Deloitte Consulting has published its eighth annual report on the state of technology, calling the 2017 version, Tech Trends 2017: The Kinetic Enterprise. CTO Bill Briggs suggested in an interview that as the pace of switch-over to the digital economy picks up, it could also be called “The Frenetic Enterprise.”
The ongoing technology changes will continue to have an impact on IT and how it works. But the heart of the report highlights seven trends that cast a spotlight, amidst general change, on the most significant developments out there, from Deloitte’s point of view.
In Deloitte’s preferred order of importance, they are:
1.Dark analytics, or “illuminating the opportunities hidden within unstructured data.” Various estimates have labeled 80% of the data being generated in the enterprise as “dark” data, such as one by John Kelly, IBM’s senior VP who is sometimes described as the father of Watson analytics.
Relational data in SQL databases is sometimes thoroughly analyzed, but not all of it is put to use. Some of it remains dark data, not to mention all the information in emails, corporate contracts and other raw text documents and meeting presentations. Dark data also includes images, video and audio files, all of which can be searched for useful information and insights.
But doing so takes staff that is skilled in the modern, neural networks and other technologies that can analyze images and video for objects, facial expressions and details of consumer choices evident in them. Briggs said that with effective image analysis, retailers would have a major tool in predicting which fashions are about to become current and where to place that bets on lines of clothing.
2. Machine intelligence might overlap dark analytics in some areas, but when it comes to all the sensor data being generated on the Internet of Things, or the “deep learning” applied to server logs or specific assembly line operations, machine intelligence takes on a distinctive meaning. It’s a combination of machine-generated data with analytics. But unlike the majority of analytics systems, which are performing historical analysis or taking a compilation of data and looking backward at it, machine intelligence is trying to look forward. What’s needed, said Briggs in the interview, is to be “looking for insights that are predictive and prescriptive,” he said. In other words, identify known, pending problems or dilemmas, along with where their possible solutions might lie.