Data fabrics hold the key to unlocking data value. But first, business executives have to understand how fabrics work, and IT needs to know how to build them.
An inventory management specialist for a large enterprise once told me that he knew every supply depot where various types of parts were stored. Because of this familiarity, he could effortlessly source any item from anywhere to anyone quickly. In a way, this can be equated with information supply and demand within a company. What if you could automate processes to deliver any kind of information—structured or unstructured—to any business function whenever it was needed because you knew exactly where to get it?
SEE: Digital transformation: A CXO’s guide (free PDF) (TechRepublic)
There is a name for this kind of agile IT data supply chain: data fabric.
Gartner defines a data fabric as “a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable and inferenced metadata assets to support the design, deployment and utilization of integrated and reusable data across all environments, including hybrid and multicloud platforms.”
The best data fabrics work on a just-in-time basis. They are able to cull through multiple data sources that might exist anywhere and aggregate the right types of data needed to deliver value to a business user.
“The first step [toward building a data fabric] isn’t a technical one,” said Sean Knapp, founder and CEO of ascend.io. “Organizations should start by defining their core principles, the ‘why,’ and the key metrics associated with their fabric.”
In the process, Knapp encourages IT to work hand-in-hand with business users to see what value these users want to derive from their data. This in turn can point the way to developing a data fabric that is most conducive to the information pipelines that the company needs.
“By enabling more individuals across the company access to not only raw data sets where they reside, but derived and refined data sets produced by colleagues, companies can unlock the network effect [and value] of their own data products,” Knapp said.
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In other words, executives, managers and line workers obtain the right information at the right time for the tasks they are working on. They do it effortlessly, because IT and software tools have constructed a matrix (or fabric) of data that is so malleable and intuitive that it can go to exactly the right data parts (like the inventory specialist) to pull together the information that is needed.
If this sounds good for a C-suite executive, it is, because he or she will get to decision-making information faster. But will he or she support the idea of a data fabric when it is a somewhat nebulous term that only a technician would understand?
This is exactly the quandary for CIOs, DBAs and others who want to champion data fabrics. They must somehow convince management that data fabrics and the tools and training investments required to build them are worth it.
One way to present the idea of a data fabric to a non-IT decision-maker is by comparing it to other network-based elements with which they are familiar.
Executive decision-makers understand the value of networking in their professions, or networking in social media.
Data fabrics are not unlike those other forms of networking and association. They organize a network of disparate pieces of data from a diversity of systems to make a whole. They do this through a power of association that is not unlike the power of association that is found in the human brain. In this way, an end-to-end data fabric is constructed for an enterprise where data can easily interact and recombine with each other.
SEE: 3 steps to build a data fabric to integrate all your data tools (TechRepublic)
The non-IT executive doesn’t need to understand the nuts and bolts of data fabric building, but he or she does need to know how data fabrics will expedite informant fulfillment through the organization.
On the IT side, initial training in data fabric development might be needed. From there, the IT team will need strong familiarity with the technologies employed across the organization.
“This is important for two reasons,” Knapp said. “First, IT must be able to observe and collect user behavior across systems to identify new data sets that are of value to the broader organization. Second, IT must be able to connect data across these systems, so that users are able to access and work with that data in their tools of choice.”
This is a tall order for most enterprises, but it is a necessary step if enterprises are going to master the deluge of data that they ingest each day in unstructured and structured forms, plus have a method for cultivating that data for immediate anywhere, anytime business use.
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