“State and government agencies are grappling with huge amounts of data,” says Michael Anderson, chief strategist for Informatica’s public sector business. “Most recognize that the data can help them make better decisions, and they’re also looking for cost reduction.”
Most important, the job of the fabric is to function invisibly. “We do the plumbing behind the walls to get you the data,” Anderson says. The required software doesn’t live onsite. It sits in the cloud churning away while workers access the information they need without headaches.
Informatica’s software focuses on data integration, be it in warehouses or lakes, which are different. “A lake is like that kitchen drawer you put all your junk in,” Anderson says. A warehouse is far more organized.
Next, the company automatically cleans up and identifies the data for later retrieval. Once the groundwork is complete, the information is available for use.
Ideally, in the near future, a state worker at the Department of Motor Vehicles may pull up information about a driver from a dozen different sources without ever knowing it was knitted together. The objective is to make information retrieval easy.
“It’s a single point of entry into what are many complex interactions,” says Jason Adolf, industry vice president of global public sector for Appian.
What Is Landed Data Fabric vs. Logical Data Fabric?
There are two approaches to data fabric architectures. Many vendors, including Informatica and Talend, store copies of retrieved information in centralized locations, where it gets cleaned up and then accessed. This “landed data fabric” approach differs from “logical data fabric,” where information stays in its original place.
Data management firm Denodo embraces the logical data fabric approach.
“Denodo brings computing to the data,” says Bill Sullivan, vice president and general manager of U.S. federal for Denodo. “Government is waking up to the cost of moving data in and out of the cloud.”
Denodo uses a data virtualization scheme, which leaves information at its source. According to the company, virtualized views allow workers to find data easily and understand its content, currency, quality and validity before using it. “We can cache data for specific inquiries without impacting an agency’s production system,” Sullivan says. The bottom line is that it’s cheaper and less disruptive.
Data.World also uses a logical fabric architecture. The company helps customers build enterprise data catalogs so information can be searchable and findable, says Juan Sequeda, principal scientist and head of Data.World’s artificial intelligence lab. It’s all about establishing and keeping track of an agency’s metadata.
“If you have a big bucket of data, you create a map of how things are connected,” Sequeda says. “You want to create a prioritized list” of information sources, which is fed into a knowledge graph.