Data management is one of the most evolving tech disciplines of today. As new technologies emerge, data users become more equipped to break down data access barriers and reduce inefficiencies within their data systems.
One of the many big data innovations increasing in adoption today is reference data management (RDM). It has become a go-to for companies seeking efficiency gains with their data efforts. Here’s a complete guide to reference data management for beginners.
Reference Data Defined
Many businesses use reference data in their daily operations to optimize data use and availability. Reference data involves any company’s data used to categorize other data in its database. Sometimes referred to as metadata, reference data doesn’t usually change over time. This is because they serve as the basis for identifying other data. Country and postal codes, currencies, etc., are all static identifiers and can thus be great examples of reference data.
Reference data management uses reference data to create and manage data classifications. Businesses can leverage reference data management solutions to create hierarchies within a company’s dataset from multiple data sources. A company’s reference data can either be internal or external. Internal reference data comprises customer data, product inventory, and all other data types that require no external authority.
On the other hand, external reference data include SIC codes, NACE codes, and other data types created and maintained by an authority outside of a business’s data enterprise. Efficient reference data management combines these two reference data types in ensuring data systems attain optimum value.
How it Works
Reference data management processes begin with the consolidation stage. Here, businesses integrate all reference data into a central repository. The result is a golden copy that can make combing an organization’s enterprise more convenient. This consolidation stage involves enumerating and categorizing reference data into a comprehensive list.
The next stage enlists data stewards and analysts to manage the generated golden copy on an ongoing basis. The goal of the second stage is to ensure data quality before clearing data results for consumption. Here, data professionals build codes to detect and reconcile duplicates. They also determine and map out relationships between data sets, making it easier for hyperlinking during the data analysis.
The final stage is distribution. This is where data professionals build mechanisms for convenient access and distribution of reference data across an organization’s enterprise.
Benefits of RDM
Some of the benefits of reference data management include:
It contributes to business agility.
One of the main attributes of reference data is data accuracy. Accurate reference data can define current business processes and generate forecasts that help businesses make better future decisions. As businesses can easily generate accurate business processes data, they can tailor these processes during economic downturns.
Business agility affords businesses the freedom to address new challenges as they arise. Now more than ever, agility has become essential for businesses due to the impact of the pandemic. It behooves companies with efficient reference data management solutions to detect opportunities in real-time and leverage them for a competitive edge.
It helps build resilience in dealing with risks.
Risk-taking is an indispensable element in doing business. There are many unknowns in the business world. The only way to make it big in such an unknown world is to occupy it and learn as fast as you can. Taking and managing risks as a business is a hard task. This task can be a little less hard with reference data tools and resources. When companies take risky endeavors, they can use reference data to quickly scan through large volumes of data and determine practical solutions for survival.
It helps to reduce operational overhead and ensure data quality.
Reference data management tools can help IT teams reduce overhead costs. With these solutions, businesses can automate data access and consumption, improving productivity in the long haul. And compared to manually dealing with different sources and formats, this is a more cost-effective and convenient approach.