 The digital universe, consisting of all the data we create annually, is currently doubling in size approximately every 12 months. According to research by IDC, it's expected to reach 44 zettabytes in size by 2020, that's 44 trillion gigabytes, and will contain nearly as many digital bits as there are stars in the universe. Likewise, it is estimated that by 2030 more than 90% of this data will be unstructured data. This explosion of data is of course far outstripping our capacity to actually use it. A small fraction is in a traditional structured form that is easily accessed and used by organizations. A larger section of big data is unstructured but at least somewhat accessible, while the vast majority is simply hidden altogether, going unseen and unused, and this is what we call dark data. As Alessandro Corione of IBM notes, 80% of all this data that is created is dark, unstructured data, data that the computers we've developed in the past 40 years are not able to analyze effectively. We miss 80% of the knowledge inside this data. Few organizations have been able to exploit non-traditional data sources such as audio, image, and video files, the growing flow of machine and sensor information generated by the Internet of Things, and the enormous stores of raw data found in the unexplored depths of the deep web. These all constitute dark data. As an example, we can think of supply chain data. A recent Gardner survey found that 85% of respondents felt that supply chain complexity is now a significant and growing challenge for their organizations. Supply chain is a data-driven industry spanning across a network of global suppliers, distribution channels, and customer base. This industry churns out data in huge numbers. Given that an estimated only 5% of this data is being used, there is ample opportunity for big data technologies to bring this 95% dark data to light. Dark data was a term coined by the IT consulting firm Gardner, who defined it 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 and physics, dark data often comprises most organization's universe of information assets. Data may be considered dark for a number of different reasons. Because it's unstructured, because it's behind a firewall on the Internet, it may be dark because of speed or volume, or because people simply have not made the connections between the different data sets. In many organizations, large collections of both structured and unstructured data sit idle. On the structured side, it's typically because connections haven't been easy to make between disparate data sets that may have meaning when combined, especially information that lives outside of a given system, business unit or function. We traditionally think and look inside of silos, and that's how we deal with data, making it difficult to bring different data silos together. Regarding traditional unstructured data, such as emails, messages, documents, logs, notifications etc. These are often text-based and reside within organizational firewalls, but remain largely untapped. This may be because they do not reside in a relational database, or because until relatively recently, the tools and techniques needed to leverage them effectively, such as text mining, did not exist. Buried within these unstructured data sets could be valuable information for organizations. The second dark analytics dimension focuses on a different category of unstructured data that can't be mined using traditional analytical techniques, such as audio, still image and video files from other sources. Much of the world's information is now being created in rich media, such as images and video, but computer scientists have long since viewed video as the dark matter of the internet universe, because they did not have the tools to analyze it. In 2016 alone, we took an estimated 1 trillion photographs. In the past, this was simply unstructured data we couldn't use, they were just collections of thoughts of color without meaning. And the same was true for video, unless someone put a tag on it to describe the content and text, it was effectively dark data. It's only very recently, with advances in machine learning and image recognition methods that this dynamic is changing. Google's video analytics API can now go through every scene in a video and identify specific elements in those scenes, such as a dog, birth decay, a mountain or a house. A search engine can then be implemented to look through these videos to identify specific features and when they show up in the video, thus converting the dark data into light data. As a major dimension to dark analytics, the deep web owns what may be considered the largest body of untapped information. The deep web consists of information that is not indexed by public accessible search engines today. According to a study published in Nature, Google throws up about 16% of the surface web. Popular science described it like fishing in the top two feet of the ocean. It's impossible for us to accurately calculate the depth of the web size, but by some estimates it's 500 times larger than the surface web that most people search every day. The domain's sheer size and distinct lack of structure makes it a dauntingly complex adventure. Data curated by academics, consortia, government agencies, communities and other third party domains, medical records, legal documents, scientific documents, multilingual databases, financial information, government resources, organization-specific databases, all are hidden from outside usage and largely unknown to anyone but their owners. To date, companies have explored only a tiny fraction of the digital universe for analytic value. The term dark analytics refers to turning dark data into intelligence and insight that an organization can use. Dark analytics seeks to remove these limitations by casting a much wider data net that can capture a mass of currently untapped signals. Recent advances in computer vision, pattern recognition and cognitive analytics are making it possible for companies to shine a light on those untapped sources and derive insights. New dark analytics companies like deep web technologies build search tools for retrieving and analyzing data that would be inaccessible to standard search engines. Another example is lattice data recently purchased by Apple, which is a company that applies an AI enabled inference engine to take unstructured dark data and turn it into structured and more usable information.