By Bill Franks, Apr 12, 2018
As the Internet of Things (IoT) continues to explode, so does the need for the analysis of IoT data. At IIA, we call the analysis of IoT data the Analytics of Things (AoT). There are many success stories from the world of AoT. However, without some attention to standards of both IoT data itself and the analysis of it, organizations will struggle to achieve AoT’s potential. In this post, we’ll dig into several different areas where standardization must be pursued.
By Tanya Cashorali, Apr 10, 2018
Most people will agree that interviewing is one of the most difficult and least enjoyable professional activities in which we engage. Given the recent demand for data analytics and data scientist skills, it has become an increasingly daunting task for managers to adequately test and qualify candidates. Our team at TCB Analytics has interviewed hundreds of individuals with various backgrounds over the years and needed a more efficient way of quantifying technical and cultural fit. This led us to design a deceptively simple data exercise, which reveals a surprising amount of information about the interviewees.
By Geoffrey Moore, Apr 05, 2018
Everybody is talking about it. Mine your data for insights. Use your digital exhaust to feed machine learning programs to develop algorithms that will transform your productivity. Amazon does it. Google does it. Microsoft does it. All three offer cloud services to help you get started. Shouldn’t you be doing it too?
By Thomas H. Davenport, Doug Gray, Mar 22, 2018
While we are supportive of companies’ efforts to hire quantitative Ph.D.’s to practice data science, we believe that most firms are better off hiring people with other types of training and general management skills to manage analytics and data science groups. Why? Because there are a series of traits that make for effective managers of such groups, and most Ph.D.’s don’t tend to have them. We describe ten of those traits in this blog, and the reasons why they are unlikely to be found in the average doctoral degree holder. The list of traits may be useful for anyone seeking to hire a leader of analytics or data science functions-whether they are considering Ph.D.’s or not.
By Linda Burtch, Mar 20, 2018
Is the analytics and data science market starting to settle down? Our recent analysis of job change and tenure shows an interesting new development, which certainly suggests that things are leveling out a bit.
By Kathryn Hume, Mar 15, 2018
In November, I gave the opening keynote at an event about the future of commerce at the Rotman School of Management in Toronto, where I shared four insights outlined in this blog and also heard a great anecdote from an audience member.
By Geoffrey Moore, Mar 13, 2018
Everyone gets that data is the new oil in the digital economy, but not everyone gets that there is a critical difference between data as records—data in databases—and data as signals—data from log files, sensors, social media posts, and the like. Let me explain.
By Bill Franks, Mar 08, 2018
Within a two- to three-year span, Artificial Intelligence (AI) has gone from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell “Artificial Intelligence” a few years ago are now charging full steam ahead to pursue AI initiatives. A common question that is raised is, “Why is now the time for AI?” After all, there have been bursts of hype around AI multiple times over the last few decades. Is today different? And, if so, why?
By Peter Moore, Mar 06, 2018
Since 2000, over 50 percent of Fortune 500 companies have been acquired, merged or declared bankruptcy. Any company that cannot or will not transform itself into a digital enterprise faces an existential threat to its existence. To compete as a digital enterprise requires starting at the top with the CEO and Board agreeing on the right digital strategy for the company and a game plan to implement it.
By Thomas H. Davenport, Feb 27, 2018
One of the fastest-growing areas of artificial intelligence—at least if that term is defined broadly—is “robotic process automation,” a set of capabilities for the automation of digital tasks. RPA, as it is often called, has some valuable functions, but digital-centric companies may need more intelligence and process simplification to than RPA can currently provide.