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.
A MODERN MULTI-DOMAIN APPROACH TO REFERENCE DATA
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. Being a part of a Modern Data Management Platform as a Service, RDM in Reltio Cloud 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.
ENSURE REFERENCE DATA GOVERNANCE
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.
EASY TO MONITOR AND MAINTAIN
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.
ONBOARD THIRD-PARTY REFERENCE DATA
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.
GET STARTED QUICKLY AND EVOLVE WITH BUSINESS
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.