Keep Your Eye on the Enterprise: Developing a long-Term master Data management strategy
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WHITE PAPER 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 considerations. 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. FUNCTIONAL SOLUTIONS 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 StrategyDEVELOPING 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 StrategyDEVELOPING 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.
FIGURE 1: THE TOUCHPOINTS OF ENTERPRISE MDM
Customer Channels
Field Call Speaker E-Detail E-Sample Direct Convention Web
Force Center Programs Mail
Organizational Operational
A GOV
DAT ER
NA Campaign
Sales
Mgmt
NC
Incentive
E
Marketing Analytic Comp
Insights
Market Sales Segmentation
Data
Research & Targeting
Master
Data
Speaker
Finance
Programs
Customer
Interactions Travel &
Compliance
Expense
DA
Managed
TA A C C E S S Brand
Markets Planning
Supply
Transparency
Chain
Business Intelligence
Reports Aggregate KPIs Customer Campaign Customer Market Statistical
Spend Segments ROI Analysis Analysis Analysis
Developing a Long-Term Master Data Management StrategyDEVELOPING 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 StrategyDEVELOPING 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
Management
Sales
ERP Sales Comp Legacy
Reporting
• 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 StrategyDEVELOPING 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 StrategyDEVELOPING 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 StrategyDEVELOPING 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.
ABOUT THE AUTHORS
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 StrategyIMS Health
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ABOUT IMS HEALTH
IMS Health is a leading worldwide provider of information, technology, and services dedicated to making healthcare
perform better. With a global technology infrastructure and unique combination of real-world evidence, advanced
analytics and proprietary software platforms, IMS Health connects knowledge across all aspects of healthcare to help
clients improve patient outcomes and operate more efficiently. The company’s expert resources draw on data from
nearly 100,000 suppliers, and on insights from 39 billion healthcare transactions processed annually, to serve more than
5,000 healthcare clients globally. Customers include pharmaceutical, medical device and consumer health manufacturers
and distributors, providers, payers, government agencies, policymakers, researchers and the financial community.
Additional information is available at www.imshealth.com
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