Keep Your Eye on the Enterprise: Developing a long-Term master Data management strategy

Keep Your Eye on the Enterprise: Developing a long-Term master Data management strategy
Developing a Long-Term Master Data Management Strategy

Keep Your Eye on the Enterprise:
Developing a Long-Term
Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                            1

For business decision makers, there is perhaps nothing
more troubling than realizing that you’ve painted
yourself into the proverbial corner as the result of earlier
decisions. What do you do when further progress will
either complicate or undo the groundwork you’ve
carefully laid—and often at considerable expense?

Many life sciences companies find themselves in this unfortunate situation when it comes to their systems
for managing their master data—information on customers and products that’s used by multiple functions in
the organization. Given the cost and complexity of developing an enterprise-wide solution, they’ve allowed
spot solutions to proliferate function by function, only to have integration issues later on.
However, companies need not be forced to choose between an expensive and powerful enterprise tool that
they don’t yet need and functional solutions that won’t scale later on. With proper forethought and careful
planning, they can ensure that their MDM solutions solve their immediate needs and build toward what
they’ll need in the future.

Setting a Master Data Management (MDM) strategy for a life sciences company is one of those
responsibilities that largely goes unnoticed by downstream information consumers—that is, unless it was
shortsighted or overly ambitious. Adopting an enterprise-wide solution that isn’t agile enough may leave
some users frustrated as they stand in line for their unique needs to be addressed. Yet, allowing functions
to choose their own solutions could lead to incompatible systems that are at odds with the very purpose of
MDM: having a single version of the truth for a customer, a product or any of the MDM domain records.
So how do you choose between an enterprise-wide solution and those that are specific to individual
functions? Is it possible to start with tools at the functional level and “grow into” an enterprise system? Can
multiple MDM solutions “peacefully co-exist?” When should an organization make the leap from single-
purpose systems to a broader solution that’s standardized across the company?

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                           2

Here we’ll offer our views on how life sciences companies can think strategically, preparing for the long
term, and act tactically to satisfy local and immediate needs when developing their MDM systems. Spoiler
alert: the key is to understand where your company is headed and create a “road map” that will guide you
along the way, ensuring that any tools created for a single-use case can be integrated later on. Decisions
made today will not need to be countermanded tomorrow if they’re made with all of the right future

Functional Vs. Enterprise Solutions
“Functional MDM solutions” are those that are designed specifically to satisfy the needs of a particular
business function—for example sales (or even a specialized sales force) or managed markets or compliance.
They are, by definition, limited in scope and geared to a specific purpose. In contrast, “enterprise MDM
solutions” are those that serve multiple company functions simultaneously, and they may even be common
across geographies. They are powerful, complex and comprehensive systems, yet difficult to modify or
upgrade once they are in production.

Because functional MDM systems address more focused needs, they don’t need a great deal of processing
power and are less expensive to create than systems that must address the diverse needs of multiple
stakeholders. They are practical, fit-for-purpose solutions, so they don’t automatically include features and
utilities that aren’t needed. Typically, they require only a limited amount of configuration and can be up and
running in a fairly short timeframe, providing quick “time to insight” for the specific functional team.
Small and emerging companies may turn to separate functional MDM systems before they are ready or able
to move to an enterprise system. Alternately, in large companies that have installed enterprise systems, a
functional system may simply be an easy way to meet an individual group’s needs quickly and painlessly. A
new business unit or a new sales force may, for instance, need an MDM solution but be unable to wait until IT
is able to incorporate its specifications into the existing company-wide solution. Many of today’s functional
MDM systems within the life sciences industry have sprung up in just this situation. Even though the
company has invested in an enterprise system, there remain pockets of underserved master data users within
the organization that cannot wait for the company-wide tool to be configured for them.
For all their benefits, functional solutions have three potential drawbacks. Each can create a serious problem
that can be expensive and time consuming to fix, but they are all avoidable with the right structure and
technology. The first possible issue is that companies run the risk of outgrowing the functionality of their
selection; the tool itself may not be robust enough to adapt to broader uses and more complex types of

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                            3

data as users’ requirements grow. The second is that separate systems can, if not governed properly, quickly
spawn multiple, conflicting records for the same entity. When this happens, there is no longer anything
“master” about the data and the information can no longer provide a “single version of the truth.” And third,
inevitably, a functional solution will need to share or receive information from another functional system, and
thus begins a spider web of complexity that many companies can’t maintain over time.
To summarize, functional solutions are sufficient when only limited scope is required. However, even then,
they must offer an upgrade path to more advanced features and broader application—at a price point
and implementation timeframe that remains in line with the original intent of the functional solution. If by
upgrading a functional solution it will become costly and unwieldy, you would have done better to have
chosen an enterprise-wide solution at the outset.

Enterprise-Wide Solutions
Solutions designed to serve multiple stakeholder groups are, naturally, more time-consuming to design and
implement because they must address so many different downstream users’ needs, all in one. Imagine the
complexities in having to gather and satisfy user requirements from sales, sales reporting, marketing, speaker
management, managed markets, the call center, compliance, finance, operations, and research. It is very
difficult, organizationally, to reach the consensus required to design an enterprise-wide system.
The underlying technology for such systems, of course, needs to be powerful, flexible and capable of
managing massive amounts of information. Typically, these systems are highly configurable and include a
wide array of capabilities in anticipation of nearly every conceivable demand. In that respect, they are the
“aircraft carriers” of the technology world.
While enterprise-wide systems can be more time consuming and costly to implement—a process that can
take months or even years to complete—companies can realize savings in integration efforts and ongoing
maintenance because the support team can be consolidated. Because they need only maintain one tool,
companies can develop a center of excellence in the skills required.

On-Premise vs. Cloud-Based Solutions
There are several options for the technology platform that supports an MDM system—whether at the
functional or enterprise-wide level. It can reside on premise, as it has been done historically, or “in the cloud,”
meaning that it is accessed via the Web and computing resources are available on demand with pay-as-
you-go pricing.

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                            4

The advantages of a cloud-based solution over an on-premise solution are the same in MDM as in other
popular cloud-based offerings. Briefly, they include:

  • Lower  start-up costs. Subscribers do not need to invest in software, hardware, data centers, disaster
    recovery, redundant systems, or IT staff.

  • Faster implementation. MDM as a Service takes advantage of immediately available infrastructure.
  • Ongoing    savings. When the MDM software is purchased as a service (SaaS), the license fees associated
    with on-premise solutions are eliminated.

  • Automatic    enhancements and upgrades. The service provider makes improvements as needed, without
    having to push out new releases sporadically that lead to significant disruption and downtime for users.

  • Usage-based    pricing. As with utilities, subscribers pay for only the services that they use and do not incur
    costs for capacity and features that they simply don’t need.

  • Scalability. Cloud-based applications can be scaled up or down as needed. The solution maintains
    performance levels while delivering new capabilities.

  • Painless upgrades. SaaS solution providers must invest in keeping up with industry changes and in
    providing a solution that keeps subscribers current. All updates and upgrades are provided as part of the
    service; there are no patches or hot fixes to install … and no need to worry about the loss of support from
    a license expiring.

  • Accessibility and security. SaaS solutions are available via a browser and secured behind unique user
    credentials and encryption protocols. Thus both remote and in-house users have connectivity to the
    system while corporate intellectual property is protected.

  • Simplification. A SaaS application leverages industry-standard practices, which translates into less
    complexity across integrated commercial systems.

  • Strong  support. SaaS solutions are usually a center of excellence for providers, offering highly trained
    support resources for which the costs are spread across multiple clients.

Making the Choice
There is no automatic right or wrong answer as to which type of MDM system (functional or enterprise) is
best in any given situation. It depends on a number of factors:

  • What  types of data are involved, and who needs access to them? Does one function, such as Finance,
    need to perform analyses on data drawn from across the organization? Or, do multiple functions need
    access to the same information—such as product reference data? In these cases, the database may require
    global management, arguing for an enterprise-level solution. (An exception might be if a company only
    has a small number of products, in which case it may not need a product reference hub just yet.)

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                          5

    Or conversely, does the information pertain to a unique customer universe, such as oncologists who
    are of interest only to the oncology sales force, for example? In such a case, functional MDM may be a
    sensible solution.

 • Does  the company’s strategy require a 360-degree view of the customer? When a company decides
   that everyone with customer contact should be privy to all interactions with a given customer, the system
    must be powerful enough to provide commercial teams with a comprehensive customer record. Until
    such integration is necessary, it is quite possible to make do with functional solutions—provided that
    they follow certain design and governance principles as discussed below. (Note: A single, 360-degree
    view of a customer does not preclude different users from looking at a given customer differently, based
    on their functions. For instance, the same physician may be of interest to research and sales, but the
    individual attributes that are important to each group may well be different.)

 • Are there efficiencies to be gained by adopting one system? Admittedly, it is difficult to calculate the
   return on investment from implementing an enterprise-wide MDM solution because the benefits are
    realized downstream by various users, not by the MDM team itself. Even so, there often comes a point
    when it becomes too costly and cumbersome to manage and maintain data with different tools using
    different matching rules. Companies can also realize efficiencies by storing and managing using one tool
    with one support team.

 • How   much rigor is required to support data quality? Typically, supporting an MDM solution does
   not require a consistent level of resourcing. There are peaks and valleys with respect to special projects
    and data stewardship workloads that require flexibility in resources. By its nature, an enterprise solution
    should be capable of providing support for planned spikes in workload to ensure that negotiated service
    levels are attained consistently.

 • Are  acquisitions, mergers, or co-promotions part of the business strategy? Strong practices related
   to reference data management are critical for organizations that must support acquisitions, mergers, and
    even co-promotions. Customer, activity, and sales data must be aligned with outside parties to ensure
    required synergies. In one case of a merger of two large pharmaceutical companies, the capabilities
    provided by one company cut the time to commercial integration of the sales teams by over 60 percent,
    thus setting a new industry benchmark for integration excellence.

      There is no automatic right or wrong answer as to which type of MDM system
      (functional or enterprise) is best in any given situation.

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                                               6

Recommended Practices
Companies can avoid the perils of going down the wrong path by following certain guiding principles in how
they select and design their MDM systems. By following the recommendations below, companies can make
any combination of functional and enterprise-wide systems work in the short and long term:

  • Companies
    Take a long-term view
               should have a clear idea of the type of system they will want and need at least five years from
    now. Figure 1 illustrates the type of broad map that can guide choices. It is, in fact, safe to assume that in
    time, all successful companies will want and need an enterprise solution. They need not start there, but
    they must plan for the fact that their data and technological needs will almost certainly become more
    complex and more intertwined over time. Having this as an end goal will dictate certain decisions along
    the way and make the eventual transition to an enterprise system smooth. Companies can be guided in
    their journey by measuring their progress against an MDM maturity model.
    If a company’s immediate needs can be met with a functional solution, the strategy and platform can
    be provided through an MDM as a Service solution. This approach will permit the eventual growth to an
    enterprise solution with minimal pain and effort.


                                                          Customer Channels
                      Field          Call       Speaker      E-Detail     E-Sample            Direct   Convention         Web
                      Force         Center     Programs                                        Mail

                   Organizational                                                                              Operational
                                                               A           GOV
                                                            DAT               ER
                                                                                         NA                         Campaign


                      Marketing                               Analytic                                                 Comp
                        Market                                                   Sales                             Segmentation
                       Research                                                                                     & Targeting
                                                                  Interactions                                       Travel &
                                                               TA A C C E S S                                         Brand
                       Markets                                                                                       Planning

                                                          Business Intelligence
                     Reports       Aggregate     KPIs       Customer      Campaign        Customer      Market         Statistical
                                    Spend                   Segments        ROI            Analysis     Analysis        Analysis

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                           7

 • Rely on a single, base source of industry data.
   Having a single source for customer and product reference data as a common underpinning is what
    enables separate, functional MDM systems to speak to one another today … and then be easily
    integrated into an enterprise-wide system tomorrow. Even separate systems should all start with the
    same basic profile information and use common definitions and identifiers. External sources can be
    supplemented with internally sourced data as needed. Companies that have opted to integrate multiple
    industry reference sources have found it very difficult to manage the conflicting updates and definitions
    that exist across sources.

 • Keep   MDM processes and technology separate from the applications that will use master data.
   It can be tempting to build an MDM system within an operational application (such as a data warehouse
    or a customer relationship management system or a financial system). But, what on the surface may seem
    like a natural fit can actually be quite problematic. This is the case because the tool’s layout, data models,
    and fields are all prescribed by the application, not by the data need. You cannot, for instance, add
    customer types or attributes that are not important to, or recognized by, the application, no matter how
    vital they may be to the business operation. Additionally, operational MDM systems are usually limited to
    the customer data that is used by the application and does not facilitate integration of multiple, diverse
    sources. The rigidity and narrowness of an MDM solution emanating from a functional application limits
    both the current utility of the system and hinders—if not prohibits—expansion over time.
    In contrast, when the MDM system (be it functional or enterprise-wide) is built with open application
    program interfaces (APIs), it can accommodate real-time access from various applications. MDM systems
    that are built independent of operational applications can expand and change with the organization’s
    needs, without “breaking” the application itself. In a word, they are nimble.

 • Adopt    a common underlying technology across MDM systems.
   If different business functions are permitted to adopt MDM systems that emanate from their primary
    operational applications, multiple technology platforms are bound to spring up within the organization.
    And that, naturally, creates challenges for maintenance and integration.
    The technology platform on which MDM systems are built should integrate seamlessly with the
    company’s systems and applications such that users can sign in once and move from one application
    to another. The best architecture centralizes customer data in a customer hub that feeds into multiple
    back-office systems (such as sales reporting, compliance reporting, compensation, and contracting)
    as well as integrates with customer-based applications including sales force automation, call center
    support, order management systems, expense and finance systems, campaign management, speaker
    programs, websites, and physician portals. (See Figure 2.) The resulting tools all share a common look
    and feel and can be supported by a common technical team and training resources.

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                          8

Figure 2: A Customer Hub Approach to MDM

                                     Call                                               Physician
                  SFA/CRM                            Order Mgt       Website
                                    Center                                               Portal

            Master Data
                                               Customer HUB

                            ERP                             Sales Comp         Legacy

 • Establish data governance policies and processes to maintain data integrity.
   Regardless of whether a company is supporting multiple functional MDMs in anticipation of creating an
    enterprise solution later, or already has an enterprise-wide solution, it must establish data governance
    principles around the data and its use. These include:

    • Standards around data definitions and taxonomy, metrics, and measures
    • Policies and processes related to monitoring, measurement, change management, data access,
      and delivery

    • Defined roles and responsibilities related to data acquisition, maintenance, and use
    Such an approach will ensure that there is one true representation (a single version of the truth) of the
    customer or product that underpins whatever applications may tap into the master data from across
    the organization.

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                        9

 • Give careful consideration to the data stewardship model employed.
   The quality of master data must be maintained by stewards who are able to make decisions on the
    disposition of individual data records according to the data governance rules that have been established.
    The best data stewards are those who work with life sciences reference data for a living and who
    thoroughly understand the industry and all of the applications of master data. It is impossible to properly
    steward data without understanding how different commercial teams engage with customers, the
    regulations that apply to them, and the industry dynamics they face. Indeed, entrusting this work to
    professionals is one of the key benefits for contracting with a provider for master data management as a
    service. Companies that do so benefit from the stewards’ expertise and realize faster ramp up time, more
    timely resolution of data inquiries, and improved data quality. At the same time, they reserve their own
    business experts for working on the core business functions for which they are responsible.
    A company’s data stewardship model can either be federated or distributed. In the former, changes or
    updates to all data records are made by one set of stewards using one, all encompassing set of business
    rules. In the latter, they are pushed out to stewards responsible for each functional area, recognizing
    that it can be very difficult for one set of stewards to understand the needs of each functional area
    with the depth required. The best structure must be developed on a case-by-case basis, considering
    the functional areas served by the solution, the complexity of the data and business rules, and
    organizational decision-making structures. Most functional MDM solutions will employ a distributed
    model, but this model should be based on common standards, policies, and processes across the
    broader organization.

 • Provide  reports to stakeholders on system performance and usage.
   The system(s) put in place should be capable of producing reports for stakeholders on the quality,
    timeliness, and accuracy of the master data contained within them. Monitoring this output is an
    important step in maintaining data standards as well as helpful when a company is considering moving
    from a functional MDM structure to an enterprise system. These metrics should be used to measure the
    performance of the organization’s data governance function.

      Companies can avoid the perils of going down the wrong path by following certain
      guiding principles in how they select and design their MDM systems.

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                                                                10

Upgrading to an Enterprise System
If disparate functional systems have been allowed to proliferate without a common, underlying technology,
a single data source as the backbone, and strong governance principles, graduating to an enterprise-wide
system is infinitely more difficult. Essentially, a company will need to apply the seven steps detailed in
Figure 3, a significant undertaking. Costs accrue from needing to rework processes, renegotiate with various
vendors, adopt new technology, and match and harmonize the data. As a rule of thumb, it is safe to assume
that 25-60 percent of the data records from different functional systems will require manual verification
during the conversion to an enterprise system depending on rigor of the individual MDM solutions.
The preferable approach is clearly to have a future ready solution that takes into consideration an expanding
use case for MDM. Functional solutions built according to the principles above can easily meet this criterion.

Figure 3: the seven steps of master data management

                                                        Master Data Management

                STEP              STEP              STEP                 STEP                 STEP                 STEP               STEP
                ONE               TWO               THREE                FOUR                 FIVE                  SIX               SEVEN
                 Source          Load and        Match, Merge           Steward               Report               Publish            Consume
                                  Cleanse        & Augment                                   and Audit

             BIG DATA          • Client files    • Exact and         • People, process    • Operational        • Standard          • Systems
             SOURCES             converted to      fuzzy matching      and technology       and quality          outbound            integration
                                 standardized                          to reconcile         reporting            interface layer     services
             • Dimensions                        • De-duplication
                                 format                                gray area                                                     to enable
             • Transactions                                                               • Maintenance of     • Enable web          downstream
                               • Name, address   • Augmentation        matching             cross-references     services for
             • Relationships                       with reference                                                                    use of
                                 and other                           • Manage data        • History of all       data access and     integrated
                                 parsed            attributes          quality                                   functionality
             REFERENCE                                                                      changes and                              data
             DATA                attributes      • Create ‘golden’   • Manual merges        updates            • Role-based
                                 standardized      record                                                                          • Analytical and
             • Customers                                             • Control what is                           visibility and      operational
                                                                       of value and how                          business rules      MDM users
             • Products
                                                                       it is defined
             • Procedures
                                                                     • Workflow
             • Patients                                                management

              STEP ZERO         Governance – People, Processes, Scoping, and Definitions to ensure Quality.

Developing a Long-Term Master Data Management Strategy
DEVELOPING A LONG-TERM MASTER DATA MANAGEMENT STRATEGY                                                                     11

Vendor Selection Criteria
No matter where you are in your MDM maturity journey and which type of MDM system will best address
your needs at the moment, the provider you choose should:

  • Have the ability to both meet your current needs and scale with you as you grow. What meets your needs
    today, may not in five years. If you begin with the right foundational elements and work with the right
     vendor, you can make a smooth transition over time.

  • Be intimately familiar with the master data itself and how it is used throughout the organization.
  • Understand    the life sciences industry and the workings and requirements of all the functions within it that
    have a need for master data.

  • Beeffectiveness.
         ISO 9001 compliant. The accuracy of your master file is not merely a matter of efficiency and
                     It is a critical company asset that has strong legal and compliance implications, and its
     management is a specialized responsibility.

All life sciences companies should assume that, no matter where they currently are in their MDM
development, they will eventually want and need a common MDM solution across their commercial
organization. Whether it is for global financial analyses, for varying departments that need a comprehensive
view of the customer, or for operational efficiencies, most organizations will at some point want to move
beyond functional MDM solutions. This step can be part of a company’s natural evolution in MDM maturity
and need not require a complete overhaul of systems and procedures, provided that functional solutions are
built with this end goal in mind.


Michael Allelunas, General Manager of Information Management, is responsible for developing and delivering a wide range of
solutions to clients. Key disciplines within the Information Management practice include Master Data Management, Specialty
Data Integration, Data Warehousing and Information Management Strategy and Diagnostic services. Throughout his 19 years
of Life Sciences experience, Michael has remained focused on working directly with clients to solve their most critical needs.

Will Gurney, Senior Principal of Information Management, specializes in helping clients realize the potential of their MDM
programs. Will has over a decade of experience implementing and managing MDM solutions for Life Sciences and Healthcare
companies of all sizes and varying scope. From Strategy and Design, Implementations and Delivery, to Operations and
Governance Will has helped guide our clients to success.

Developing a Long-Term Master Data Management Strategy
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