In the Field with our Experts

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.

Public Sector takes SaaS for a Spin

By Rob Paller, Consultant

There has been a lot of talk about the growth and potential impact of Software as a Service (SaaS) in the realms of business intelligence, MDM, and to some extent data warehousing on Twitter and numerous industry blogs.  The premise behind SaaS is to move the hardware and software from your data center into the cloud. By doing so you reduce the costs associated with deployment and maintenance of the hardware and software. Much like Google has done with your email, documents, and photo libraries.

Google is turns out to be a great example because of their partnership with Panorama Software. Panorama Software is helping lead the charge by providing one of the first OLAP services in the cloud. Using Google Docs and Panorama’s SaaS solutions allow you to create pivot tables or take your cubes from SQL Server and move them into the cloud for collaborative analysis.  However, that has nothing to do with what is going on in the public sector and is best saved for another discussion.

Recently, one of our clients in the public sector has taken the concept of SaaS and applied it across the agencies and departments for many of the same reasons a small or medium sized business would consider looking to the cloud. Our client has established a group within its IT department whose purpose is to share services at an enterprise level to help drive down costs by reducing multiple contracts with the same vendors and standardize on the solutions offered. The solutions offered by this group range from data cleansing, data integration, business intelligence and data warehousing.  Going forward, this group is also working toward establishing a master data management solution as they begin to address the requirements to provide electronic medical records. A substantial benefit in applying the SaaS concept across the departments and agencies is found in the reduced cost in training staff that transfer from one department or agency to another and reduce time to delivery of new solutions. 

While the traditional SaaS concept may work great for the small and medium sized business the fact of the matter is that large corporations and even the public sector is less likely to make the jump into SaaS right now.  Do you think the spin being applied to SaaS concept at this client could work for large companies or other areas of the public sector? Let me know in the comments below or on Twitter.

photo by David Accampo (via Flickr)

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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.

What’s Your (MDM) Type?

By Bill O'Kane, Senior Consultant

 

photo by Dan4th

As part of an interview recently given by Evan Levy, he was asked to explain the various flavors of MDM implementation. The three generally accepted categories are persistent (all master attributes stored and maintained exclusively in the data hub for access by client systems), registry (sets of master attributes stored and maintained in client systems are registered, or “pointed to” by the data hub), and hybrid (also sometimes referred to as co-existent, where a copy of some or all of the client systems’ master attributes are stored and maintained both in the data hub and the client systems). While each MDM implementation is inherently different due to the uniqueness of each business and operating environment, these categories sufficiently encompass the vast majority.

I’ve come to realize that all of the projects I’ve been involved in have been of a single type: they began life as persistent-style hubs (and in fact, were justified this way in terms of a business case), and later became hybrid, as the costs associated with ancillary tasks (such as remediating all of the legacy batch processes to access the data hub) came to light during detailed source system integration planning and analysis.

I often discovered a heavy reliance on vendor-driven operational systems, which many times limited the ability to remediate certain technical processes. However, in spite of the fact that it became necessary to retreat from the completely persistent view from a tactical perspective, I believe that all of these projects remained successful because the persistent-style vision was kept on as a strategic guiding principle. In other words, the project teams were held accountable for creating data architectures that would support a persistent-style implementation, even if the source systems were allowed to keep their copies of the master data for a while longer. In fact, on some occasions, the project team was even asked to submit project plans for these future remediation tasks for use as time and budget might permit.

Although I’m less familiar with pure registry-style implementations, the idea of implementing something like this without a more persistent-style strategic vision to guide the proceedings makes me a bit uneasy. For example, questions I’ve heard come up more than once in regard to registry style: How is this different from the Virtual Data Warehouse concept we tried and failed to implement fifteen years ago?, or Won’t we just be creating another data silo? I have to confess, I don’t have a great response to these. Moreover, one of the things that originally drew me to MDM is that its nature (when persistent or hybrid) forces organizations to at last come to grips with the sins of their pasts in regard to data architecture, data governance, and metadata. 

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Bill O'Kane has twenty years of experience in the design, development, and implementation of large-scale Master Data Management (MDM) systems and Data Warehouses, with an emphasis on Customer Data Integration (CDI). His experience includes both the development of internal systems and the integration of vendor-based solutions in complex environments. Bill has achieved success in diverse corporate environments including Fortune 100 financial services and health management companies, mutually held insurance companies, and privately held software vendors.

Finding the Gold in Enterprise Data

By Adam White, Senior Consultant

Without control of their data corporations can no longer compete. The emphasis on globalization, new competitive pressures, and the widespread adoption of the Internet have introduced a new sense of urgency around data integration and management.  These days a company must not only have control of its physical data, the actual information that it has captured, it must have the ability to derive information from that data, interpreting it accurately and leveraging it for operational efficiencies and strategic advantage. And it must be able to deploy enterprise data across business users, including call center agents, point of sale staff, sales representatives, managers, directors, and executives. The data may also be required for direct use by customers and/or prospects.

Corporate control of data extends beyond just using data. Data management is also an emerging discipline in most companies. Data must be protected. There are regulatory requirements, customer privacy expectations, and the need to securethe customer base from competitors.  The unforeseen forces of a global economy mean that companies must be able to work with data that is not English. And, of course, the need to integrate the data across various external and internal silos means that point-to-point interfaces must be phased out in favor of a single version of the truth.

Indeed, many of your company’s databases are replicated to provide access to various constituencies. But the point-to-point interfaces they use to exchange data are costly both development and maintenance-wise. Control over heterogeneous data—not to mention sustained management and integration of that data—is next to impossible.  Application upgrades and data enhancements mean large, expensive projects that come with a high risk of failure. A deliberate approach to enterprise data integration can help IT save time, headcount, and rework, as well as launching efficiencies that can not only drive down costs—ensuring responsiveness, repeatable development practices, and data reuse—but, more importantly, delivering value to the business.

So what are the approaches that will give you control over your data and provide the functionality that you need to support your business?  There are different options for integration (the sharing of data and services), including: Extract, Transformation, and Loading (ETL); Enterprise Application Integration (EAI); Enterprise Information Integration (EII); and Customer Data Integration (CDI). The optimal solution at your company may be one of these, or a combination of all four. The point is that decisions about data integration remedies should involve a complete understanding of the differences of data integration solutions, and a deliberate technology selection process.  Also, the implementation of the solution means that you will have to have a robust data governance and data management framework in place so that data integration supports your needs going forward, and doesn’t over time revert back into the original legacy architecture, losing the capabilities that are instrumental in implementing the solution in the first place.

Note: Baseline Partner Evan Levy will be teaching a class at the TDWI World Conference in Las Vegas next month, titled Architectural Options for Data Integration, in which he will outline the differences in these and other integration approaches, compare the strengths and weaknesses of each, and offer lists of vendors in each category. I strongly recommend attending!

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Adam White is an expert in Customer Data Integration (CDI), Services Oriented Architecture (SOA), Systems Development Life Cycle Methodology, Project Management, IT Governance, and establishing IT Process and Organization. Since joining Baseline, Adam has been responsible for conducting enterprise CDI assessments, implementing CDI, implementing SOA architecture, and implementing development methodologies.

One Problem, Three Parts: Making MDM Work at a Bank

By Frank Dravis, Senior Consultant

We have a banking client that is grappling with how to get a single view of their customers from the nine major types of systems they have and the myriad of subsystems. You’ll recognize those systems as you probably have them yourself: Billing, Product, Agent, G/L, Call Center, etc. When you first approach the problem there is a tendency to think, “Oh, this is a data warehouse problem. The bank wants to get a 360 degree view of their customers.”

But the problem is that our client’s challenges and issues go deeper. Yes, a data warehouse would achieve one objective of their goals, getting customer data from heterogeneous systems loaded onto a common platform, but the problem is more multidimensional. 

A main component of the solution is customer master data management, otherwise known as Customer Data Integration (CDI). CDI solutions automate the harmonization and integration of customer data from operational systems. Unlike a data warehouse, CDI guarantees that the data is cleansed and that business rules are applied before the customer data is stored on the CDI hub.

However, for the CDI system to function effectively it cannot do so in isolation. The rules it needs to apply should be defined. Enter data management. Data management is the practice of implementing rules and policies on data.  It is all about building systems and processes  to ensure consistent application of data quality, data integration including ETL and federation, metadata management, security, access, etc. Moreover, data management establishes the framework and culture for the original source systems to turn into target systems when the CDI solution serves as the system of record or reference and sends data back. Yes, for those of you who have implemented a successful MDM system, you have some form of functioning data management effort.

But unless senior management at the bank is willing to engage in sanctioning the right business rules, security and access policies, and corporate definitions, neither CDI nor data management will work. Thus data governance is a critical component of the bank’s long term data integration success. Understand, that data governance comes in big and little forms, such as the more operational policy of “the parts data mart shall attain a quality score of 82% in within the next six months.” At the bank, data governance policies serve as crucial guidance to business, IT, and their data stewards. 

So the next time you see one of those star-like diagrams with disparate data sources all feeding towards the customer center, consider there is a multi-dimensional view of that diagram: MDM, data management, and data governance.

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Frank Dravis is a senior consultant with 21 years of experience in enterprise information management (EIM) and data quality solutions design, implementation, and consulting. He specializes in data integration, data quality, and data governance solutions, advising key clients and industry vendors.