What is Unified Data?
Unified data brings disparate data sources together to present a single view of an enterprise’s data. The term most often refers to a combination of cloud-based and on-prem data that can be virtualized via a unified layer.
How is Unified Data different from Federated Data?
Federated data is what data looks like before it is unified; it’s the source data, from any number of repositories, that is referenced by an adaptive analytics fabric and presented as a unified database through data virtualization. Think of a group of states that become a political federation or a group of soccer teams that became the Fédération Internationale de Football Association (FIFA). For example, if a company has a group of databases that exist independently (think states), a technology such as AtScale’s A3 will virtualize that data (think FIFA) and then create one “unified” database that can be viewed from a single point of access by a variety of team members.
Why is Unified Data Important for Enterprise?
Unified data presents a complete, secure and accurate picture of what is happening in a business by allowing business intelligence teams and analysts to perform more robust and granular analyses. AtScale’s work with online retailer Rakuten provides a good example.
Rakuten’s data warehouse contained more than 50,000 data points spread across multiple databases and data warehouses. In order to run the analyses needed to make effective business decisions, data points from these different sources needed to be brought together. Virtualizing the data using AtScale’s Universal Semantic Layer allowed Rakuten’s business users to access a unified view of the data across sources using their preferred BI tools. If the Rakuten database was still spread across a variety of data warehouses, those critical points could not have been pulled together and made accessible to the right business users. Unified data helped business users and data scientists to quickly build and run queries to get all of the data they needed without complicated SQL scripts or resource-intensive IT requests.
Challenges and Considerations for Data Unification
Unifying data delivers value to organizations, but brings its own set of challenges in implementation: IT resources, security and compliance.
Can’t data be extracted from the different data sources in order to unify it?
It could. There are challenges. The ETL (extract, transform, load) process requires planning and resource hours by the data engineering team and can strain the data warehouse. Security is another challenge. Extracted data may contain PII (personally identifiable information), and would, therefore, need to be removed or else risk compliance violations.
How does data unification work with hybrid cloud environments?
The concept of the hybrid cloud is defined by multiple cloud platforms and on-prem databases existing within the same company. Some companies can store data on six or even more cloud platforms and several on-prem databases, some relational, some transactional. Taken individually, these databases do not accurately depict a company’s customers, sales or other activities and therefore cannot be analyzed effectively.
How Can Data Virtualization Help with Data Unification?
The first step in creating a unified view of data is to virtualize it. Data virtualization takes data stored in different locations, often with different data architectures and formats, and presents them as a single, unified view for business users. Virtualization allows enterprises to have the benefits of a single virtualized data warehouse, while the data itself may still reside in the disparate cloud and on-premise data warehouses. Business users query the data without concern for where that data lives or how it might be distributed across their company’s various data repositories.
Does every company have the same technology to unify data?
What does the future of data unification look like?
AI in particular will take the speed and scale of the hybrid cloud and the unified data it enables to a higher level. It is important to remember two points: 1) Data drives AI, not the other way around. If the source data is compromised, inaccurate, or incomplete, the AI algorithms will not produce accurate predictions. 2) Data scientists and data analysts don’t guarantee AI success. They struggle to access, normalize, clean and relate data into logical business structures, ready for consumption by their BI and AI tools of choice.
Unifying data will enable organizations to integrate more complete sets of data into their BI and AI tool. This will, naturally, result in more robust and complete outputs. Even the most sophisticated tools can only work when high quality data provides their fuel.