We’re all guilty of some level of data hoarding. 

On a personal level, that may mean storing duplicates of the same vacation photo in a digital album or saving multiple drafts of a resumé which creates clutter on a personal device or cloud. Now think of this same scenario, but across an entire company. The amount of content stored is now multiplied across each individual, team, business unit, etc. creating mountains of data that are impossible to manage manually.

Industry estimates are that over 80% of data is “dark.” This dark data, is siloed, buried amidst redundant, obsolete and trivial content (AKA data ROT) and in multiple formats such as PowerPoint presentations, PDFs, images, etc. Uncovering this data can provide valuable insights that can fuel organizational intelligence and business decisions, but has traditionally been tedious and expensive to collect. Historically, this meant tasking teams of highly-trained professionals to spend days, weeks or months, manually sifting through and analyzing digital content. In most cases, these teams only come up with a fraction of the data they could due to project deadlines and human error that leaves critical insights undiscovered.

Until now.

Operationalizing Your Dark Data with Deep Learning

The possibilities around A.I. have been heavily hyped for a number of years now. However, a subset of A.I., deep learning, is showing real-world promise, particularly in data management and analytics. The manual data extraction and content analysis mentioned above has typically relied on humans telling machines a specific set of ways to find data and respond to queries. The problem (as discussed above) is humans are imperfect and thus, often don’t know every single way to perform these tasks successfully.

With deep learning, we’re able to shift this decision-making from human to machine. Instead of telling machines how to find the information we need, we can train algorithms on sets of metadata allowing us to tell the machine what we want and letting it decide the best way to find it. Given machines are much better at finding patterns in data than humans are, the task of sifting through and analyzing content is accelerated significantly, regardless of its format. Now what may have taken days or weeks can occur in seconds or minutes.

A Curated Data Experience

While the speed that deep learning provides speaks for itself, if the outcomes aren’t accurate then the value is lost. Fortunately, algorithms can learn just like humans can. By validating results, algorithms can learn the best answers and fine-tune query outcomes over time. This leads to a particularly helpful use case when it comes to research and business intelligence – smart tables. 

Regardless of industry, there is one commonality about the collection of intelligence – it’s almost always in a spreadsheet. While this helps with organization, these spreadsheets pile up and without regular maintenance can become obsolete. With deep learning, this no longer needs to be the case. New smart table technology is integrating deep learning directly into spreadsheets enabling autonomous updating of content based on new data that becomes available. The addition of natural language queries means users can quickly ask questions of the data set and the smart table will respond with its most accurate responses. This eliminates the need for consistent maintenance and unlocks users to timely intelligence. 

Vyasa Synapse Smart Table Technology

These capabilities enable a new level of data curation previously inaccessible without the manual work of data analysts. Now users can eliminate time-intensive maintenance while unlocking the most up-to-date intelligence that can fuel anything from clinical trial research to competitor analysis to disease discovery. 

Maintaining Data Sustainability

Managing exponentially growing sets of dark data will be a constant challenge for any company. However, with the right deep learning and new management architectures such as data fabrics, the way data can be approached and utilized is drastically different. Now data can continue to remain where it’s stored and deep learning applications can remove the manual labor associated with accessing it leading to happier, more productive research and analyst teams and greater share of intelligence across the organization.