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Data Rx: Your Prescribed Solution to Achieve Strategic Objectives in Emerging Pharma

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Discover how purposebuilt data capabilities can help commercial operations teams in emerging pharma quickly access accurate insights to reach their strategic objectives.

Data. Its value as a strategic asset to the enterprise cannot be stressed enough. Yet, many pharmaceutical organizations suffer from problems with their data management systems due to the challenges posed by fragmented data sources in healthcare and the overall lack of data transparency and structure. Silos within the organization and the motivational differences between IT and business teams exacerbate the problem, resulting in business teams frequently lacking the quality data they need to make decisions. KMK reached out to both business users and IT users within pharmaceutical and early-stage biotech companies to identify their key perceptions and satisfaction levels on the various aspects of data management within their respective organizations. Our research identifies the common issues that need to be addressed to achieve effective data management that would enable emerging pharma companies to reach their strategic objectives.

The Ubiquity of Data: How Business Users and IT Users Look to Harness its Power

There is a recognized need for effective data management due to its critical role in delivering actionable insights. Yet, despite this recognition,

75% of business enterprises do not have a solution in place to manage end-to-end data activities1

80% of their effort is spent on the collection and preparation of meaningful data

20% on analytics2

The time spent and the dollars wasted take a decided toll on an organization’s effectiveness and bottom line. The emerging pharmaceutical and biotechnology organizations desiring to hit the ground running with their product launches are hit especially hard. To better understand the issues within these organizations, we spoke to business users and IT users working primarily within these companies. In both user groups surveyed, 84% had between two to five products on the market, and 16% of business users surveyed had commercialization underway, but had no marketed products to-date, and 14% of the IT users surveyed had one product on the market.

There were some telling differences between the two groups on how they perceived their current data systems and what they required:

  •  81% of IT users think their current master data management (MDM) system is satisfactory where only 46% of business users were satisfied with their current system.
  •  The top priority for IT users is “Organizing and Storing Data” followed by “Performance and Scalability while the top data priority for business users is “Quality and Accuracy” closely followed by “Operational Efficiency”.
  • 84% of IT users reacted very strongly to “Inaccuracies in the data are not systematically recognized and addressed” while only 32% of business users felt this was true.
  •  IT Users reported “Data preparation by business users” as having the most importance while business users attributed high importance in their data system to “Self-service business intelligence and analytics to business owners” followed closely by “Master data/ quality management”.
  • IT users were more satisfied with their existing data management and analytics platform, with 45% looking to change within the next six months, and 42% looking to change within the next six to twelve months. Business users indicated a more urgent need for change with 29% looking to do so as soon as possible and another 35% within the next six months.

The agreements between the two groups are also telling:

They both agree that their Data Warehousing, Analytical Data Mart and Business Intelligence tools need improvement, however, business users are quantitatively more unhappy

  • They both would like to have better data integration.
  •  Neither group seemed very concerned about transparency of the data management process or compliance.
  •  61% of IT users and 45% of Business users were concerned about the time lag between data updates, analytics and reporting.

The Common Challenges Posed by Data Management Platforms

What this research underscores is the need for a data management platform and analytical tools to satisfy and integrate the needs of both IT professionals and business users within an organization. The vagaries of the various groups’ requirements and expectations are enormous, and yet, their common goal to deliver and use quality data unites them in the need to have a data system provide the insights needed to drive strategic commercialization decisions, and do so in a
timely manner.

In order to have actionable insights, business users are looking for data consistency and integrity, insights into marketing and management, security and data mining, storage, stability, performance, and simplicity of access with a user-friendly interface. In emerging pharma and biotech, this can be a tall order as many of these organizations do not have the IT strength needed to deliver on all these requirements. Most have rudimentary departments used to support implementation of their back office systems. Data is often fragmented or inaccurate there is often no one responsible for cleansing and oversight. Many times, the siloed infrastructure initially set up within each separate business unit, results in each one of them having their own environment and process for analytics. In so doing they frequently end up using different versions of the data yet attempting to get a unified outcome from it. The end result leads to even higher confusion and dissatisfaction. For the most part, the IT departments in these

Many emerging biotech and pharma organizations are dealing with a homegrown data management solution originally concocted to handle simple situations and never intended for increase in volume and complexity that it is now expected to deal with.

organizations tend to focus only on getting the data assembled and stored in some enterprise system, at best a singular data warehouse. Working with this data is a different remit, and can become a nightmare for data analysts trying to figure out the story being told by the data. The rules of how the data gets loaded, what data gets discarded, and how conflicts are resolved are buried in layers of code accumulated over the years. The patchwork of implementation-specific code doesn’t cleanly translate into a clear set of business rules. Neither Business nor IT have certainty that the rules are consistent and accurate. Such legacy systems are very inflexible, with hard-coded assumptions and business rules, and the implementation lacks the agility and flexibility to change as the business changes.

A robust data management platform should establish a single point of reference for data integrated from different sources and varied business transformations, however, integrating dozens of data sources with differing and inconsistent formats can be a herculean task.

Common integration issues result from:

  • Lack of transparency on data processing or business rules.
  •  Under or over-merges within the HCP/HCO entities causing data churn and possible gaps between cycle call plans resulting from misalignment with the latest data.
  •  Inadequate address standardization causing address duplicates which in turn impact account call, sample drop capture, and other account related activities.

In addition, slow, inefficient coordination of the “DCR” (Data Change Request) process causes data drops, ultimately impacting the decision-making of CRM users and analytical users. The lack of a common published layer to present “one view” of mastered data causes data refresh delays and requires ad hoc end-user support.

The data platform can be misaligned for sizing , leading to incorrect processing and most often it is missing an embedded analytical component to generate insights. The model itself can be wrong leading to incorrect conclusions being drawn. Couple this with bad data coming from source vendors and data quality issues arising due to varied operational processes, one can see how a significant investment of time goes towards identifying and resolving data issues on a continuous basis. No wonder that operational data feeds are delayed and business users are left wanting. 

Faced with these issues, pharma and biotech organizations encounter difficult choices on the way to achieving the data management required to deliver quality strategic insights:

  • The lack of internal infrastructure and technology expertise.
  • The need to maximize the impact of their investment while maintaining a positive ROI.
  •  The fear of ending up with another “black-box” system with its own set of quality issues delivered by either the in-house or hired IT.
  •  The multitude of COTS solutions and the never ending effort to integrate them together.
  •  The actual or perceived vendor “lock-in” and wellsubstantiated concerns of multi-million dollar investment required to switch to a different vendor.

Clearly, the accuracy and integrity of data management, and the business insights generated from this data, are worth the effort to conquer today’s informational silos. Ultimately, one needs to properly design and implement a robust and modular data management platform to successfully navigate these issues.

Designing a Purpose Built Solution to Data Management

Solving the issues related to achieving quality insights with limited staffing and budget requires thinking outside the box, and more than likely working with outside vendors to arrive at a solution. Each pharma organization needs to understand its unique requirements and be prepared to handle customization. Yet, for the most part, there are general issues that all pharma organizations need to resolve when designing a purpose-built solution for its data management.

First and foremost, is understanding of the implications of not owning the data infrastructure. Often COTS data management solutions become outdated or inadequate for a growing enterprise, yet in moving to a new solution, the data gathered and the encapsulated business logic is often lost or a full fidelity migration is not possible. For some organizations, this risk is too great. Ideally, they would be wise to work with a technology stack that handles the data and analytics while allowing them to own their infrastructure and host with their desired cloud provider. The solution should be scalable from a computing power perspective and have versatile and robust functional integration capabilities.

Secondly, one should work with a single-source solution provider for the core platform housing and organizing the data. This will avoid duplication and desynchronization, reduce conflicts, and minimize the chance of losing data through data transfers. A single solution would also minimize time to implement as well as reduce investment costs and operational maintenance. A multi-domain system able to accept data from any source, will become increasingly important as the enterprise grows.

Ideally, the solution should address key business use cases out of the box with little to no customization. In doing so, the solution’s processing logic and business rules should be transparent to the user.

Specifically for emerging pharma use cases, one should look to have it encompass the following capabilities at a level appropriate to the organization:

  •  Data Warehouse – A central repository of all data emanating from internal or third parties that is integrated and scrubbed according to preestablished business rules.
  • MDM – Providing a single view on all attributes of HCPs, institutional accounts, SCOs, payers, patients, and their affiliations
  • Data Stewardship– Assuring the quality of data and managing data conflict resolution
  • Analytics and Reporting – Business Intelligence (BI) components with self service capabilities answering to business user needs for the right insights to drive strategic decisions.

Business users should be able to find answers to their questions and review analytics without any technical heavy lifting.

All users of the system should be aware of the business rules applied to the data, easily visualizing the data lineage from ingestion to output. Ideally, business rules can be adjusted and data scientists should have the capability to build their own data applications and model libraries to keep up changing business requirements.

In today’s rapidly evolving technology landscape, one should also look for a solution with pre-built AI and machine learning capabilities to enhance data quality by discovering potential issues, as well as predictive analytics and trend analysis.

Finally, one should look to partner with a solution provider who can leverage its experience and best practices in the field to not only implement a highquality solution quickly, but be capable of providing the ongoing support and business consulting needed as the enterprise grows

CASE STUDY: Supporting an Emerging Pharma Company’s Product Launch With an Integrated, End-toend Data Management Platform

The Challenge:

An emerging pharmaceutical company had acquired a new product to launch and was faced with the dilemma of having non-integrated data from disparate sources such as SP’s, IQVIA, sales activities, claims, affiliations and DCR. In addition, their current system lacked integration with 3rd party systems such as CRMs, MedPro and Concur.

Although data management is designed to treat enterprise information as a strategic asset, and provide the end-to-end business oversight to pave the way for strategic and operational decisions , the lack of transparency and data challenges resulting from fragmented data sources was dramatically hindering the client’s efforts to successfully use the data.

Recognizing the need to overcome its data siloes to rely on a single-source data strategy to support its launch efforts, the client turned to KMK to provide the requisite data management within the data warehouse, integrate and manage the data sources, as well as provide business insights based on a single data reference integrated from multiple sources and varied business transformations.

KMK Vortex is an integrated data management platform covering data collection, data mastering, data quality, data cataloging and self-serve analytics.

The Solution:

KMK turned to its turn-key cloud-based data management platform, KMK Vortex, to create a data solution to drive and deliver business insights for the client.

Working with the business team and other downstream systems, KMK first set up a data management warehouse that was built to ingest all the data from disparate sources. KMK then implemented a scalable, flexible Master Data Management (MDM) module with a configurable and transparent data quality subsystem responsible for scrubbing the data then integrating it into one longitudinal data exchange with the CRM. An analytical data mart was established in a preferred format for power users to enable adhoc analytics and provide timely insights to the business users such as leadership and sales team. Transparency in data operations was achieved with users able to view data processing in real time and receive notifications on data changes.

To further enrich the data and subsequent analysis, KMK enabled address standardization capabilities and designed processes to support specific business rules such as specialty determination, best address and TDDD Ohio relationship. KMK Vortex’s pre-built and self-serve reporting and analytics capabilities supported the business and analytical needs of multiple cross-functional commercial stakeholders and were accessible from any platform or device. The client’s data was no longer being simply stored but was set up to be used for insights to drive the business.

After the successful launch, KMK expanded the services scope on providing ongoing data operations, analytical support, and strategical consulting services post-launch.

The Result:

The successful deployment of the KMK Vortex data management platform and CRM integration improved the client’s overall data quality and data management capabilities and empowered the analytical team to generate high quality and timely insights, eventually led to improvements in the overall sales effectiveness and business performance.

About the Authors


Vakhtang Agayan

CTO at KMK Consulting, Inc.

Vakhtang is a transformative leader with 20+ years of progressive experience in the Information Technology field. He is an expert in systems and platform architecture, cloud services, product development, and technology excellence. He is highly experienced in driving process transformation initiatives, directing largescale implementations, and delivering high-performance solutions.

Kun Liu

Associate Principal of Commercial Operation & Analytics at KMK Consulting, Inc.

9+ years of experience in Life Science specializes in data strategy, technology, and analytics. Kun is leading the commercial operation & analytical team in NJ, has implemented many of data & analytical solutions for varies of life science companies and bringing in the latest technology such as AI/ML to enhance analytical services.

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