AI models can benefit as much from soft data such as personal anecdotes as much as hard data. It's well known among data science circles that the more diverse your set of training data, the more accurate your model will be. This includes structured, unstructured, and semistructured data. However, not all data is treated equally, especially when it comes to unstructured data. Soft data such as collective memory and personal anecdotes can be challenging to access, but they can help build better decision-making systems.

The major hurdle for any machine learning model to succeed, in the majority of cases, is the lack of adequate data. If the model fails even for an instance, for sure the team would be looking for a new set of data that is compatible with the context. One particular type of data that can help ML engineers is dark data. One may have many questions like, what is dark data? How is it different from big data?

Did you know that a recent estimate suggests that 90% of a company's data consists of different types of dark data? As the interest in big data has increased rapidly, the amount of information businesses collect has also grown over the last few years. To make use of more and more data, companies are investing in talent and modern technologies to leverage the value of this data. But despite the efforts, nearly 60-73% of all enterprise data goes unused. For example, in the manufacturing industry, it has been estimated that around 90% of data generated by analog-to-digital conversions and sensors never really get used!

IT leaders seeking to derive business value from the data their companies collect face myriad challenges. Perhaps the least understood is the lost opportunity of not making good on data that is created, and often stored, but seldom otherwise interacted with. This so-called "dark data," named after the dark matter of physics, is information routinely collected in the course of doing business: It's generated by employees, customers, and business processes. It's generated as log files by machines, applications, and security systems. It's documents that must be saved for compliance purposes, and sensitive data that should never be saved, but still is.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Data teams can provide insight into important information. They can help with making key decisions, as well. However, data teams are also expensive. The need to hire the right people and equip them with the necessary tools to collect, organize and analyze data can be costly.

Dark data " is the glaring issue at hand; everybody knows it's there. However, undertakings would rather not address it. It's frequently viewed as "another person's concern," whether that is the IT, consistence, protection or lawful division. Dark Data has little worth if inappropriately made; it's unsafe and exorbitant to store, and every year that obligation just becomes bigger. Indeed, even the positive thinker concurs the expression "Dark Data " conveys an unfavorable undertone of a poisonous universe like in Star Wars, keen on annihilating the ton of good and splendid information.

In this contributed article, Ronen Korman, Founder and Co-CEO at Metrolink.ai, explores the problem of dark data through the prism of known and unknown unknowns, as used in intelligence analysis. Dark data is a known variable with an unknown value, as the exact benefits of its exploration are often unclear. True business value may in fact lie in the unknown unknowns, the data that the company has access to, but is not collecting at all due to a lack of a broader strategic outlook.

Dark data is different in each industry. "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.

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.

Data democratization should be a top concern for every company moving forward. We're approaching a point where the problem of too much data (and too few insights) can't be ignored any longer. Companies are generating more customer, employee and operational data than ever. But that data remains underleveraged and, in some cases, a liability. According to a report from Splunk, 55% of the data in every organization is "dark data," or "all the unknown and untapped data across your organization, generated by systems, devices and interactions."