/ A guide to data federation: Everything you need to know

A guide to data federation: Everything you need to know

Data federation is a data management strategy that can help you improve data quality as well as data accessibility. Data federation is the process of querying data from different sources into a single virtual format.

These federated systems allow for better data integration and analytics than other types of databases because they eliminate the need for massive data storage systems and can provide more accurate information by aligning data across multiple sources. If you’re considering using a federated data model for your business there are a few things you should know.

 

 

How does a data federation work?

Data federation doesn’t consolidate information or eliminate other sources. Instead, it leaves the data in the location it already resides while using virtualization techniques to provide a unified view of the information. Essentially, data federation makes it easy to access and find information from various systems as if it is located in one data source.

Data federation also makes it simple to access your information by executing federated queries. These data access points are usually quite easy to use. Even if you employ different data platforms with your data federated solution, end-users who need information from these sources can typically learn how to query their data in a short amount of time. In fact, using data federation can reduce the challenges of analyzing, searching, and finding relevant information in real-time.

 

Why is data federation so important?

Data federation is one of the most critical data management strategies in today’s data-driven world. With so much data being generated and collected every minute, it’s necessary to have a way to manage all of this information right when you need it.

Businesses across many industries are using data federation for better search results and analytics as well as improved customer interactions. Customers expect companies to be able to provide them with relevant information that pertains specifically to their interests or preferences; businesses can do just that by connecting different sources into one system where they can then use data integration techniques such as de-identification, masking, and anonymization effectively and efficiently. In a federated data model, all of this can be done without creating and storing redundant copies of data.

Data federation helps solve many of the problems that businesses and organizations face when it comes to raw data, whether it has to do with large amounts of data that need storage or a lack of consistency among the data.

 

 

Data federation vs. data consolidation

One common misconception that many people have when it comes to data is the assumption that data federation and data consolidation are the same things. However, this couldn’t be further from the truth. Data federation is a data management strategy that can help you connect data from different sources virtually. In contrast, data consolidation involves the process of converting data into one format where it can then be stored in a separate location, often in a data warehouse.

One primary difference between data consolidation (data warehouse) and data federation lies in the fact that once your data is in your data warehouse, there is no need to query any additional systems when looking for information because all of this information is located either in one queriable source. Once your data is there, it can be easy to search through and analyze.

Data federation often allows you to query the most recent data directly from a source system to offer more up-to-date information. This can give your business an advantage when it comes to making timely and accurate decisions. It also allows you to take action based on the data that’s coming in now, which could help you; improve customer interactions, pivot on marketing campaigns, increase or decrease operations spend.

 

Data warehouses vs. data federation

At a glance, data warehouses are very similar to federated databases because they can both pull data from multiple existing sources to provide information. However, data warehouses require a physical integration, meaning that they store a redundant copy of a dataset so information can be queried directly from it. A federated database on the other hand is virtual and doesn’t physically store any data. Instead, it provides an interface where users can query information across multiple data sources and serve up coordinated information across these non-congruent systems.

 

 

Benefits of a federated data model

Data security: There are many data management benefits that data federation brings to the table, including data security. Data federation helps organizations ensure their data is secure by using encryption techniques which make accessing this data difficult for those who don’t have authorization. With these processes in place, it helps to ensure that information is secure while also sharing your data with the right people, like teammates, co-workers, or clients.

One centralized system: Another data management benefit of data federation is that it allows businesses to have one centralized system where data from different sources can be accessed. A federated data model allows you to quickly pull the data you need when you need it.

Accurate data: Data federation takes data management to another level by helping to ensure the data presented is accurate. Businesses can access data from different sources in real-time, which can give them a better idea of what people are thinking or how they feel about their products, services, and more. Instead of relying on out-of-date sources, or one system to provide relevant information, you can search tons of data in seconds to have precise results for your business needs.

 

Conclusion

Data management can be challenging, especially when data is scattered across multiple systems that all need to work together. However, federated data models can help simplify data management, and when used correctly, can save businesses time and money. Data storage is extremely costly. Eliminating the need for redundant data copies through a federated data approach is an effective way to reduce data storage costs.

Choosing an industry-leading, federated-friendly business intelligence solution like Domo can help you avoid dataset duplication and unnecessary redundancy. Minimize storage costs, protect your data with encryption best practices, and quickly create datasets with Domo’s federated connectors.

Check out some related resources:

How dark data is hurting your business

Explore Data Commercialization Opportunities: A Guide to Taking Your Data to Market

Modern BI for All Field Guide: Data Agility

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