Gartner defines dark data as the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets.
These assets include emails, contractual documents, multimedia, and other intellectual property. Parsing, tagging, linking and searching all this dark data is the greatest immediate opportunity for most businesses to unlock the value.
With the advances in NLP and Machine learning, making sense out of this data and generating actionable insights to surface the risk, liabilities, and opportunities to renegotiate contractual terms is a low hanging fruit to prove the ROI of the AI initiatives in a company.
It’s a good idea to enrich an organization’s data with external data sources that will help in creating new services. The data from Experian, Nielsen and other data aggregators are being used by companies that provide identity theft management, social lending platforms, and others to make instant decisions by applying various machine learning models.
As companies evolve and face competition, these strategies help in pivoting if needed to unlock potential revenue streams. New age platforms that integrate the following features help organizations to benefit from their dark data:
My team built a platform (https://www.docskiff.ai) that can take the contractual documents ( MSA, NDA, SOWs, etc.) from different data sources such as Dropbox, Box, S3, and Sharepoint; based on what users are interested in extracting, the platform applies the machine learning models to extract and present the key attributes such as start date, end date, SLA’s, termination notice periods and any liabilities and risks associated. A powerful semantic search across all the documents helps in reducing time to get to the right documents in a matter of seconds and thus increases the productivity of the teams.