Data is the New Oil: Data Standardization Fuels Digital Transformation

Posted 31 January 2019 11:10 AM by Scott Mackey

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 resources 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 a 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.

Businesses that thrive as digitally transformed enterprises are those that are best able to leverage the full breadth of their data. These companies harness their readily accessible customer data, but are also able to undertake research, build products and execute processes using all manner of other, less client-centric data.

Oil and gas companies, for instance, leverage structured data from well log files to improve operational efficiency. They draw many critical insights from the content associated with the land, equipment, analysis and reporting done.

Life Sciences organizations have massive volumes of research data, either created in the past or inherited via M&A activity, that contain critical IP they can mine for advantage.

Manufacturers often have volumes of reports, facilities documents, contracts, bills of material and invoices containing key data that, when cleaned and analyzed, could reveal powerful insights leading to new product innovations or accelerated development cycles.

And, once structured, data is not just fuel for developing these types of insights, it’s also critical for risk mitigation. Just as an oil spill fouls the environment and does untold harm to vast tracts of ocean or beach front, data breaches and PII violations can create pain and havoc for untold numbers of customers. The process of locating all instances of Personally Identifiable Information (PII), standardizing it in a structured form, and extracting and refining value from it, allows it to be safely stored, in known locations, and protected.

Wrap up

Data is the new oil—fueling the insight engines that enable businesses to differentiate themselves in the digital economy by creating more targeted products and services more quickly, creating better digital customer experiences and simplifying governance and compliance. This fuel is created through an automated data standardization process which— like the path that takes oil from an untapped, underground reserve to the gasoline in your vehicle’s tank—entails exploration, extraction, refinement and ultimate application in an analytics project. The implementation of such a process can ensure enterprises thrive in today’s digitally driven markets.

Tags: