7 Challenges Solved by Reference Data Management

1. Lack of transparency regarding reference data ownership

One of the most significant challenges organizations face is knowing who is responsible for maintaining lists of values and hierarchies. Reference data management solutions provide clear ownership and accountability for reference data, ensuring that it is maintained and updated efficiently.

Imagine a scenario where there are multiple departments within an organization, each responsible for maintaining its own sets of reference data. However, there is no clarity on who owns which reference data, leading to duplication of effort and inconsistencies across systems. With reference data management, ownership can be clearly defined and assigned, ensuring data accuracy and reducing the risk of errors.

In the healthcare industry, compliance with regulatory standards is crucial for patient safety and data protection. For instance, HIPAA (Health Insurance Portability and Accountability Act) requires healthcare organizations to maintain the privacy and security of patients' protected health information (PHI). In order to comply with these regulations, healthcare organizations must ensure that all reference data related to PHI is accurately maintained and updated and that clear ownership and accountability are established for the maintenance of such data. With reference data management, organizations can easily assign ownership of reference data related to PHI, ensuring compliance with HIPAA and other regulatory requirements.

2. Unclear master system for certain reference data

Another challenge that organizations face is having an unclear master system for certain reference data. Reference data management solutions offer a clear master system for all reference data, eliminating any confusion or discrepancies.

Suppose a company has multiple marketing systems, such as email marketing software, social media management platforms, and ad-tracking tools. Each of these systems may have its own set of campaign tags or labels used to categorize marketing campaigns. However, there may be inconsistencies in how these tags are used across systems, leading to confusion and inaccuracies in marketing reporting.

By implementing a reference data management solution for marketing campaign tags, the company can establish a clear master system for these tags and ensure that they are consistently applied across all marketing systems. This would enable accurate and reliable marketing reporting, which is essential for making data-driven decisions about marketing strategy and budget allocation.

3. High maintenance costs

Maintaining reference data across multiple systems can be a costly and time-consuming process. A reference data management solution consolidates all data into one system, reducing costs associated with maintaining reference data in multiple systems.

Without reference data management, maintaining reference data across multiple systems can be expensive and a waste of time. For example, an organization might have to reconcile different versions of the same reference data set across multiple systems, leading to additional effort and cost. Reference data management streamlines the process, reducing maintenance costs and improving efficiency.

In the finance sector, a bank might have multiple systems for managing customer data, such as account information, transaction history, and loan details. Without a reference data management solution, the bank may need to reconcile different versions of the same reference data set across multiple systems, leading to additional effort and cost.

4. Complaints regarding the quality of reference data

The lack of validation features in systems where reference data is maintained can lead to poor data quality. Reference data management solutions provide a focus on data quality and clear lookup tables for reporting purposes.

In the absence of validation features, it can be challenging to maintain high-quality reference data. For example, if reference data is manually entered into a system, it can be prone to errors and inconsistencies. Reference data management provides validation features that ensure data accuracy, thereby improving the quality of reference data.

Take, for example, a retail company that maintains product information across multiple systems, such as an e-commerce platform and a point-of-sale (POS) system. Without validation features, manual data entry can lead to errors and inconsistencies in product descriptions, pricing, and availability.

5. Reactive approach to the impact of reference data changes

When reference data content changes, it can be difficult to identify and address the impact on other reference data entities. Reference data management solutions enable easy identification and addressing of the impact of changing reference data content on other reference data entities.

When a change is made to a reference data set in one system, it has a cascading effect on other reference data sets in other systems. In the absence of reference data management, it can be challenging to identify and address the impact of such changes. Reference data management provides tools to easily identify the impact of changes and make the necessary adjustments, ensuring data accuracy and consistency.

In logistics, you could have a shipping company that maintains customer addresses and shipping zones across multiple systems. If a change is made to a reference data set in one system, such as a new postal code, it can have a cascading effect on other reference data sets in other systems. Without a reference data management solution, it would be hard to identify and address the impact of such modifications.

6. Slow time to market to produce consolidated reports

Producing consolidated reports can be time-consuming when mapping and reconciling decentralized lists of values. Reference data management solutions allow for the quick production of consolidated reports by having all reference data in one place.

Imagine an organization that maintains reference data in different systems, making it difficult to produce consolidated reports. It might take significant time and effort to reconcile data across systems before producing reports. Reference data management provides a centralized system where all reference data is stored, making it easy to produce consolidated reports quickly and efficiently.

Consider a manufacturing company that operates multiple factories and supply chain partners across different regions. Without a centralized system for managing reference data, it can be time-consuming to produce consolidated reports on production volume, inventory levels, and shipping status across all locations.

7. Redundant IT implementations

Implementing the same features for reference data maintenance in multiple systems can be redundant and inefficient. Reference data management solutions eliminate the need for redundant IT implementations and provide a centralized system for reference data maintenance.

Without reference data management, an organization might need to implement the same features for reference data maintenance in multiple systems. For example, if an organization has multiple systems that maintain reference data, it might need to implement validation features in each system, leading to redundant IT implementations. Reference data management provides a centralized system where all reference data is maintained, reducing the need for redundant IT implementations.

Think about a scenario where a telecommunications enterprise stores customer information in various systems, such as billing and customer service platforms. The lack of a reference data management solution could force the company to install equivalent functions for preserving reference data in every system, resulting in duplicated IT implementations and maintenance expenses.

Key takeaways

In conclusion, reference data management is a crucial aspect of any organization that deals with data. By implementing a reference data management solution, organizations can solve several challenges that arise with managing reference data efficiently. From reducing costs to improving data quality, reference data management solutions provide an efficient and effective solution.

Reference data management is closely related to other data management practices, such as master data management (MDM), data governance, and data quality management. Organizations should adopt a holistic approach to data management, integrating these practices into their overall data strategy.

The Accurity Reference data management solution

If any of these challenges sound familiar, get your demo to see how Accurity Reference data management can solve them.