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 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 Mark Molau, Jun 07, 2017
Available to Research & Advisory Network Clients Only
I’m interested to discuss methods/options for scaling analytics across our highly matrixed organization. We have done a lot of work building out our analytics strategy, but now need to get more tactical. How can we make this transition while maintaining the current systems, building future systems and constructing a bridge between the two?
By Geoffrey Moore, May 09, 2017
We are all stakeholders in the economic systems within which we live and work, and the better we can understand their dynamics, the more likely we are to navigate them successfully. For the most developed economies of today, this means understanding the transition from an industrial to a digital economy, and specifically, how economic power is migrating from familiar to unfamiliar sites.
By Thomas H. Davenport, May 02, 2017
Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation. There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t. Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.
By Peter Moore, Apr 25, 2017
Less than 30% of companies have a process in place to measure the return on investment of their emerging technology projects according to a recent survey of 150 CIOs and CTOs. Too many companies still measure the performance and business value they get from IT based on the old work of IT rather than the new work of IT.
By David Alles, Apr 24, 2017
Available to Research & Advisory Network Clients Only
Strata is a large conference covering a diverse set of data, analytics, and business topics. Tuesday (3/14/17) featured morning and afternoon tutorials (22 total with half day and full day sessions) covering a range of topics including: Developing a Modern Data Enterprise, Getting Started with TensorFlow, Architecting a Data Platform, and Determining the Economic Value of Your Data. Wednesday (3/15/17) and Thursday (3/16/17) featured keynote sessions in the morning followed by 45-minute breakout sessions until late in the afternoon. There were up to 17 breakout sessions in each session block and the conference also had an Expo Hall featuring over 150 vendors. Our objective for this report is to summarize the common themes and key trends emphasized at Strata into an easy-to-read guide that can serve as both a general reference and a resource for planning analytics initiatives. With this in mind, the report is organized into the following seven sections.
By Geoffrey Moore, Apr 20, 2017
As I have discussed in prior blogs, the focus of enterprise computing for most of the 20th century was on deploying Systems of Record, first on mainframes, then minicomputers, then client-server systems. These were and continue to be the transaction processing backbones that drive global commerce. In the first fifteen years of this century, however, we have seen a profound shift in spending emphasis away from Systems of Record, which are now in maintenance mode, and toward Systems of Engagement, the focus being on connecting with customers, partners, and employees in digitally effective ways leveraging the ubiquity of smart phones. That movement has been inside the tornado for some time now such that, while there will be a lot of money spent here over the next ten years, I think it is time to look ahead to the next wave.