By Thomas H. Davenport, Jul 25, 2017
I have been thinking about some of the changes over the last decade in analytics, coinciding with the revised and updated release of my book with Jeanne Harris, Competing on Analytics. The book is ten years old, and much has changed in the world of analytics in the meantime. In updating the book (and in a previous blog post about the updates), we focused on such changes as big data, machine learning, streaming analytics, embedded analytics, and so forth. But some commenters have pointed out that one change that’s just as important is the move to self-service analytics.
By Joanne Chen, Jul 20, 2017
Over the next ten years, I don’t believe AI is overhyped. However, in 2017, will all our jobs be automated away by bots? Unlikely. I believe the technology has incredible potential and will permeate across all aspects of our lives. But today, my sense is that many people don’t understand what the state of AI is, and thus contribute to hype. So what can AI do today?
By Geoffrey Moore, Jul 18, 2017
Okay, so you know a sector is in trouble when there is a Web page in Wikipedia entitled “The Retail Apocalypse.” This post is not about how much trouble retail is in. This one is about how it can get out.
By Bill Franks, Jul 13, 2017
Artificial intelligence has quickly become one of the hottest topics in analytics. For all the power and promise, however, the opacity of AI models threatens to limit AI’s impact in the short term. The difficulty of explaining how an AI process gets to an answer has been a topic of much discussion. In fact, it came up in several talks in June at the O’Reilly Artificial Intelligence Conference in New York. There are a couple of angles from which the lack of explainability matters, some where it doesn’t matter, and also some work being done to address the issue.
By Peter Moore, Jul 11, 2017
Over the past two years, I observed a very distinct pattern between companies that successfully navigate the new digital world and those that fall behind. As it turns out, those who are emerging as the early leaders in the age of digital disruption share one thing in common – a clear statement of intent. This blog includes examples that have helped shape my thinking on this issue.
By Sarmila Basu, Jul 06, 2017
When you bring together a wildly diverse group of geniuses, the hard part isn’t finding work for them to do; it’s finding something that’s hard for them to solve, something so challenging that they get a little bit mad and a lot fired up. If not, they’ll get bored and they might wander off. That’s why it has taken me seven years to build my team: an eclectic mix of statisticians, economists, mathematicians, electrical engineers, biophysicists, and telecommunications specialists who are helping shape the way Microsoft uses data.
By Thomas H. Davenport, Jun 29, 2017
Analytics and big data have penetrated most large organizations by now, and are helping to improve many internal decisions. But they can also have a major impact on the decisions of customers or citizens. This applies not only to decisions about what products to buy, but also to decisions about safety and crime.
By Beth Kotz, Jun 27, 2017
As businesses increasingly adapt to the realities of modern technology, data security has become a critically important component of any successful business plan. Business runs on data – whether it’s financial records, credit card numbers, medical records, email addresses or anything in between – and companies that fail to adequately protect that data leave themselves and their customers exposed to tremendous risk. As high-profile incidents at Target, The Home Depot and other large companies have shown, data breaches can incur millions of dollars in expenses and damage the trust of consumers. This blog is a more detailed look at the true cost of a data breach, as well as best practices for keeping digital data safe and secure.
By Jack Phillips, Jun 22, 2017
Last week I had the pleasure of meeting with two promising new(ish) data analytics companies that are worth exploring: NGDATA and Podium Data. Both have established and tested products, clear value propositions, and a strong list of initial customers.
By Geoffrey Moore, Jun 20, 2017
There is a lot of serious talk in America these days about improving the state of our manufacturing sector. Smart products, Internet of things, robotics, predictive maintenance—all great stuff. But none of it addresses the most fundamental challenge facing the sector: how to deal with a demand/supply inversion which has made the customer king.