How to mine dark data with machine learning and AI Posted on : Sep 22 - 2021

Machine learning and AI can transform unstructured dark data into valuable business insights. Learn how to process dark data and use the information to your advantage.

To compete in modern digital environments, machine learning, deep learning and AI are increasingly accessible. By using machine learning and AI, companies can use dark data to acquire more competitive business insights.

Dark data consists millions of unstructured data points that businesses accrue and store in multiformat data lakes. Until recently, there have been few tools available to mine these massive volumes, but that's changing.

Explore different approaches to process dark data and how organizations can harness that information to strengthen machine learning results.

Define dark data

Dark data is different in each industry. It's primarily unstructured, untagged and untapped information that flows through every organization. "Classic" dark data, while captured and stored, is never analyzed. It comprises everything from log files, company documents and emails to social media sentiment, webpages, tables, figures and images. Increasingly, companies are deploying sophisticated technologies to process this data to gain valuable business insights and drive systems automation with deep learning algorithms.

Companies apply the three components that comprise machine learning: models, training data and hardware. Models have become a commodity due to the availability of user-friendly frameworks, including TensorFlow, PyTorch and Keras. Developers can easily install the latest natural language processing (NLP) models, deploy them and begin to see results.

Even with standardized models and hardware, technicians still must supply the training data -- and engineers must structure it. The information is often noisy and imprecise, but finding the connections between unrelated pieces of information is key to uncover dark data's potential.

The manual processes to label and manage dark data are inefficient and consume valuable time and resources. Dark data analysis tools, such as DeepDive, Snorkel and DarkVision, streamline categorization and help computers understand human-generated documents. View More