A potential treasure trove of information exists just beyond the purview of many corporate analytics programs. A strategy for uncovering this “dark data” could unlock previously inaccessible business insights.
Dark analytics helps us the highlight the hidden opportunities under the unstructured data. Dark analytics can help us to make use of advanced technologies to extract valuable information from unstructured documents, documents with poor image quality, handwritten documents, voice-based interactions, and images. The impact and benefit of unlocking Dark Data to pursue STP is enormous: by automating end-to-end, insurers can increase efficiency gains, reduce risk, and realise the potential of intelligent automation technologies. However, in the era of data-driven businesses, the benefit from unlocking Dark Data stretches far beyond the empowering of STP. Dark analytics assists in recognising the unseen opportunities mainly in sales and marketing processes by analysing customer behaviour insights. According to Deloitte, dark data extends into information that can be found “in the deep web and the dark web – which comprises everything online that is not indexed by search engines, including a small subset of anonymous, inaccessible sites known as the ‘dark web’ ’’. It is impossible to calculate the deep web’s size, but it is estimated to be 500 times bigger that the surface web that most people use. Since the data is significantly big and lack of structural algorithms might make it difficult to utilise it completely. Thus machine learning and AI can help in extracting the big data with the view of maximum utilisation.
The key to leverage the dark data better is having a clear outline of a problem to be solved. With a context of problem solving, it is easier to anticipate the sources from which you may find the answer. Data scientists might unearth these data for a valuable business, better customer service and operational insights. When we imagine the power of data analytics and its potential, we often are limited by the structured data presented before us. Dark analytics helps us to remove the limits and capture a wider picture of non-traditional utilisation of unstructured data.
Data in the deep web has the largest body of untapped information. These data maybe curated by different party domains. This is the gold mine of network every company needs. Data mining and analytics are limited to define and focus one target at a time. Identifying different potential targets at a time, companies will be able to curate competitive intelligence. For example, Deep Web Technologies builds search tools for retrieving and analyzing data that would be inaccessible to standard search engines. Its software is currently deployed by federal scientific agencies as well as several academic and corporate organizations. Stanford University has built a prototype engine called Hidden Web Exposer that scrapes the deep web for information using a task-specific, human- assisted approach. Other publicly accessible search engines include Infoplease, PubMed, and the University of California’s Infomine. Indeed, dark analytics efforts that are precise in both intent and scope deliver the greatest value. Like every analytics journey, successful efforts begin with a series of specific questions. What problem are you solving? What would we do differently if we could solve that problem? Finally, what data sources and analytics capabilities will help us answer the first two questions?
Answering these questions makes it possible for dark analytics initiatives to illuminate specific insights that are relevant and valuable. Most of the data universe is dark, and with its sheer size and collection across various domains it can be utilised to focus on areas that matter to the business.