What is a data federation?

A data federation is part of the data virtualization framework. This data virtualization grew with data federation but sprouted extra features, applications, and functions. Data virtualization, therefore, has a huge range of functions outside of data warehouse compilation. It includes metadata repositories, data abstraction, read and write access to source data systems, and advanced security.

While a data federation is part of data virtualization they are not the same thing.

Data federation in business

One of the biggest challenges facing businesses today is managing data effectively. There can be multiple problems with data:

• Multiple cloud databases and different sites restricts access
• Large volumes of data need massive storage
• No consistency among data, requiring effort and time to cleanse and organize
• No single format of how or where data is stored

Data federation takes away a lot of the problems associated with raw data, saving businesses time and money. For instance, a data federation converts information from multiple sources and combines it into a single format. Then, it places all the databases in a single store virtually. This means, rather than creating another copy of the data, it integrates virtually, eliminating the need for another storage system.

Data federation should be part of a data management and virtualization strategy. This strategy combines cloud systems, data warehouse extensions, data integration, and a host of other data management strategies.

Benefits of data federation

No extra storage space required

Because the software does not make a full copy of the data from the source businesses do not need to invest in hardware. There is no need for expensive infrastructure or more data processing capabilities; it’s all managed by the data federation software.

A single source of truth

One accurate data source is invaluable. Not only does this save time when looking for specific information, but it is far more accurate. Regardless of where the updated, most recent data is entered, the data federation database will have it. This means less errors, happier customers, and more reliable business information.

Data silos are common, especially when businesses take a less-holistic view of IT. Data federations remove silos and ensure easy sharing throughout the business.

More reliable data for machine learning and artificial intelligence

A large part of a data scientist’s role is cleansing data: removing unneeded data points and double ups, finding the most recent information, and eliminating outliers. Data federation does most of this automatically. The resulting data is accurate, consistent, and offers superior predictions and outcomes.

Fast data access

No hardware and no complex infrastructure, just incredibly fast data access. Also, if software needs to be built, there is no need to create the warehouse and the extract, transform, and load functionality. It is far faster to create a data federation.

Accessible with minimal coding required

The barriers to entry are low to create a data federation. There’s minimal coding, and no need for dedicated, specialist IT staff. Simply install the data federation development runtime software on a standard server, create views and services, and fine tune the queries.

Cheaper than alternative options

In addition to not requiring physical hardware to store data on, a data federation does not need software licences, extra data governance, and expensive IT staff.

Minimize risk

Because the system is not replicating or physically moving anything, there is minimal-to-no risk of data loss. If the data federation system is set up correctly, any existing reports can be mapped so they run exactly the same way. No missing, lost, or confused data or reports, and no risk.

Problems with data federation

Unable to manage significant data cleansing

While some fine tuning and data cleansing takes place, very inconsistent or problematic data can pose challenges to the software and jeopardize business outcomes.

Solution: Data should be in relational or XML formats. If this is not possible, reconsider using a data federation, especially with very large or complex databases.

No historical data

When changes are made in most data systems, the historical data is retained in some form. That way, it becomes easy to track back, find, and resolve errors. However, data federations only have the most recent, current data.

Solution: Physical data storage systems are still required to capture historical data.

Needs consistent system capability

If a business’ computer systems are operating at maximum, or are struggling to handle capacity, then a data federation will not work. The infrastructure needs to handle the ad-hoc nature of enquiries without slowing essential data processing tasks.

Solution: System upgrades may be required to run data federations correctly.

What does the data federation look like in an organization?

An organization will often have multiple databases and separate data silos that are hard to access. With fragmented access to data, business information is inconsistent and unreliable.

A data federation brings all the data together. The control of the original databases remains with the division or branch, ensuring continued accuracy. This makes the implementation far more supported, with more political buy-in throughout all levels of the organization.

Staff and end users can access a variety of accurate reports and information, enabling better business intelligence, data for predictions, and outcomes for all stakeholders, including customers and suppliers.

Alternatives to data federation

The major alternative to a data federation is a data warehouse or enterprise data warehouse (EDW). These, much like a data federation, are a centralized repository that pulls data from multiple sources for analysis. However, unlike a data federation, they require physical integration.

This means that the data is collected from a range of sources and then stored in the data warehouse as a physical copy.

While this presents drawbacks, a data warehouse and data federation should not be considered an either/or outcome. They need to be used in conjunction with each other in order to create a seamless, flawless system that captures all relevant information. The data federation makes it easy for users to access the correct data, while the data warehouse provides a physical home for it.

The future of data federation

On average, large enterprises have around 40 singular databases. These systems all run in parallel and can cause a huge range of issues, decreasing the functionality and accuracy of a business. But since data federation became commonplace in the mid 2010’s, many of these problems have disappeared.

While organizations should focus on creating a holistic, easy-to-use database that avoids data silos and huge hardware costs, data silos are difficult to resolve. The pace of technology development means that a custom-built platform will be out of date within years, and no one piece of software will ever meet all business requirements. As legacy systems are retired, data still needs to be accessed.

This is where a data federation shines. As a part of a system that incorporates data warehouses, cloud and on-premises, and data integration, data federations become a seamless system satisfying all requirements. The challenges and weaknesses of a data federation are offset by the strengths of a data warehouse, making them the ideal solution to most business database problems.