+1.855.360.3282 Contact Us

Efficient & Compliant Business with Trusted Reference Data Management (RDM)

Reference data, a subset of master data (lower in volume, variety and volatility), is generally uniform, enterprise-wide and often created by external standardization bodies. Let’s say your customer’s address is a part of a master data record, then the Zip Code and State fields are reference data. It is the kind of data you will find in dropdowns and lookups–restricted values that you can choose from within a field on a form.

The value of reliable reference data cannot be undermined. Due to the nature of IT application development and the reliance upon off-the-shelf application systems, reference data is all too often isolated in silos within many different systems. Inconsistent reference data across multiple systems can cause invalid transactions (state and zip code mismatches), revenue leakages (bad discount codes) and compliance risks (improper tax codes).

As a part of transactional records, reference data is grouped with associated master data and transactional data, and is needed for both operational and analytical master data management enterprise use cases to provide attributes, hierarchies and key performance indicators. Traditional mapping requires human judgment as well as manual synchronization and remediation of reference data. This is neither efficient nor reliable.

To ensure accurate reporting and analytics, proper governance and operational efficiencies, enterprises require a standardization system that makes it easier to define, map, manage and remediate reference data across the organization.

Reference data management is an integral part of Modern Data Management and needs to be a part of your data management strategy. Thinking about reference data in isolation or as an afterthought leads to expensive rework and compliance risks. Since Modern Data Management includes graph technology to establish relations across people, products and places, interesting capabilities result when combined with reference data. With the graph, reference data become pivoting attributes. For example, let’s say physician’s specialty is the reference data, as it needs to map to multiple systems and different physicians. Now speciality_code can be a pivoting attribute, which enables you to drill into a specialty to see the physicians across the organization, and other information relevant to the specialty. The graph makes such relationship management simple.


Today’s data management solutions need a user-friendly solution to define and manage reference data across multiple functional areas, industries and data domains. Whether customer, product or supplier data, Reltio Cloud RDM is a simple, business user-driven application that is adaptable to business needs across any use case required to preserve values and mappings between reference data sets–both in a domain and across domains.

Unlike other legacy MDM tools, that charge separately for basic RDM capabilities, Reltio Cloud Modern Data Management Platform as a Service includes core RDM functionality built-in. Being built-in makes it much simpler to ensure that there is consistent reference data for all downstream operational applications. By managing complex mappings among customer, partner, product and supplier data domains, and managing their interrelationships, enterprises will improve data quality and reduce compliance risk.



Governance of reference data is vital–manual or custom RDM often lacks change management, audit controls and granular security and permissions. Due to the complexity in managing and governing reference data, an RDM solution should include a seamless, intuitive user interface to manage lookups, and ensure data consistency across systems with version control, security and access controls. Reltio Cloud’s RDM facilitates remediation and improvement of reference data quality along with mapping to localized data, which helps with global harmonization. Built-in workflow capabilities, such reviews, approvals, history and audit trails help make structural changes to reference data with complete governance.


RDM data are often managed by business users who want to maintain, manage, standardize and remediate reference data at their fingertips. They need complete visibility into the “Crosswalks” for understanding data change impact. Collaborative curation of information through fine-grained workflow and governance allows cross-functional teams get the most accurate information real-time. Teams should be able to flexibly deliver information to downstream applications or provide access through embedded widgets within operational applications.


In many cases, organizations purchase or subscribe to third-party data sources for verified reference data. Lines of business want an easy way to connect to third-party reference data sources to enrich the existing data. Data as a Service within a modern data management platform lets you connect to such data sources, and merge the data with other master data for your data-driven applications.


A Modern Data Management platform lets you connect to existing MDM, operational applications and third-party data sources for real-time integration. User-friendly interfaces with import and export capability help map reference data sources quickly, and eliminate the burden of managing reference data sets. Reference data from multiple source systems require no transcoding, translation, custom code or IT involvement. Configuration, Lookup and Transcode REST APIs are available to manage reference data through integrations. A multi-tenant cloud platform ensures ease of provisioning and zero downtime upgrades. Deployed in the cloud, you will be delivering value faster than ever possible without the overhead of managing the infrastructure for this highly critical and available data.

Mona Rakibe is a Director of Platform Product Management at Reltio. She’s an expert in data management technologies with a specialty in content management and BPM, having worked for companies such as EMC, Oracle and BEA Systems.

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.

If you enjoyed this post, please feel free to share the short video below

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

An Industry Shift for Master Data Management at Summit NYC

If you missed it, the premier industry event for master data management (MDM) in NYC took place this week, with attendees from all over the globe. The event was well attended but for many attendees, the information they were seeing was anything but business as usual.

In among the traditional sessions on master data management fundamentals, case studies and master reference data, there was a decidedly different tone to the event. 

Interest was high on graph databases, end-business user trust and access to the information through data-driven applications, and how modern data management platforms were now providing master data management as a core foundation, upon which big data, transactions, analytics and machine learning are fully integrated.

Here are some highlights:

The “Godfather of MDM” Aaron Zornes kicked things off, describing the new strategies and elements at this year’s summit. He highlighted graph databases, temporal and big data particularly as trends.



Neil Cowburn and Robert Quinn of iMiDiA delivered a lunchtime keynote describing how a modern data management platform was used for multi-billion dollar mergers and acquisitions (M&A)

He described how IT teams can now cope with data across hundreds of sources (e.g. over 96 SAP instances!) and increasing volume, variety and velocity, while managing veracity. Modern Data Management techniques were applied in an M&A use case where data collection, tracking of changes & control access were accelerated at minimal cost. And how all capabilities used for pre-merger analysis were available for post-merger competitive advantage. Attendees learned how to:

  • Leverage & enhance Master Data scope – entity overlap & cross-entity relationships
  • Support enrichment to enable the combined business teams to get more value out of the master data
  • Syndicate master data to existing operational applications & analytic platforms – all while supporting a single point of governance
  • Follow a methodology to work with the people and processes needed to co-exist with existing legacy MDM tools, while making the move to a modern data management platform

Vivian Wu of AbbVie presented as part of the Reltio sponsored virtual pharma track. She described how the limitations of a legacy and traditional MDM tool led them to select a modern data management platform. 

AbbVie used an incremental approach to deliver relevant insights while creating reliable data that could be shared company wide. Their modern data management included master data, transaction, social and third-party data delivered through mobile and desktop data-driven business applications that improve the productivity of their frontline business users every day.

Attendees learned how:

  • A cloud-based modern data management platform was selected & how it co-exists with existing systems/applications
  • Data quality can be continuously approved, while seamlessly segmenting & analyzing information
  • To plan to start small & expand to limitless possibilities company-wide 

In a particularly poignant slide she described how companies should insist that their third party data providers provide on demand subscription-based data-as-a-service functionality. She likened the current batch list method of acquiring and loading data as extremely dated and inefficient. 

Data providers used to ask us for a sample list of our data, upon which they would then deliver to us the list back of records either augmented with additional info, or some newer records that meet the same criteria.
If you think about it that’s like me walking into a clothing department store, and the salesperson asking me to show him/her my wardrobe and then telling me that these are the clothes that they have to offer me.
I want the full browse, preview experience and the freedom to see and try all the clothes in the store. I want that same experience with data for my business users.

Michelle Goetz, principal analyst at Forrester delivered a keynote that emphasized that business users need to trust their data. And the best way to provide that trust is to give them access to the data, as quickly as possible. And to accept their feedback and input in a collaborative manner. 

Unlike retroactive reports and analytics, data-driven operational applications are one of the best ways to provide this capability, in conjunction with their daily business activities.

Darius Kemeklis of Google presented a session on graph databases and MDM. 

He described how graph databases offer tremendous potential to model and analyze complex relationships inherent in the real world. He pointed out that many enterprises are increasingly considering graphs as a pre-requisite for their MDM strategy.  He provided a foundational tutorial answering questions such as “Is Graph a Concept or Storage Technology?”

Attendees also learned:

  • From successful industry–wide & industry-specific examples of Graph use cases – Darius showed Reltio’s commercial graph as a prime example
  • Where Graph & MDM intersect
  • Key concepts such as building and visualizing graph schemas 

Overall the event represented an industry shift in traditional MDM philosophy. Attendees were definitely evaluating and considering modern data management techniques, and looking to collaborate with business teams to gain their trust, and get their input into continuous data quality through data-driven applications.

From my perspective it was great to old friends and make new ones. We had fun giving away Big Data Lake Crocs and talking data. See you at the next event!