In the Field with our Experts

MDM and All That Jazz

By Rob Paller, Consultant

Recently, senior consultant Bob Wall mentioned building the team as one of four proven starting points for getting your MDM program rolling. By now, you have probably identified the C-level executive experiencing the most pain as a result of working with unmanageable data. You have even gone so far as to identify a small project that allows you to demonstrate the value that MDM will provide the company.

However, the success of your MDM pilot project and overall MDM strategy will come down to the team you assemble to support it. Interestingly enough, there are a few parallels that can be drawn from what makes up a good jazz ensemble that can be applied to building your MDM team. Every musician must possess a core set of competencies—just like employees possess in order to do their jobs. However, in order for a jazz musician to excel, he must possess an additional set of skills that is, for the most part, unteachable, but enables him to reach a level where his inspiration and creativity matches his ability to execute. 

• A jazz musician must be a virtuoso of his instrument in order to communicate what he visualizes in his mind instantly through his instrument. An MDM team member must be a subject matter expert with an innate technical understanding of the source system or a similar understanding of the business processes surrounding the subject matter.

• A jazz musician must possess a trained ear that is not only able to identify something being "in tune" or "out of tune," but hear the melodic structures and chord progressions. A soloist must be able to adapt to the piano player making chord substitutions underneath the soloist's performance. Any member of your MDM team needs to be able to find the nuances in the business when it comes to defining the core subject areas of your business. For example, defining what a "student" vs "alumni" vs "donor" means to your university.



• Many jazz musicians are able to recall hundreds of songs at a moment’s notice—not only the melody, but the chords as well. Your MDM team must understand how and be able to explain the countless business processes and policies surrounding your master data.


• A good jazz musician must be able to listen and interact with the group in the present moment and contribute to improving the overall quality of the performance. This is one of the most important skills that an MDM team member needs to possess. The MDM team must be willing to listen to the rest of the business and communicate effectively. There are no ivory towers in MDM or jazz. 


Keep in mind when assembling your MDM team that it must be a balance of members from both the business and IT—much like a jazz ensemble consists of a balance among percussion, brass, and/or woodwind instruments. If you are unable to find a balance, you risk discord in what you produce.


photo by Fillmore Photography via Flickr (Creative Commons License)

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Rob Paller is an expert at business analytics and database administration. Since joining Baseline, Rob has been responsible for developing a case analysis system to streamline the oversight of food assistance benefits, implementing a common citizen data model, and assisting in the rollout of a new public assistance data model integrating data from over 10 years of legacy with a new benefit eligibility determination system.

Why Data Governance Matters in 2010

By Andy Field, Senior Consultant

Imagine a large family with many teenagers and one car. It’s Friday night, the parents are out of town, and the teenagers have plans in different parts of the city.  On top of this, everyone is broke and the car is out of gas.  This situation has been coming up more and more over the last few years.  Over time some of the kids get jobs and buy their own cars, trucks, motorcycles to get around.  They don’t figure in the cost of insurance, repairs, maintenance, or parking.   In retrospect they realize they can’t afford to keep this up and some of the vehicles, especially the motorcycles, just don’t meet their needs when, say, the weather gets bad.

If you can envision this  then you have a pretty good idea of what occurs in many companies that have not implemented a data governance program.  There is no common data warehouse platform, let alone other shared data assets, costs are out of control and there is no room to grow. And there are no formal ways of making or delegating decisions. Things go from bad to worse.

What if the kids got together and met with their parents to lay out their transportation needs in advance?  They may figure out that if they had two cars and a truck, instead of one reliable car, an old clunker, a beat up pickup and two motorcycles that 90 percent of their needs would be met over the foreseeable future at half the cost.  The parents and kids could also lay out who is in charge in their absence and how conflicting needs are to be resolved and gas costs shared.  With the savings over the current situation some money could be put in a pot for a taxi or rental fund when there is an extraordinary need for transportation.

Hopefully you can appreciate what a sound data governance program can do for your company.  It may substantively end years of organizational friction, bringing with it the inherent benefits of a collaborative work environment while reducing costs and improving information accessibility and delivery for everyone to help address business opportunities. The question is: are you ready?

Data governance is an essential component of an overall Enterprise Information Management program.  It provides the forum and processes for making all significant organizational and policy decisions with respect to how data is managed in the enterprise.  It includes formalizing decision processes around privacy, security, and accessibility of corporate information. It enforces accountability at the business level, specifically among subject matter experts, key business users, and data stewards. And it sanctions standards around products, customers, and other master data.  

Data governance is difficult, often broad in scope, and requires a structured process to get right. Data governance is typically deployed incrementally to address issues that span functional, project and geographic boundaries.  For local decisions where the scope of the data decision will only impact one project the data decisions may be limited to a subset of data constituents (though sanctioned by the data governance council).  At the other end of the spectrum when decisions need to be made that may have serious consequences to the brand perception of the company (such as privacy policies)  then executive input is required to balance the perspectives of individuals directly representing this leadership such as legal (General Counsel), marketing (Vice President), operations (COO) and IT (CIO).

So if your company reminds you of the family described in this blog post there may be a better path to family harmony through the implementation of data governance.  Understanding that data governance can involve the whole “family” with benefits for all who use information in their jobs is key to ensuring that the right constituents and scope are considered when planning its long-term deployment. Maybe 2010 is the year you’ll get started. Good luck on your journey!

photo by brightroyalty via Flickr (Creative Commons License)

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Andy Field has been involved in information management activities for over thirty years in both the private and public sectors internationally and domestically. He has held senior leadership positions accountable for establishing Enterprise Information Management practices in several organizations, including Fortune 500 companies. Andy has also consulted with clients from many industries and government sectors over the years and established and ran as president a consulting firm specializing in strategic information systems planning. He has broad experience in both operational and data warehouse projects from both a hands on and leadership perspective. Andy is currently a consultant specializing in Enterprise Information Management.

’Tis the Season for a Data Governance Carol (Part Two)

By Rob Paller, Consultant

 

The writing was on the wall, and Ebenezer was beginning to comprehend that while the mergers and acquisitions looked good on Wall Street, the company was imperiled by the aftermath of poorly integrated systems, an absence of proper data management, and a gaping void in the governance of the company’s data. Furthermore, Ebenezer knew that one more ghost had yet to call upon him. Never before had the fear of uncertainty enveloped Ebenezer, but tonight was different.

The Ghost of Data Governance Future

No sooner did Ebenezer begin to calm his nerves, when from the shadows of Ebenezer’s office, the Ghost of Data Governance Future appeared. Ebenezer’s pulse quickened as he did not want to acknowledge the ghost’s presence or what the ghost would ultimately share with him. It was with great reluctance that Ebenezer agreed to go with the final ghost.

Much to Ebenezer’s surprise, their travels did not take them very far into the upcoming decade before trouble surfaced. Poor Bob had suffered through another year and a half of cobbling reports together before reaching his saturation point and submitting his resignation. The remaining analysts scrambled to glean what they could from the frazzled, empty shell that was once the brightest among them. Surely a lone analyst would not beckon the fall of a Fortune 500 company, but it would begin the unraveling that followed in the months to come.

After Bob left the company, the remaining analysts fought to produce the fiscal reports on time. The data fixes that Bob orchestrated in the various systems to reconcile the reports were not being done. Some analysts were entertaining the idea of bringing in an outside firm to try and help address the problems, but IT was adamant they could shore up the problems on their end. Slowly the company’s competitive advantage began to erode and confidence on Wall Street went with it. It wasn’t long before rumors of bankruptcy began circulating. By then, Ebenezer had seen enough.

He couldn’t stand to see the company he so proudly guided through the first decade of the new century crumble before his eyes. He asked the Ghost of Data Governance Future if it was too late to turn things around. And if he promised to acknowledge all the data management, quality, integration, and governance issues that had been brought to light, would the company be able to avoid an inevitable collapse? But the ghost made no promises and offered no reassurances that the company could be saved. He simply returned Ebenezer to his office and quietly retreated back into the shadows of the office.

Ebenezer Wakes Up

Ebenezer, startled awake by the sensation of falling, quickly realized that he was still seated in his office chair. However, the relief was short-lived as he wiped the sleep from his eyes, only to see that the once-organized stacks of paper on his desk were strewn across the floor. Dream or no dream, the gravity of the situation was beginning to weigh on him:  The single most vital asset neglected for the past decade would be responsible for saving or crippling Ebenezer’s company.

There would be no quick solution. In order to build the momentum necessary to stay the course, Ebenezer needed to identify projects that would make an impact with the right sponsors who would, in turn, spread the word.

Slowly, over the course of several quarters, Ebenezer began to see the results of his mission to properly manage his company’s data as an asset. There were policies in place that supported the data governance initiative, and there were regular meetings to review the company’s progress with regards to an industry-approved maturity model. Data quality was being driven back to the source systems whenever possible.

There were formalized processes in place to deal with exceptions. There were master data hubs that dealt with integrating the various sources of master data across the company.  Data was beginning to move with the precision of a fine time piece.

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Rob Paller is an expert at business analytics and database administration. Since joining Baseline, Rob has been responsible for developing a case analysis system to streamline the oversight of food assistance benefits, implementing a common citizen data model, and assisting in the rollout of a new public assistance data model integrating data from over 10 years of legacy with a new benefit eligibility determination system. He also believes in Christmas, and he'll gladly give you a shilling to buy the prize turkey for Christmas  day.

’Tis the Season for a Data Governance Carol

By Rob Paller, Consultant

Charles Dickens wrote the classic story A Christmas Carol over 165 years ago, and since then, there have been countless adaptations from the stage to the silver screen. While watching Disney’s latest adaptation with my family, it dawned on me not long after Scrooge declared Marley “dead as a doornail” that there was one adaptation of this classic that isn’t nearly as dead as Marley. It was when Marley came back to visit Ebenezer that this adaptation started to solidify.

In this adaptation, Ebenezer is played by the CEO of a Fortune 500 company, Marley is the CIO, and Bob Cratchit represents one of the many white collar workers at this company.

This tale begins with the present-day departure of Marley from the company. The past decade had presented this company with the usual challenges: Y2K, September 11, the market meltdown, and lingering economic woes. The company had weathered most of these storms but faced an uncertain future—even after completing several successful mergers and acquisitions. Ebenezer was a frugal CEO, but even he knew that the current economic climate would be hard on the company. Even with careful planning.

Ebenezer was working late one night in December trying to plan for the upcoming year as the year-end figures were coming in. He realized that with several companies still reporting their information independently, it was going to be another long night. As each minute passed spent digesting spreadsheet after spreadsheet, Ebenezer fought to stay awake—but it wasn’t long before Ebenezer drifted fast asleep. He would soon be visited by Marley in his dreams.

Marley was shackled in chains, carrying the burden of every system introduced by a past merger and acquisition—only to be poorly integrated. Marley warned Ebenezer that if these systems were not properly integrated, if the data was not properly managed and mastered, and if they didn’t get a grip on their data governance, he could either risk being ousted by the board or losing the company under the weight of these mergers and acquisitions. To make sure Ebenezer understood the gravity of the situation, Marley told him he would be visited by three more apparitions.

The Ghost of Data Governance Past

The Ghost of Data Governance Past was the first to visit. This ghost would bring Ebenezer back to see how the company operated long before the mergers and acquisitions. While things were far from perfect, the issues surrounding data quality, compliance, security, and master data management were easier to address. This was a time long before many of these issues were on the radar of many CIOs. Back then, data warehouse teams did their due diligence to mitigate the risks associated with the lack of formalized processes and technology solutions to address these problems

As a result, there was a semblance of a single version of the truth and trust placed in the reports produced. But as the decade went by, the company grew organically, and through each merger and acquisition, the ability to integrate information became impossible. Ultimately, the company was left supporting federated systems that fed multiple warehouses and relied on multiple reporting systems.

Upon Ebenezer’s return to the lobby of his office building, he rushed to the elevators in a panic. Now that he had seen the 30,000-foot view of the path his company had traveled, he was scrambling to figure out how and where to start solving his company’s massive data disaster. Unfortunately, for Ebenezer, he would be visited by the next ghost before he could jot down a few scant ideas.

The Ghost of Data Governance Present

The Ghost of Data Governance Present was seated at Ebenezer’s desk and offered Ebenezer a seat in one of his own guest chairs. The ghost laughed while looking over the stack of reports that Ebenezer had left behind. With a swipe of the ghost’s arm, the stack of papers flew through the air, landing haphazardly across the floor. The ghost went on to explain that it didn’t matter whether the reports were collated or not—they couldn’t be trusted any longer. The ghost only needed to bring Ebenezer back a few weeks to show him all the trouble that Bob Cratchit had gone through to finalize the numbers for these reports.

Bob scoured over the numbers—only to be forced to dive deep into the tables in the countless systems to validate the numbers. When Bob discovered what he thought was a data quality problem, there was no process in place to fix the data. The subject matter experts could only commiserate with Bob about the problems and had little recourse in solving them. When all was said and done, Bob’s effort required a 60-hour week every fiscal period. Before he had a single iota of confidence in the results. Before he could send them on to Ebenezer.

The ghost reminded Ebenezer that he had only witnessed a single day of Bob preparing these reports, and that Bob had been suffering through this for years. He was the lone analyst capable of producing these reports since he had been around the longest and understood the intricacies that each new merger and acquisition introduced. But Bob could only be the unsung hero for so long. Eventually he would burn out and move on. The thought that Bob could leave the company was a burning question that Ebenezer tried to get answered, but the ghost of Data Governance Present offered no affirmation of Bob’s actions. The ghost returned Ebenezer to his office to ponder an uncertain future.

To be continued...

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Rob Paller is an expert at business analytics and database administration. Since joining Baseline, Rob has been responsible for developing a case analysis system to streamline the oversight of food assistance benefits, implementing a common citizen data model, and assisting in the rollout of a new public assistance data model integrating data from over 10 years of legacy with a new benefit eligibility determination system.

MDM and the Psychology of Data Management Maturity

By Bob Wall, Senior Consultant

I recently read an interesting article on the web entitled “Ascending the Data Infrastructure Hierarchy: The Five Stages of Data Infrastructure Maturity." ¹

In it the author discusses Maslow’s self-actualization psychology² and the Hierarchy of Needs (further information here) and creating an enduring Data Management Infrastructure. I believe it has implications to achieving data management maturity, which is a fundamental ingredient for MDM success.

As Baseline partner Evan Levy has explained in his MDM presentations, MDM is comprised of both “(Master Data) Management—capabilities to support master subject areas such as Customer, Product or Account—and Master (Data Management), in other words sustaining data management via standards, guidelines, and procedures. This includes the following Data Management functions:

• Data stewardship
• Data architecture, analysis and design
• Database administration
• Data security management
• Data quality improvement
• Reference/master data management
• Metadata management  


In trying to better manage these data management functions, reach an optimal level of data management maturity, and support a successful MDM implementation, Maslow’s hierarchy of needs and self-actualization psychology provides some helpful insight. In his epic psychology writing, Maslow organized human need into three broad levels: first, the physiological - air, food and water; then the psychological - safety, love, self-esteem; and finally, self-actualization. His insight was that the higher needs were as much a part of our nature as the lower, indeed were instinctive and biological. Maslow's greatness was in re-imagining what a human being could be.

The following schematic shows Maslow’s Hierarchy of Needs and an analogous Data Management Hierarchy.

 

Maslow’s Hierarchy of Needs and an analogous representation of a Data Management Hierarchy





We can see the 5 areas of the Data Management Hierarchy, mimicking Maslow’s Hierarchy of Needs, as follows:

Level 1: Tribal – Basic Needs (survival) 

At this level, an organization functions from day to day, but without any formal processes or systematic management. Instead, data management is left to the decisions of knowledgeable and skilled individuals.

Level 2: Enforced – Safety Needs (comfort)

As a company advances to the next level of the Data Management Hierarchy, the Enforced stage, it begins to adopt some semblance of governance, which includes documented standard operating procedures and putting in place change controls to better manage workflow within its data environment.

Level 3: Standardized – Psychological Needs

As organizations advance from the Enforced stage to the next level, they reach the Standardized stage, in which, as the name suggests, processes are established to handle various aspects of data management and less importance is placed on individual decision making.

Level 4: Actualized – Self-Actualization

In the Actualized stage, organizations are able to start getting more creative with their data. Data can be extracted from across the enterprise - as well as from outside sources - and combined to create useful information and made actionable to create new knowledge and value added business propositions.

Level 5: Peak Performance Peak Experiences

At last, the organization reaches the height of data management development, where it can devote all its resources to high-level strategic initiatives, rather than administrative issues.

MDM success is directly proportional to how well an organization integrates data management principles and reaches a peak performance/experiences maturity level.  Hence we can see the possibilities of the gradual application of Maslow’s Psychology of the Hierarchy of Needs, leading to the ascendance of data management maturity and re-imagining what a successful MDM project could be.

¹ John Bostic, Ascending the Data Infrastructure Hierarchy: The Five Stages of Data Infrastructure Management Maturity (Auerbach Publishing, 2007).
² Abraham Maslow, A Theory of Human Motivation (Psychosomatic Medicine, 1943).

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Bob Wall is a senior consultant with Baseline Consulting. He is an information technology specialist with 30 years experience in all areas of data warehouse administration, data architecture, data resource management, training, and applications systems development, as well as in corporate management.

Who’s on first when it comes to integrating customer data into a single version of the truth…

By Steve Bebout, Senior Consultant

 

We’ve all had situations when we think, Does this company even know who I am? Why do I get contacted about products or services I have ‘opted’ out or for which I am not a viable candidate?
Additionally, when I specifically request information, why can’t they provide me with that information?  Addressing these issues is complex but at the crux of why a single version of truth is worth investing in.

I was recently involved in a project where each line of business (LOB) was positioned against the other from a revenue standpoint.  Each LOB was rewarded by creating the largest amount of gross revenue for the company which made information sharing not a top priority.  It wasn’t a situation where they didn’t see that customer data was an asset; however the value of that asset was directly tied to the LOB’s profitability.  Senior management saw this as a problem and realized the opportunity cost associated with multiple versions of the truth.

Without coming to an agreement on a single version of the truth for customer, they would never know "Who’s on First" and how to maximize "who's" lifetime value.  Why am I using "Who’s on First?" as an analogy?  Well, it’s an Abbot and Costello routine that portrays confusion around a person’s identity.  In this parody, even with the best of intentions, they are talking past each other.  Their disagreements reveal their lack of understanding of "who" really is, what he does, and what his value is…

After creating agreement on the enterprise data definitions, senior management provided us with future business goals that we used to create key business drivers for the company.  The key business drivers were:

• Create efficient data accessibility and ensure data quality across the enterprise
• Measure the effectiveness of marketing activity
• Understand behavior for more targeted cross-sell and up-sell
• Share integrated, cleansed, and enriched customer data

Alignment was key to developing support and creating a new cultural view of data sharing.  Aligning analytical processes, key functional capabilities, and subject areas supporting customer behavior to business drivers created prioritization and a road map for creating a single version of truth.  How do you support your assumptions to the client?  We created traceability examples for the key business drivers mentioned above.

By providing the company with basic examples of how bringing together the customer data from the LOBs disparate sources into a single centralized data repository can provide value opened their eyes and was a key way to get the enterprise ‘onboard’ to create a single version of the truth.

I found traceability matrices to be a valuable tool for resolving the multiple versions of the truth in identifying ‘who’, has anyone else used this approach or have other techniques that they have found been successful?

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Steve Bebout is a consultant with over 15 years of experience in information technology in the areas of application development, business requirements, data analysis, and project management.  He has experience in the Communications Media and Entertainment, Financial Services, Life Sciences, and Insurance vertical markets; spending the majority of his efforts on customer data integration initiatives. 

Real-Time Data Quality Through MDM

By Bob Wall, Senior Consultant

I have found that one of the biggest challenges for data migration, data integration and data warehousing projects that I’ve worked on, is the requirement for clean data. Typically, one of a plethora of data quality/cleansing tools is deployed. The tool employs sophisticated heuristic, probabilistic, deterministic, phonetic, linguistic and empirical methods and algorithms to perform data quality analysis. For example, for customer data being integrated into a data warehouse, we want the tool to reconcile Easthartford, Hartford East, Hartford and East Hartford to the same physical address. The data quality process is usually run in a batch process, first for the initial load and then continually after that.

Today, operational MDM hubs present an even bigger challenge. We have to ensure synchronization across multiple source systems and all data must consistently be correct. The data quality checks must be applied at various stages within the master data lifecycle, and must support federated sharing across and between systems, databases and applications. In a sense operational MDM requires real-time data quality.

Digressing for a moment, I have also worked on projects that employed various search engine technologies. On one in particular, a client wanted to have an automated way to categorize structured and non-structured web data (emails, documents, images, etc.); search for certain conditions and do it in an automated fashion. We investigated some technology that used what was referred to as the semantic web technology, which incorporated advanced semantic and linguistic analysis with classification schemes (using Hyper Text Markup Language (HTML), eXtensible Markup Language (XML), Resource Description Framework (RDF), and Web Ontology Language (OWL)) to render web content machine-readable and make it capable of being searched in an automated fashion.

Software tools that support inline SOA data quality services combined with advanced semantic and linguistic analysis/machine learning capabilities are beginning to evolve. Microsoft’s purchase last year of Zoomix is a testimonial to the strategic value of these types of products. The convergence of data quality and semantic web technologies may provide operational MDM projects with the ability to automate ongoing classification, matching, and standardization of master data records. I think it is definitely worth keeping an eye on to see if it leads to real-time data quality capabilities embedded in MDM tools.

photo by Blude (via Flickr)

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Bob Wall is a senior consultant with Baseline Consulting. He is an information technology specialist with 30 years experience in all areas of data warehouse administration, data architecture, data resource management, training, and applications systems development, as well as in corporate management.

Triple Play Package for MDM

By Bob Wall, Senior Consultant

Several months ago I decided to upgrade my home internet connection to broadband and discovered that several of the carriers offered a bundled package that allowed me to not only get an upgrade for my internet service, but also for my telephone and television services at the same time. It would offer better speeds, consolidate my bills and most importantly save me money. This bundle is often referred to as a “triple play” deal. So recently I decided to upgrade to a triple play package and I have been extremely pleased with the services and the cost savings.

Baseline offers a similar set of services that we deliver to companies to help out with with their master data management strategy. It’s called the “MDM Masterplan” and it combines a set of MDM specific tasks and skills to deliver an overall MDM roadmap. This can save companies an inordinate amount of time and money, focusing them quickly on their objective and how to reach them. The service is its own type of  “Triple Play for MDM.”  Instead of an internet, telephone and television bundle, the service bundles three core MDM activities:  Business Scoping, Architecture & Technology Review, and Roadmap Development:

Business Scoping is analogous to the internet service in the home triple play package because it allows you to discover the company’s readiness and chart a path—kind of like a Google search!

Through review of business and IT documentation and interviews of key IT executives, MDM sponsors, key business and IT stakeholders, try to understand their concerns for MDM, opportunities for further efficiencies, the organization’s business priorities and challenges, and MDM business requirements.

Architecture & Technology Review is analogous to the telephone service in the home triple play package because the strength of the telephone service is the backbone architecture design and technology network of the service provider.

Through review of the “current state” technology development and processing environment, a gap versus the “desired state” environment, and  identification of the existing methods, practices, and functional capabilities available to support the business capabilities associated with MDM, come up with a blueprint for an MDM architecture and the required technology to support it.

Roadmap Development is analogous to the television service in the home triple play package because you can tune in and plan your future viewing.

Reconcile the business and technical findings to produce a moving forward MDM implementation plan that is based on the company strategy, business priorities, and technical activities necessary to deliver an MDM initiative in an iterative, business-driven approach. 

In summary, this “Triple Play for MDM” can assist a company in preparing for an MDM implementation and ensure that MDM is not only well-planned, but well-packaged and priced for success!

photo by rick (via Flickr)

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Bob Wall is a senior consultant with Baseline Consulting. He is an information technology specialist with 30 years experience in all areas of data warehouse administration, data architecture, data resource management, training, and applications systems development, as well as in corporate management.

Welcome to the MDM Show, “What’s My Role?”

By Bob Wall, Senior Consultant

Years ago, I regularly watched a television show called “What’s My Line”, moderated by John Charles Daly. The show featured a panel of four celebrities (e.g. Bennet Cerf, Steve Allen, Dorothy Kilgallen and Arlene Francis) who questioned the contestants and tried to discern their occupation. The panelist would begin asking the contestant yes-or-no questions. If they received a “yes” answer they continued, but if they received a “no” questioning passed to the next panelist and $5 was added to the prize tallied on cards Daly would flip over. A contestant won by receiving ten "no" answers (as Daly occasionally noted, "10 flips and they are a flop!") or if time ran out.

If you’ve worked on a Master Data Management (MDM) project, specifically a Customer Data Integration (CDI) effort,  you may think you’re in a remake of “What’s My Line”, but now it is called “What’s My Role.” Because we are trying to integrate customer data from possibly dozens of systems, we need to understand what role the customer played in that system.

For instance, we may designate Jane Doe as a customer. For argument’s sake, a customer defined as a person or organization who has purchased one of our products, but in the shipping system she shows up as ship-to-customer, in the accounts payable system as a bill-to-customer, and in the HR system as an employee.

One way to get around this is to use a generic Party data model (with subtypes of Person or Organization) and to use 2 types of roles for that Party; Declarative and Contextual. For a Party, we might define a “role” for the party, and declare that role as “customer,” the part that an organization or person plays within the enterprise. We then might look at the associative relationship between a source application system and Party and define a contextual role, how a party is or was involved within the context of another entity. Here the roles might be ship-to, billed-to, or employee, but we always establish one instance of the specific party, in this case Jane Doe. A good discussion of using roles can be found in Len Silverston’s “The Data Model Resource Book, Volume 3, Universal Patterns for Data Modeling.”

Using a generic approach to roles for your MDM/CDI project may help you to integrate these roles and not wind up like panelists on “What’s My Role,” where you could face 10 flips and you’re out!

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Bob Wall is a senior consultant with Baseline Consulting. He is an information technology specialist with 30 years experience in all areas of data warehouse administration, data architecture, data resource management, training, and applications systems development, as well as in corporate management.

Get on Board the MDM Express

By Bob Wall, Senior Consultant

Imagine if you will you're the CIO of a mid-market pharmaceutical company and you've just been to a TDWI conference that had a session (“Introduction to MDM for BI Professionals”) that provides an overview of master data management. You’re heading back to your corporate headquarters by train. You feel convinced that like this train, MDM is speeding along gathering momentum, and you don’t want to be left at the platform and miss the opportunity to get your company on the MDM Express!

You may have heard that MDM offers a single version of the truth, enterprise-wide, about customers, products, and other information types. As CIO, if you look around at your company’s issues you realize right off the bat that customer data integration is a real issue. You think to yourself that it would be nice to be able to take an MDM strategy and implement it to consolidate, integrate and synchronize all of the customer data so that it would be more accurate and it would enable your sales and marketing teams to better leverage the market value of your customer relationships. However, you're faced with the daunting fact that MDM is a very complex concept—and potentially an expensive one. Moreover, many MDM vendors price their products such that they’re out of reach for the small to medium size company.

How do you get started?  Here are four proven starting-points for MDM:

1. Find the business sponsor. I know it’s trite and overused, but since you’re the CIO it’s likely you know where the pain is among your C-level colleagues. Enlist the CFO whose numbers don’t reconcile across billing and invoicing systems, or the CMO who struggles with a growing number of customer lists.
2. Engage a readiness assessment. You can do this with internal or external resources, but do it. Gauging readiness has little to do with whether people know what MDM is and more to do with your incumbent skills, data management practices, toolsets, and needs.
3. Define a Small, Controlled Project. Work with the sponsor and cut a slice out of the overall problem that you can use to not only demonstrate the value of MDM, but establish the technology foundation.
4. Build the team. You can’t just “assign” MDM to your BI or CRM team. You need to construct a new team of data-savvy practitioners who will own enterprise master data for the long-term.

Sure, there are other deliberate steps. Maybe you want to do an educational workshop to get everyone on the same page, or talk to some incumbent vendors about their offerings. But these tactics and others could backfire if they are ill-timed. Better to think globally but act locally, and reap the rewards.

photo by jurvetson (via Flickr)

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Bob Wall is a senior consultant with Baseline Consulting. He is an information technology specialist with 30 years experience in all areas of data warehouse administration, data architecture, data resource management, training, and applications systems development, as well as in corporate management.