By Thomas H. Davenport, Dec 16, 2014
There is little doubt that the intelligence sector in the US - including the Central Intelligence Agency or CIA, National Security Agency or NSA, parts of the Federal Bureau of Investigation or FBI, Homeland Security, and many other agencies - and elsewhere is quite accomplished at several aspects of data management and analytics. It’s also clear that businesses can learn from these organizations in several respects. Below are a few lessons from which business leaders could draw.
By IIA Faculty, Dec 10, 2014
Available to ERS Clients and Professional Members
Each year, IIA asks its faculty and leadership to offer perspective on what the world of analytics will look like in the coming year. This year we also solicited predictions from our diverse community of analytics practitioners. The following ten predictions were selected by a panel of judges to represent IIA’s outlook for 2015:
By Thomas H. Davenport, Nov 28, 2014
Unless you are willing to become a recluse and go completely off the grid, you are stuck with a high degree of transparency of your personal data. The only real course of action is to be selective in the services and relationships you consume that affect your privacy.
By Thomas H. Davenport, Oct 02, 2014
If a huge, big-iron-focused company like GE can jump headfirst into the data economy, any firm should be able to do it.
By Thomas H. Davenport, Sep 18, 2014
It’s evident that financial services are going to be very interesting users of big data over the next few years. Of course, there will be important regulatory and consumer privacy issues to navigate. It will also be important to figure out just how to make money from these data products.
By Thomas H. Davenport, Sep 04, 2014
The big data underachievers are companies that have had a lot of data for a long time, but haven’t done much with it. They had big data before big data was big, but for various reasons they simply didn’t use it to improve their business.
By Thomas H. Davenport, Aug 21, 2014
What kinds of activities and decisions should a company pursue as it wrestles with its big data strategy? I see two major decisions at first, and then several others that follow from them. I’ll use Monsanto as an example, since it is a company that is clearly moving from being a provider of seeds and herbicides to one that provides data and analytics-based products and services.
By Thomas H. Davenport, Aug 07, 2014
More than twenty years ago, consultants Stan Davis and Bill Davidson, in the book 2020 Vision, argued that a company’s “information exhaust” (information byproducts gathered in the course of its normal business) could be used to “informationalize” a business (develop products and services based on information) and turbocharge its performance. Their primary examples of this phenomenon were information companies—Quotron, TV Guide, TRW, and the like. They did argue, however, that any company in any industry had the potential to be informationalized by its data exhaust.
By Thomas H. Davenport, Jul 22, 2014
There’s been a lot of discussion about the shortage of quantitative analysts and data scientists in this world, and many people wonder where they will all come from. Today I have good news and bad news for you. The good news is that there are a rapidly growing number of educational institutions that are offering courses, concentrations, and degree programs in analytics and big data.
By Thomas H. Davenport, Jul 07, 2014
The press and blogosphere are full of references to “The Internet of Things” (TIoT) or even “The Internet of Everything.” It’s great to connect inanimate objects to the Internet, of course. But that’s only a first step in terms of doing something useful with all those connected devices. “The Analytics of Things” are just as important, if not more so.