What is dark data?

As defined by Gartner, dark data are ‘information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes’. Those “other purposes” include, e.g., analytics, Machine Learning, Business Intelligence, or monetizing.

We may even compare dark data to dark matter in physics – often it comprises a majority of a company’s information assets. Nonetheless, storing and securing such data typically causes more expenses or even risks than… value.

Dark Data in business

Living in a fast-paced world, the corporate culture encourages the bottomless data lakes that employees are creating. Daily examples? Saving the mail ‘just in case’, partially developed app as an unfinished project, archived files or code snippets that are never to be used again…. However, the term ‘dark data’ is much broader and covers all the unstructured and chaotic data streams/objects which need to be analyzed in order to make it helpful. The dark data in business potential is the eventually structured and analyzed information stream that may be exceptionally helpful.

Dark Data analytics

Dark data analytics is the way to discover the new development opportunities or the cost reduction areas.

  • In terms of user behavior – it can show where you acquire or lose your customer.
  • In terms of CRM – it can highlight the sales funnel improvement areas.
  • In terms of Machine Learning or Business Intelligence – it can provide your ML models with more predictive power and make your BI reports extremely accurate.

The proper processing can even provide you with the geolocation insights that are just waiting to be used. Nowadays, the challenge is not to hoard and store as much data as possible, but to structure the hoarded (and constantly growing) assets in a way it CAN be analyzed.

Dark data structurization

When to start? The sooner – the better. Every day of chaotic data collection means greater structurization challenges.

Move your data into a secure container and secure it once again – safety comes first. Then, work on the datasets accordingly to the desired results. You can cut out the unnecessary parties, you can delete and deduplicate records, and then share the access with your business analysts. All your actions should be planned. And, of course, you do not have to do it all manually. If you require any help, consider using the trusted tools.

Algolytics created a complete and scalable technology line that collects, structures, and etiquettes data records – to be used in ML & BI especially, but not only. In the case of geolocation, the company’s experts created a solution that improves the address data quality (even from a various different sources), corrects mistakes and typos, and deduplicates your records in a web app or on-premise. You can check the demo by yourself – and decide if this is the way you want your data to work for you.

Pin It on Pinterest