Reference data is a special subset of master data used to classify or categorize other data across the entire organization. Managing reference data is increasingly important in the big data era due to the massive influx of new data sources.
One of the benefits of reference data management is that it helps teams access, distribute, and update reference data across multiple systems in a consistent and controlled way to scale up business operations and analytics processes and meet all business needs.
Reference data management requires a new approach that considers big data's volume, variety, and velocity. Learn how to manage reference data in the age of big data.
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| How to manage reference data in the age of big data |
Reference Data Management in the Big Data Era
As the volume and variety of data increases, so does the challenge of managing reference data. In the big data era, organizations must be able to manage reference data to ensure data quality and integrity effectively. Keep reading to learn some examples of reference data management and how to manage it in the big data era.
What is Reference Data Management?
Reference data is a critical component of big data management, yet it's often overlooked or treated as an afterthought. Managing reference data in the big data era requires a new approach that considers big data's volume, variety, and velocity.
Organizations can use several approaches to manage reference data in the big data era. One common approach is to create a centralized repository for reference data. This repository can store all types of reference data, including master customer and product information, geospatial information, and regulatory lists. The repository can be housed in a traditional database or a big data platform like Hadoop.
Another approach to managing reference data in the big data era is to break it up into smaller chunks and distribute it across multiple systems. This distributed approach can improve performance by taking advantage of parallel processing capabilities inherent in many big data platforms. It also helps ensure that no single point of failure will cripple the entire system. However, distributing reference data across multiple systems can also make it more challenging to maintain consistency and accuracy across all copies of the data.
What are the Advantages of a Centralized Reference Data Management System?
A centralized reference data management system can provide several benefits for an organization. The first advantage is that it can improve data accuracy and consistency. This is because all the reference data is stored in a single location, making it easier to maintain and keep up to date. Additionally, a centralized system can help ensure that all users access the most accurate data.
Another advantage of a centralized system is that it can improve efficiency and speed up decision-making. This is because users can access all relevant data from a single location rather than having to search through multiple sources for information. This also helps ensure that everyone uses the same data sets when making decisions, reducing the potential for inconsistency or conflict.
Finally, a centralized system can help reduce costs and complexity. Having all the reference data in one place makes it easier to manage and maintain, leading to cost savings. Additionally, having a centralized system means only one point of contact for support and troubleshooting, which reduces complexity within the organization.
How can Organizations Manage Reference Data?
Reference data is important for many reasons, including powering analytics and supporting decision-making. However, managing reference data effectively in a big data environment can be challenging. This is because traditional methods of managing reference data, such as using a centralized database, are not well suited to big data environments.
Instead, organizations should use a distributed architecture for reference data management. This involves dividing reference data into multiple parts and distributing it across different systems. This allows organizations to take advantage of the scalability and flexibility of big data technologies while still managing reference data effectively. Additionally, it's important to use automated tools for managing reference data to keep up with the constantly changing nature of big data environments.
Managing reference data is increasingly important in the big data era due to the massive influx of new data sources. Organizations need a centralized, holistic view of all their data to use it best. Managing reference data is critical for ensuring data accuracy and consistency and enabling data-driven decision-making.