Siperian team did a great job, and about 125 people attended.
After a brief “MacGyver” video, Ramon Chen (Siperian’s VP of Product Marketing) kicked things off, welcoming everyone and introducing the first speaker.
I saw the presentation by Charles Bloodworth, Director of IT at Johnson & Johnson Health Care Systems, when J&JHCS won the first “MDM Excellence” award at the Gartner MDM Summit. This time, he had more time and was able to tell a more complete version of their story. I liked his emphasis on “master data as the foundation of what we do”.
They funded their MDM effort by reallocating money from 3-4 different IT projects which would have all involved master data on customers, affiliations, products, list prices and sales force alignment. I’ve seen this approach to building a business case work several times – rather than duplicating all that effort, do it once and share the results, and you can usually do it for less.
J&JHCS has been weaving consistent master data into their major reengineering programs for about four years now. Information flows from the MDM system into their ERP and manufacturing applications, and from there into a data consolidation and business intelligence delivery capability, and this has driven a huge amount of value for them.
That session was followed by a demonstration of Siperian’s Business Data Director™, a new data governance user interface. I was impressed by its built-in workflow for data stewards, the slick view of data lineage, and its flexible hierarchy management.
Dan Goldsmith, a partner at IBM Life Sciences, did a session on IBM’s new managed service offering for Siperian MDM. It was revealing that IBM chose Siperian as the heart of its managed services offering, rather than its own MDM product. I spoke to Dan after his session, and he agreed to do an interview with me in a future article on the Hub Designs Blog.
Lynn Weishaupt, the MDM Technical Team Lead for Weyerhaeuser, talked about their use of Siperian in their SAP manufacturing environment. Having done a number of ERP implementations at manufacturing companies prior to the development of separate MDM systems, I found her session very interesting.
Siperian’s founder and CTO, Ken Hoang, gave an overview of the next generation of the company’s products, including an intriguing look at the Semantic Master, also referred to as Master Content Management. It was an interesting view into the way Siperian listens to its customers.
Morgan Norris, the Bioscience Division Market Manager for Millipore, talked about customer data management and their vendor selection process. He laid out some useful “lessons learned”: find or hire a technical team with experience in MDM and an experienced manager for data governance, start data governance 4-5 months before capturing your requirements, be flexible and keep it simple, follow a phased development approach, and communicate constantly with your stakeholders. Most importantly, make sure you’re solving a business problem, not just building infrastructure.
The highlight of the day for me was being invited to be part of the “Ask the Experts” panel discussion. I really enjoyed answering questions with Lynn Weishaupt from Weyerhaeuser, Morgan Norris from Millipore, and Manish Sood and Ken Hoang from Siperian.
Matching – MDM’s “Secret Sauce”
digg this | del.icio.us | Reddit | Stumble It!
I wrote a new guest article, published yesterday, on Infoglide Software’s Identity Resolution Daily blog. My previous articles are here and here.
Here’s an excerpt of the new article:
Few areas in master data management (MDM) are as critical as identity resolution. Just yesterday, I was working with a client on a matching issue where their customer (a car dealership) was matched to a veterinary clinic because the business names both contained the city and the client had somehow entered the address of the vet clinic in their customer record.
This situation (a “false positive” if ever there was one) is far too common. While the current generation of MDM platforms has come a long way in the last five years, identity resolution is one of the most difficult problems to solve, especially when both your source data and the hub or referential source you’re matching to have data quality issues.
Click on “Matching – MDM’s “Secret Sauce”” to read the rest of the article.
Solid Strategies for Surviving a Slowdown (Part 2)
digg this | del.icio.us | Reddit | Stumble It!
Editor’s Note: This is Part 2 of a two-part series written by Jim Walker from Initiate Systems. You can find Part 1 here.
Here are the rest of the six proven business strategies fueled by real-time, accurate and complete customer data in a master data management (MDM) system:
Use customer service to retain customers and cut costs: Acquiring customers is expensive, and losing them is even more costly. Better customer service will improve customer satisfaction and retention and reduce your costs. Empowering your touch points with a complete and accurate view of each of your customers and their relationships provides a clear picture to both your customer and your representatives. Automated collection and presentation of complete customer information to service professionals reduces search times and has a positive effect on call waiting times, increasing productivity and reducing costs. It allows you to never ask a customer to repeat themselves. Accurate composite customer data enables web self-service applications with a single view of multiple accounts to empower your visitors to help themselves.
Be bold, change the playing field; buy a competitor: A downturn will have an indelible effect on many industries as valuations decrease and otherwise impossible consolidations becomes plausible. Historically, the most successful mergers are made in downturns. According to Harvard Business Review, “downturn mergers generate about 15% more value, as measured by total shareholder return, than boom-time mergers.” However, capital is sparse and careful due diligence of a potential target is imperative, as their problems will become yours if the merger is completed. Merger analysis should include careful consideration of customers and any potential risks. This knowledge will help you make sense of a bold move during a tenuous time.
Regulations never go away: An economic downturn does not delay compliance with government regulations. In fact as a slide continues, governments look to increase regulation to prevent future issues. Compliance in a recession is imperative so that you avoid costly fines that may have a devastating effect on your financial picture.
Obtaining and delivering accurate, comprehensive customer data is at the foundation of all six of these strategies. Your level of success will depend on the quality of the data that feeds each of these business improvements. An accurate data foundation allows you to deliver on fixing short-term pains, while setting up long-term gains. A new breed of data management technology has evolved to meet these challenges.
Master data management enables delivery of these valuable strategies and objectives. It provides complete, accurate customer views and valuable hierarchy management to your customer data. Reliability and effectiveness of this data is determined by its accuracy and ability to reflect real world conditions in any situation where that data is used.
Jim Walker is a Senior Manager, Field Marketing for Initiate Systems, a leading provider of master data management (MDM) solutions. Jim specializes in researching and writing on the effects of MDM on the enterprise. He has held numerous marketing positions at enterprise software companies, focusing on data management and security. He has also designed and implemented large scale enterprise systems as a technology consultant. Jim holds a MBA in e-commerce from Carnegie Mellon University and a BS in Computer Engineering form the University of Illinois at Chicago. He can be reached at jwalker@initiate.com.
Click here for Part 1 of this series.
Solid Strategies for Surviving a Slowdown (Part 1)
digg this | del.icio.us | Reddit | Stumble It!
Editor’s Note: This is Part 1 of a two-part series written by Jim Walker from Initiate Systems. You can find Part 2 here.
As uncertainty accelerates, some companies not only survive, they prepare themselves to thrive during the subsequent recovery. Even in the best economic conditions, organizations look to do more with less, and in a downturn, they are forced to manage things even more tightly. Added market pressures force tough decisions. Less money results in contraction and consolidation of vital resources. Many companies turn to their most valuable asset, their customer data, to forecast and define their future.
A downturn opens rare opportunities to outmaneuver rivals by reducing costs and increasing efficiencies. Reliable and accurate customer data can provide valuable insight into changing market dynamics. Organizations that analyze their data to identify and avoid fraudulent and high-risk accounts can also reduce operating costs. Many use customer data to build and strengthen relationships and identify revenue opportunities. High quality, reliable customer data allows them to spot opportunities across lines of business or properly align a sales force with an up-to-date and accurate customer hierarchy. They can find pricing discount opportunities for customers or identify commissions over-payments.
The potential gain is substantial; however, the effectiveness of these initiatives is only as good as the underlying data that drives the results. Here are six proven business strategies that are fueled by real-time, accurate and complete customer data that master data management (MDM) can provide your organization.
Incent customers through improved pricing: Purchasing behavior changes dramatically in a recession. Consumers increasingly opt for lower-priced alternatives to their usual purchases. Customer upgrades and extensions not only improve your top line, they also increase customer retention and can be completed with much less cost. Knowing your customer will present pricing discounts or up-sale opportunities. However, complex business relationships, such a subsidiary and parent structure, keep you from obtaining a clear picture. Overlaying a trusted hierarchical structure on your customer data will provide an accurate view of rolled-up sales and introduce appropriate pricing discounts to garner new business in difficult times.
Never double pay a commission: Geographies or strategic sales territories sometimes overlap for sales executives. Managing this overlap for hundreds of sales resources across complex business relationships is difficult at best, and the potential overpayment of commissions is high. Identifying and removing the overlap requires you to have a clear picture of the customer and their organizational hierarchy. For instance, if two sales resources are selling into two subsidiaries of the same company, you may have missed an opportunity to roll up the sales and pay a single commission. Identifying and eliminating redundant commissions reduces operating costs.
Spend the right time on the right customers: Organizations are concerned for their own well-being as well as their customers’ and prospects’. Identifying and mitigating customer risk requires complete insight into accurate customer information, license positions and relationships. Overlaying a trusted hierarchical structure on customer data will allow you to roll up risk calculations so that you can analyze and identify a master account and apply appropriate risk strategies. This will help you understand your best customers and make strategic plans to maximize revenues associated to them. It will help you spend the right amount of time with the right customers.
Jim Walker is a Senior Manager, Field Marketing for Initiate Systems, a leading provider of master data management (MDM) solutions. Jim specializes in researching and writing on the effects of MDM on the enterprise. He has held numerous marketing positions at enterprise software companies, focusing on data management and security. He has also designed and implemented large scale enterprise systems as a technology consultant. Jim holds a MBA in e-commerce from Carnegie Mellon University and a BS in Computer Engineering form the University of Illinois at Chicago. He can be reached at jwalker@initiate.com.
Click here for Part 2 of this series.
Announcing HubCaliber Real-Time Access to D&B
digg this | del.icio.us | Reddit | Stumble It!
FOR IMMEDIATE RELEASE
Hub Designs and Caliber Services Announce HubCaliber Real-Time Access to D&B
BOSTON, 9-MARCH-2009: Hub Solution Designs and Caliber Services have teamed up to offer HubCaliber Real-Time Access to D&B, enabling access to global D&B data from the Oracle E-Business Suite. This removes the complexity from integrating D&B with Oracle and offers a cost-effective way to access D&B data in real-time from Oracle’s popular applications suite.
Dan Power, president of Hub Solution Designs, stated, “Accessing D&B information on over 140 million businesses globally allows organizations to increase the speed and accuracy of their decision making, while reducing their process costs and decreasing risk.”
With today’s fast-changing economic climate and the rapid pace of mergers & acquisitions, credit and marketing decisions have to be made quickly and accurately. Getting real-time information on prospects, customers and trading partners, in your backyard or around the world, is difficult. Integrating that data with Oracle applications can be challenging too.
Robin Walker, managing partner of Caliber Services, noted “Companies are looking for rapid time-to-value. HubCaliber Real-Time Access to D&B literally allows companies to be up and running, and pulling D&B information into their Oracle E-Business Suite environment, in a few minutes.”
About Hub Solution Designs, Inc.
Hub Solution Designs, Inc. is a management & technology consulting firm which specializes in developing and executing high impact Master Data Management and Data Governance strategies. For more information, please visit www.hubdesigns.com or blog.hubdesigns.com.
About Caliber Services, LLC
Caliber Services, LLC is a consulting company focused on Oracle Applications with a specialty in the Credit-to-Cash process, including expertise in Credit Management, Advanced Collections, and Promotions Management. For more information, please visit www.caliber-services.com.
###
Contact(s):
Dan Power
Hub Solution Designs, Inc.
+1 (781) 749-8910
powerd@hubdesigns.com
Modeling the Blueprint for MDM
digg this | del.icio.us | Reddit | Stumble It!
Several practitioners have contributed to this complex subject (see Dan Power’s Five Essential Elements of MDM and CDI, for example) and have done a good job at describing the critical elements. There is one more element that’s often overlooked however, and it remains a key differentiator and all too often, it’s the difference between success and failure among the major initiatives I’ve had the opportunity to witness – modeling the blueprint for MDM.
This is an important first step to take, assuming the business case is completed and approved. It forces us to address the very real challenges up front, before embarking on a journey that our stakeholders must understand and support. Obtaining buy-in and executive support means we all share a common vision.
MDM is more than maintaining a central repository of master data. The shared reference model should provide a resilient, adaptive blueprint to sustain high performance and value over time.
An MDM solution should include the tools for modeling and managing business knowledge of data in a sustainable way. This may seem like a tall order, but consider the implications if we focus on the tactical and exclude the reality of how the business will actually adopt and embrace all of your hard work.
Or worse, asking the business to start from a blank sheet of paper and expect them to tell you how to rationalize and manage the integrity rules connecting data across several systems, eliminate duplication and waste, and ensure an authoritative source of clean, reliable information can be audited for completeness and accuracy. Still waiting?
So What’s in This Blueprint?
The critical thing to remember is the MDM project is a business project that requires establishing a common information model that applies whatever the technical infrastructure or patterns you plan on using may be. The blueprint should remain computation and platform independent until the Operating Model is defined (and accepted by the business), and a suitable Common Information Model (CIM) and Canonical Model are completed to support and ensure the business intent.
Then, and only then, are you ready to tackle the Reference Architecture.
The essential elements should include:
- Common Information Model
- Canonical Model
- Operating Model, and
- Reference Architecture (e.g. 4+1 views).
I’ll be discussing each of these important and necessary components within the MDM blueprint in future articles in this series, and I encourage you to participate and share your professional experience. Adopting and succeeding at Master Data Management is not easy, and jumping into the “deep end” without truly understanding what you are solving for is never a good idea.
Whether you are a hands-on practitioner, program manager, or an executive planner, I can’t emphasize enough how critical modeling the MDM blueprint and sharing this with the stakeholders is to success. You simply have to get this right before proceeding further.
Continue with Part 2.
