Discover the power of reference data: A guide to understanding and managing your business' critical information
We take it for granted. When we call a friend in France, we automatically add the internationally recognised dialling code of 33. An entire orchestra is tuned to an A at 440 Hertz frequency. Even nature has its golden ratio for reference when designing the arrangement of petals in a flower.
There are many bits of reference information that are internationally recognised and in common usage — grocery barcodes, medical conditions, industry classifications and postcodes to name but a few. Some reference data can also be specific to just one sector, where it can support a unique business process or describe the configuration of an individual application.
What is reference data?
Reference data is generally not very volatile in nature but, despite this, any changes can have significant impacts on IT systems and business operations. Consider, for example, the widespread implications of a country or tax rate code changing.
Yet despite the repercussions of such a change, many organisations have only limited business controls or governance on reference data’s definition, usage and quality
What are typical reference data problems?
Because it rarely changes, issues with reference data governance often become apparent at times of transition, such as when integrating or consolidating systems. Problems also occur during the production of analytical reports, when reference data from multiple sources is in differing formats.
Issues can also occur when there’s a need to identify a person who has been using information for a purpose for which it was not designed. The wider point is that transition projects can be hindered by non-coherent descriptions of how data is being defined or who is using that data.
This confusion can also add extra costs to each project because once the conflicting reference data has been located, a potentially an onerous task in itself, the quality of the information must then be reviewed, often manually.
Challenges in managing reference data
Managing reference data can result in many challenges that can have a significant impact on your overall business operations and decision-making processes. Here are a few of the most common issues:
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Data quality issues
Inaccuracies or inconsistencies in your reference data can (and most likely will) have a major impact on the quality of your data. As a consequence, they result in errors in reporting, workflows and business processes. -
Data integration challenges
Integrating reference data from different sources can be complex, time consuming and expensive. That’s especially true when the data sources have varying formats and standards. -
Lack of governance
Without proper governance, managing changes and ensuring data privacy can be difficult, leading to unauthorized usage and compliance risks. -
Too many manual processes
Over reliance on spreadsheets and other time-consuming manual processes for managing reference data can result in significant inefficiencies and costly errors. -
Scalability
As the number of business users grows, managing and scaling internal and external reference data across multiple systems and locations becomes increasingly challenging.
In many cases, these challenges can be greatly diminished or even nullified by using reference data served by a master data management (MDM) suite.
Benefits of effective reference data management
Implementing an effective reference data management (RDM) program alongside your current business solutions can be incredibly beneficial for all stakeholders. Here’s how:
- Improved data quality: If you want reference data that’s accurate, consistent and high-quality, there’s no better option than RDM.
- Enhanced data integration: Since your RDM is an extension of your master data management program, it helps to facilitate seamless data integration from all types of data sources, improving overall data coherence and reliability.
- Centralized governance: It centralizes control over reference data (ex. mobile device management), further enhancing data privacy and regulatory compliance.
- Automation: It reduces the hours and capital spent on manual tasks by automating your data processes and increasing operational efficiency.
- Better decision making: It provides a single source of truth throughout the business, enabling more informed and accurate decision making.
In addition to being a singular version of the truth, streamlining your reference data sets makes collecting, securing and storing your data much easier and accessible.
A few examples of reference data include postal codes, state and country codes, currencies, customer data and much more.
Reference data requires governance
Reference data management solutions provide support to the implementation of governance best practices. They provide places to document the data’s purpose, control its definition and distribution, and describe its relationship with other types of reference data.
Consider a company’s commercial organisation. It might have regional offices along with assets and employees in numerous locations and with a variety of roles and functions.
A reference data management solution can define the types of business processes and reference the job roles required to perform each one. These roles are then able to have locations and employees assigned to them.
For a second example, let’s take a healthcare institution that needs to provide regulated product labelling. The reference data describing medication types and illnesses are already internationally recognised.
A reference data management solution would be able to load existing sources of trusted information and format/transcode it in a way that is suitable for all healthcare systems to interpret. In this way, data references are actively governed but the existing systems do not need to be changed to accommodate new standards.
Finally, we’ll look at a bank that is implementing a governance strategy for financial services reference data. Since the data comes in multiple forms across many business units, it will have disparate systems, processes and people (data stewards) managing it.
Reference data is required to support both the definition of master data (party, account, product, user, etc) and transactional data (trade order, service event, contact, etc). Regulatory compliance initiatives such as BCBS239 require the support of reference data to successfully aggregate risk data and support reporting requirements.
The importance of getting your reference data correct
The importance of managing reference data at an enterprise level is becoming increasingly important as 3rd platform initiatives tend to push information back into silos. Integrating those silos to support new business functions requires a deeper understanding of how the underlying reference is defined.
Perhaps if reference data management systems had been available to Mr. Shakespeare at that time, he might have changed his mind about what he understood a rose to be… or not to be.
Features to look for in a reference data management solution
Insights gained from business intelligence are only as good as the data those insights were derived from. As such, here are a few things to consider before deciding which platform you’ll trust with collecting, storing, and protecting your enterprise data:
- Centralized data catalog: Most data dependent businesses require a comprehensive data catalog that keeps their reference data and metadata in a centralized location. This supports both business intelligence initiatives and auditing.
- Automation capabilities: Automating data processes like data validation, enrichment, integration and others, can save a lot of time and money.
- Data quality tools: It should provide cutting-edge tools for data cleansing and data validation to ensure the highest quality data.
- Scalability: The ability to handle large volumes of data and scale with business growth.
- Governance framework: Robust governance features are a must. That includes allowing role-based permissions and workflows to help manage data authoring and record stewardship.
- Real-time integration: Whether you have an ERP, CRM or enterprise systems, you’ll need an RDM that supports real-time data integration.
- Compatibility: Compatibility with various data sources and formats, including ISO standards and external reference data sets.
Whether you’re in manufacturing, retail, banking, etc., implementing a robust MDM platform that includes reference data management tools can be a game changer.
Not only can it enhance internal operations, but it can also help to better understand customer needs.
EXECUTIVE BRIEF
How to Develop Clear Data Governance Policies and Processes
