If oil was the resource that powered the great industrial gains made in the 20th century—the rise of the automobile, airplane and assembly line—then data is its 21st century corollary. This raw resource is fueling the growth of both the biggest companies on the planet and the explosive, born-digital start-ups who are eating up market share. Data, the raw material fed to analytics engines, is vital to an organization’s success in delivering better customer experiences, accelerating product and service innovation, and streamlining compliance.
Dirty Oil Clogs the Engine: The Unstructured Data Challenge
The challenge is that, although data (unlike oil) is an effectively infinite and reusable resource, that doesn’t mean it is necessarily easy for enterprises to find, extract, refine and put to use. In many companies, as much as 80 percent of their data is locked away in inaccessible unstructured data (emails, image files, CAD files, etc.) This dark data can’t be leveraged for analytics, RPA or machine learning—meaning that organizations must convert it to structured, high-quality data through a data standardization process. Like the process for getting from raw oil reserves locked away underground to the gasoline that powers your vehicle’s engine, data standardization requires four steps: exploration, extraction, refinement and application.
Step One: Data Exploration
This first step in the data standardization process is akin to the mapping and test-drilling that mining companies perform when searching for new oil reserves. In the case of data capture (or exploration), the goal is to find unstructured data hidden in every file share, ECM and repository in an organization—not something that can be easily accomplished using a manual process. Instead, all repositories need to be crawled (and this needs to be done automatically and consistently to keep up with the new volumes of data constantly being ingested).
As the repositories are searched and unstructured data is uncovered, any duplicates and all of the ROT (redundant, obsolete and trivial) data is deleted or migrated into different storage. As it‘s encountered, each document no matter what its file type, is automatically converted to a unified format, such as a readable PDF, to prime it for the next step after data capture is complete.
Step Two: Data Extraction
Like oil sitting underground, unstructured data doesn’t have value until you extract intelligence from it. For companies seeking to leverage the full depth of their content, this means executing data extraction to garner value from it. Files are grouped, auto-tagged and classified. Values are extracted from the files so that companies can understand different document types (i.e. invoices, contracts, etc.)
Step Three: Data Refinement
Once a company’s unstructured data has been standardized and extracted into clusters of high-value content, it’s ready to be refined so that it can be used by analytics engines—much like crude oil gets processed into gasoline before it can be used to run a vehicle.
Data preparation typically includes some freeform extraction of the information that’s in a document. The operations performed on that unstructured data may vary depending on the document type, the customer and the use case. For example, a bank may have huge numbers of legal contracts, and they may need to find all of those related to their involvement with a certain business in the last 10 years, so they can say, “Give me party A, party B, and the clause, and the relevant dates and the relevant properties associated with this agreement.”
Step Four: Data Application
When refined oil is turned into gasoline, it can be used to fuel the engines that drive manufacturing, motoring and the machines we rely on. In the same way, once unstructured data has been structured, it becomes the fuel that can be fed into any number of analytics engines, RPA, AI, and machine learning applications. This ultimately results in accelerating business decisions, simplifying compliance and improving the customer experience.
As the repositories are searched and unstructured data is uncovered, any duplicates and all of the ROT (redundant, obsolete and trivial) data is deleted or migrated into different storage. As it‘s encountered, each document no matter what its file type, is automatically converted to a unified format, such as a readable PDF, to prime it for the next step after data capture is complete.