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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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Challenges in Leveraging Big Data in Retail

Ankur Gupta, Sr. Product Marketing Manager, Reltio

1. Consolidate and cleanse data from various sources:

Retailers want to bring data together from multiple internal, third party subscriptions, public, and social sources to create a complete and accurate view of their customers. They want to uncover relationships, not just between consumers and products, but locations and family members as well to solve the householding issues. They want a single source of truth of customer data across functional areas and a reliable data foundation for accurate customer segmentation and identification of the influencers.

2. Gain relevant insights from omnichannel data:

There were several discussions around retailers wanting to blend interaction data from various channels with consumer profile information, giving marketing, e-commerce, and customer support teams visibility into customer preferences, product interests, and channel choice. Retailers want to deliver insights like churn propensity, lifetime-value, and abandonment rates to relevant teams in the context of their role and objectives. Furthermore, many leading retailers are leveraging machine learning and predictive analytics to suggest next-best-actions to send relevant and consistent information, across all channels, to the customer and find opportunities for up-sell and cross-sell. However, there is still a concern about the reliability and completeness of the data utilized to run such analytics.

3. Create a global product master:

Several retailers want to create a complete product or SKU profile to understand the supply chain relations, contract adherence, consumption per location, overall global business value and even social sentiments about their brands. They want a worldwide real-time view of the product, especially during a launch, to gain critical insights into accurate targeting and managing key influencers in the marketplace, designing relevant promotions and devising social media strategy.

4. Break data silos across departments:

Retailers are looking for ways to encourage collaboration across teams, in real time. With global multi-functional teams, multi-product portfolio, and big data scale consumer information, it is critical to allow as well as secure access to a convergence of information, with the proper level of role-based access and visibility. Data management has to be a shared responsibility across all functional groups and tools for social curation of internal data in the form of annotating, workflows, tagging, and voting allow every member to contribute and continuously improve data quality and the enterprise knowledge.

5. Exchange data with external parties:

There were some interesting discussions about the possibility to share the data externally with the suppliers to establish a single holistic view of the supply chain. Historically, most retailers do not have the infrastructure to process and make transaction-level data accessible easily. Fortunately, this technology is now available as Data as a Service (DaaS). Retailers can efficiently carve out a data view in the cloud and share it with partners or even monetize their data to create new revenue streams. The advantages of retail data sharing include improving on-shelf availability, better demand forecast accuracy, and improving the customer experience, among many others.

6. Be compliant:

With so many teams working with consumer data, retailers need comprehensive auditing and tracking features to guarantee compliance. They want a historical trail for any data merged or updated and want to get alerted to abnormal data viewing patterns by application users for possible information breach or theft. Compliance and transparency need to be inbuilt into the data management rather than treated as reports developed as an afterthought.

According to a McKinsey study, the continued adoption and development of big data levers have the potential to increase US retail productivity by more than 0.5 percent a year through 2020. Such a boost in profitability is especially significant in a sector where margins are notoriously tight.

Are you ready to address the above pain points and turn your big data into a valuable asset? Answer these seven questions to learn how prepared you are to manage your retail data effectively.

Flexible Hierarchies for Product Brand Management in Life Sciences

Accurate, timely and reliable product information is important for a variety of business initiatives such as detailed market, sales and competitive analysis, leading to better planning for faster product launches and improved supply chain efficiency.

Much like customer information, product data can now come from a variety of channels, with multi-dimensional and multi-format characteristics. To truly benefit, companies must not only manage data, but make sense of all this “data noise” coming from traditional software silos inside the firewall of an enterprise, third-party data providers, as well as an increasing number of cloud applications.

Traditional master data management (MDM) solutions that only synchronize downstream to applications are becoming increasingly inadequate in a dynamic real-time environment where business users want actionable insights, and not just more data.

Challenge: Not just accurate product data, relevant, actionable product information

Product MDM encompasses the global identification, linking and synchronization of product data across heterogeneous data sources. This involves creating a single product view for different teams to support their business activities. However, Product MDM for life sciences requires not just accurate product data, but definition of flexible product hierarchies by markets, brands, segments and geographies that can be categorized, organized and analyzed from multiple perspectives. Then correlated and included in context with applications that refer to the use of those products.

For example, external product information might include sources such as First Data Bank (FDB) and Symphony Health sales data.  Identifiers such as the National Drug Code (NDC11) can be used to accurately match, merge, cross-reference product records, and their hierarchies, with grouping of products at a granular level accommodating different package forms, strengths and delivery methods of the drug.

Traditionally, each team needing product information ends up hand picking an appropriate set of NDC11 level products for analysis. With each analysis by different users, there could be significant duplication of effort redefining and grouping the same sets of products over and over again. Comparing groupings can also be a quite a challenge.

This basic scenario highlights how traditional MDM with one-sized fits all view does not completely solve actual business use-cases for each commercial team and individual users. Teams end up having to either perform their own data management and transformation within applications, or develop custom processes to truly understand the data. Furthermore, sales and marketing teams that want accurate product information for planning and campaigns also must correlate products to customers, which in traditional MDM solutions are siloed within separate customer and product hubs.

For life sciences, MDM of product data should form a part of an overall data management strategy that encompasses the management of multiple master data domains and entity types. It is therefore important to leverage a solution that is not only multi-domain capable out-of-the-box, but provides a built-in architecture to relate and connect all domains seamlessly.

Next Generation Platform as a Service (PaaS) with Built-in MDM

Companies need a modern data management platform and data-driven applications that require 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, IT teams can focus on delivering relevant, operational intelligence to business users. These platforms are deployable in a fraction of the time and cost compared to traditional MDM solutions, providing significantly faster time to value.

They blend data from internal, and external systems with built-in Data as a Service, storing information in a Commercial Graph. The graph is a single repository for not just all your product master reference data but also transaction and interaction data related to products, such as product sales data, and even product intelligence from third-party data sources.

By ingesting structured and unstructured data within an enterprise, from internal systems such as SAP, Salesforce, Excel, Word, PDF, and third party data sources you purchase or subscribe to. they provide the required entity resolution and product standardization capabilities, along with flexible hierarchy management, to manage complex product groups. In the same data store, you can also combine social web data feeds including Facebook, LinkedIn, Twitter and blogs that can help uncover competitive product intelligence and customer behaviors.  

Product brand, sales and marketing teams can gain access to product information when it is exported and synchronized with downstream systems. They can also manage and access information through data-driven applications presenting relevant information and views specifically targeted to their business needs. Intuitive dashboards with easy visual navigation, provides customized analysis across any set of dimensions of the data. It can also be set up to detect new or updated data from sources, notifying various stakeholders of its arrival, proactively uncovering information. This is in contrast to a traditional SaaS application that merely provides tools for users, putting the onus entirely on them to self-discover events in a growing sea of data.

Today companies of any size to cost-effectively deliver accurate, reliable product data to business teams for brand management, competitive intelligence, market basket analysis, segmentation and more using any attribute. Flexible hierarchy management and modeling can combine non-product entities such as customers, locations, transactions and interactions supporting an unlimited combination of needs across all business teams.

Characteristics of such solutions include:

  • Managing not just Product Master Data but other entities such as customer, accounts, policies, etc.

  • Multi-domain data standardization and entity resolution

  • Integration and consolidation of data of varying complexity (including hierarchies and network graphs) from multiple silos

  • Extending and expanding the reach to include unstructured and social media content, and blending it with Enterprise data

  • Providing an operational data-driven application that supports collaboration across teams

  • Delivering relevant insights and recommended actions using machine learning from the massive amounts of data generated