What every data manager should know about dark analytics
Often, a company’s data manager has access to more information than they realize. This intelligence is hidden in dark data.
In a landscape shaped by technology, companies are accruing incredible amounts of data that remains unanalyzed. Many enterprises do not have the capability to analyze ‘non-traditional’ data sources, such as images, videos, audio files, IoT sensor information, or the torrents of raw data locked in the deep web. However, with new advancements in visualization and cognitive analytics, business will soon be able to utilize this treasure trove of information. Here, find out what every data manager should know about the business potential of dark analytics.
Understanding dark data
Today, data is a company’s most valuable resource. Concealed within IT systems are vast reservoirs of information that can offer pivotal operational, strategic and customer insights. Once analyzed, these insights can influence decision making, prove theories, and clarify business approach. This information is otherwise known as ‘dark data’ – which within a business context, describes data that remains unstructured and un-analyzed.
Usually, dark analytics refers to raw text-based data, such as documents, emails, and text messages. However, new advancements in data science now allow for the analysis of video, audio, images and in some cases, exploration of the ‘deep web’. The deep web is where some of the richest analytics lie. Consisting of all the online information that is not indexed by search engines, the deep web is estimated to be 500 times larger than the surface of the Internet.
Where the data manager should begin
Analytics is now at the forefront of IT strategy and investment – and rightly so. In order to unlock the huge potential of dark analytics, a company’s data manager should seek out the most promising informatics talent. Together, businesses and tech start-ups can develop the tools and workflows to mine and manage dark data. This process begins with the following practical steps:
Instead of attempting to analyze an organization’s dark data in its entirety, consider specific questions that need to be answered. From here, the data manager can identify discrete dark data sources. Once these specific segments have been determined, the team can undertake a manageable project that will extract relevant information.
A company’s intelligence can be enhanced through publicly available data. For example, demographics, locations and statistics can be leveraged to create far-reaching, detailed reports. This readily available information can help to put analytics in context.
Not everyone in your organization will have the experience needed to interpret advanced analytics. Therefore, companies should work with data scientists to create visualization tools. Through graphs, infographics, and dashboards, data insights can be turned into actionable strategies by sharing intelligence with the widest possible pool of staff. As such, analytics can permeate every level of business culture.
Broaden your horizons
As businesses develop new capabilities, they should consider how to extend these advancements beyond the organization. For instance, data insights can benefit partners, vendors and customers, forming an integral part of your reference architecture.
Dark analytics: The key to profit
Exploiting dark analytics are what give businesses the competitive edge. In a recent report, IDC projected that businesses that contribute significant resources to data analysis can expect an additional $430 billion in productivity gains over 3 years. With so much to be gained, organizations should begin to invest in dark analytics to unlock its incredible business potential.