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-premise 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