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Get ‘IDMP Ready’ with Modern Data Management

There is no better time than now for pharma & medical device companies to modernize their product information management and comply with IDMP (Identification of Medicinal Products). Non-compliance might result not only in hefty penalties (as high as 5% of annual EU gross revenue) but also in poor operational efficiencies. Experts advise to kick-off the IDMP initiative now and reconfigure the data model later when the final guidelines are published by EMA (European Medicines Agency), FDA (Food & Drug Administration) or other similar regulatory body.

IDMP is a set of five ISO norms which has been developed in response to a world-wide demand for internationally harmonized specifications for medicinal products. Following a phased implementation process, pharma & medical device companies will be required to submit data on medicines and medical devices to EMA in accordance with these formats and terminologies. The implementation of the IDMP standards will help achieve operational savings for these companies as well as improve the health and safety of the human population.

Product information in pharma & medical device companies is distributed across several departments or lines of business in a myriad of different systems, authored in different formats, in multiple languages, and different terminologies. Harmonizing this data within a single organization itself is a big challenge, but doing so across the continents and coming up with common standards is a daunting task. It is for these reasons, the timelines for implementations of IDMP standards have been changed a few times. This valuable grace period should be utilized by these organizations in planning and preparing for this ambitious, enterprise-wide initiative.

As per the EMA, the underlying challenge of IDMP is fundamentally a Master Data one. EMA’s approach to implementing the ISO IDMP standards is based on the four domains of master data in pharmaceutical regulatory processes: substance, product, organization and referential (SPOR) data. Pharma & medical device companies that would be regulated as per the IDMP standards by the EMA, should be right now actively getting a handle around where is their product data scattered within their enterprise, and how they would manage it scientifically.

A Modern Data Management Platform allows you to create a strong underlying master data foundation for IDMP objects in the cloud as well as derive actionable insights from various data domains, their relationships, and the interactions among them by leveraging graph technology. It not only creates the reliable product data foundation but also offers flexible product hierarchies by markets, brands, segments and geographies that can be categorized, organized and analyzed from multiple perspectives.

It is extremely easy to write metadata based definitions of IDMP objects in an agile, real-time configurable data management platform. Not only can you start with the definitions of these objects as per the evolving IDMP standards, you can also extend these definitions over time based upon your varied business needs. You can create other objects over and above the IDMP objects, define relationships among themselves, and capture transactional data that will eventually provide valuable insights. Reference Data Management is yet another underlying capability of a Modern Data Management Platform that helps master reference data from multiple systems. In the world of IDMP, the reference data can be sourced from different systems. As an example, Global Substance Registration System (G-SRS) is one of the major source systems that implements and supports the ISO-11238 substance types and controlled vocabularies (CVs).

Last but not least, a cloud-based Modern Data Management Platform requires no on-premises installation, hardware or maintenance. Instead of buying servers, installing and patching software, and constantly wrestling with how to handle the relentless growth and diversity of data, your IT teams can focus on delivering relevant, operational intelligence to business users. Such platform is deployable in a fraction of the time and cost compared to the traditional MDM solutions, providing significantly faster time to value. Also, it provides fine-grained, attribute-level, visibility of who searched for, who looked at, and who modified what data, in logs that can be tracked and monitored for security and compliance.

Business leaders who can adopt a modern data management philosophy, program management teams that can help drive the project, and technology partners who can help implement specialty technologies, would need to come together to make full, organization-wide IDMP compliance a reality. Using a next generation data management platform for your IDMP implementation will not only reduce the time to compliance in a cost-effective manner, but it will empower your organization to create a futuristic data platform that will stay current. In addition, it will help you build new capabilities such as providing transparency to your consumers, facilitating acquisition of other products or companies, and identifying emerging product safety risks apart from meeting regulatory requirements and delivering cost savings.

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Why Life Sciences Must Go Beyond Master Data Management (MDM) …. Again … with Big Data Analytics

The annual CBI conference in Philadelphia will take place Nov 3-4 at the Wyndham. As in the previous 2 years the topics center around customer (HCP) master data. In fact, this year the conference has been renamed (yet again) from Commercial Data Congress (2013) to Master data Management (2014) to now Enterprise Customer Data Integration and Innovation (2015). It’s ironic because no one has really used the term Customer Data Integration (CDI) since 2005 when Gartner decided MDM was the better moniker.

For the first time Reltio will be participating at the CBI event and we look forward to shaking things up a bit with our participation in workshops, panels about open payments, and sponsorship of the overall event.

In addition to participating at CBI, the Reltio team will also be at the Innovation Enterprise Big Data Analytics in Pharma Summit across town on the same days Nov 3-4 at the Rittenhouse Hotel. That event will be all about the latest NoSQL databases, Hadoop and massive data sets, they won’t be focusing on master data, and that too will be a mistake. We believe that a MDM foundation is required for reliable data regardless of big data volumes, and it’s impossible to get relevant insights and recommended actions without it.

Why do we care so much about this topic?

Many of the team here at Reltio formed the nucleus of Siperian (acquired by Informatica in 2010), the leading on-premises MDM tool widely adopted by life sciences companies. Back in 2005, master data management  (MDM) was just taking shape and companies used MDM primarily to improve Siebel CRM data quality before upgrading and migrating their on-premises systems. Back then Siperian was preferred by many to the “seamlessly integrated Siebel Universal Customer Master (UCM)” offering, proving that best-of-breed solutions can be superior to integrated offerings that are designed for a single primary purpose.

One of the biggest issues we faced with Siperian (now Informatica MDM) was defining a relational life sciences data model that could capture not only the basic attributes of healthcare professionals and organizations, but represent real-world  HCP-to-HCP, HCO-to-HCO and HCP-HCO relationships. Also thrown in for good measure was an emerging need to master product data, product hierarchies, groups and baskets for pricing and competitive analysis, and to feed product information (PIM) systems.

At Siperian we admittedly struggled with basic hierarchy management and performance issues with merge and especially unmerge. While we preached multi-domain and coined the term Universal MDM, we were never completely successful with standalone product master data management, let alone bringing together both customer and product data into a single consolidated Siperian Hub.

Back then, the best databases we had to model and store life sciences entities and their relationships were the likes of Oracle, DB2 and SQL Server. Cloud and big data technologies such as graphs, columnar stores, HBase (on Hadoop) and Cassandra simply weren’t available.

Fast forward to the present, the MDM landscape remains more or less unchanged despite a quantum leap in technology.

  • Informatica MDM is still an on-premises solution with many of the same challenges we faced while at Siperian
  • Cloud-based customer master only offerings such as Veeva Network continue to focus on improving CRM data quality. Much like Siebel UCM did for Siebel CRM over 10 years ago
  • Customer and product masters are still supported through separate siloed hubs, even when built using the same tool. In fact, Gartner continues to publish separate customer and product magic quadrants as if to re-enforce this fact
  • Master data must still be delivered to data warehouses or operational data stores in order for business users to get a promised “complete view”
  • MDM systems and tools built on 1990s relational database technologies continue to hinder the ability to model real-world many-to-many-to-many relationships that graph technologies are designed for

For the most part, life sciences companies are no closer to getting basic affiliation management functionality, or their dream of an all encompassing key account management application as they are hindered by legacy MDM tools. Even a new wave of cloud-based MDM solutions do not make things any better. The good news is that life sciences companies can avoid a new kind of MDM (“Making Da-same Mistake”) … again.

The most popular consumer facing applications today such as LinkedIn and Facebook have shown that business facing data-driven applications can be cloud-based, handle multiple data domains, manage structured, unstructured, master, transactional, activity and social data. Companies should expect complete end-to-end modern data management delivered as Platform as a Service (PaaS) instead of relying on recurring “next generation master data management” promises that remain unfulfilled.

I’ll be attending both events because like everyone here at Reltio I’ve got the DNA and passion in me for both. I look forward to seeing you at either … or both events. In the meantime, here are some additional musings around the future of MDM. Please let me know your thoughts.

Accelerating Mergers & Acquisitions in the Medical Device Industry & the Rise of Data Monetization

As part of a very busy month for Reltio, I attended and presented at an event organized by Q1 Productions dedicated to Medical Device Industry Corporate Strategy and M&A in Atlanta, GA. The meeting was very well attended including representatives from Medtronic, Stryker and other companies sharing their insights into techniques for more effective M&A.

The topic of my presentation was:

Accelerating M&A in the Medical Device Industry with Modern Data Management

We discussed how bringing together clean, reliable, relevant data in a timely fashion from the IT systems of both parties to support an M&A is still largely a manual, inefficient and potentially inaccurate effort.

The audience agreed that the complexity of data siloed across both companies made it very difficult to analyze information within the legal and time constraints of all pending transactions. If the merger goes through, all work is generally discarded, leaving the combined company to start from ground zero. If the merger is called off, someone is left with hardware procured to support the M&A that is wasted. 

As part of the presentation I detailed:

  • How a multi-billion dollar merger blended & analyzed data from hundreds of sources with full security & audit controls, without spending a dime on hardware
  • Why they can also now realize millions of dollars in increased negotiating leverage through better vendor/supplier management
  • Which groups are positioned to benefit from new data-driven applications that will significantly improve the efficiency of their day-to-day operations
  • I also provided a modern data management Platform as a Service (PaaS) 101 overview, so attendees could understand the difference between MDM, Big Data, IoT, Analytics, Graph databases and Machine Learning
  • What other opportunities, including data monetization, are now possibilities for the future

Due to a cancellation I also was asked to dive deeper in a separate presentation on the topic of Data Monetization.

Data Monetization, Chief Data Officers, and Industry Clouds

This session provided details on: 

  • What exactly is data monetization
  • Why Data as a Service (DaaS) is a prerequisite
  • How reliable data with full compliance and audit controls are needed as a foundation
  • What are the legal ramifications?
  • Who is responsible for thinking about monetization?
  • What is a chief data officer, and what is his/her role?
  • How does a CDO play well with a CIO?
  • What are industry clouds, and how will that change the landscape of industry specific data applications
  • And even a topic what the future holds for our children’s future career choices: whether to learn how to program or be a data scientist

Finally the next day I moderated a lively “war stores” panel on

Lessons Learned from Recent M&A Activities

Distinguished panelists included

  • Girish Gangadharan, Analyst, Corporate Strategy & Development, ANALOGIC
  • Richard B. Smith, Partner, MCDERMOTT WILL & EMERY

We discussed how over the course of the past 18 months, the medical device industry has experienced some of the highest levels of mergers & acquisition activity recorded to date, and as a result, many executives and organizations have sharpened acquisition skill-sets and have also learned substantial lessons. A lively debate ensued with the panelists and audience around recent experiences, lessons, as well as challenges that lie ahead for the industry as it continues to evolve and grow. The discussion continued throughout the day as attendees identified best practices across the business development spectrum; from identifying appropriate targets to negotiating in order to secure the optimal outcome.

Other key points raised included:

  • Managing expectations for earn outs as part of the deal
  • The difference between acquiring pre-commercial and commercial companies
  • Bringing together sales teams, and managing cost synergies
  • Retaining founders of start-ups and incentives
  • Legal and contractual requirements

Medical Device M&A continues to be hot. A recent infographic by Medical Device Trends shows some of the latest statistics: 
(BTW their site is a wealth of information and you should follow them @mdtrends on twitter) 

Contact me if you’d like to chat about your experiences in life sciences and medical device M&A, or if you’d like a copy of my presentations.