Research

Are Analytics Truly Self-Service?

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.

Read More »

Is AI over-hyped in 2017?

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?

Read More »

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.

Read More »

Push Your Analytics Out to Customers

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.

Read More »

Inquiry Response: Path Toward Advanced Analytics for the End User

By Mark Molau, Jun 27, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

Everyone talks about dashboards and tools, but tools often produce a one size fits all solutions. Our quest is to produce a solution that appeals to multiple end users, effectively socialize the benefits, and push for adoption. We hope to use predictive analytics for process optimization – our integrated delivery systems are heavy on processes – so optimization here could yield great value.

Questions:

  • How do you start to move into a more predictive analytics culture and encourage the application of advanced analytics?
  • Where should we start to ensure the different end user audiences are receiving useful information/insights in a way that is most valuable for them?
  • What approach should we take to ensure that the tools we create get socialized, engaged, and adopted?

Read More »

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.

Read More »

Inquiry Response: Data Warehouses Versus Data Lakes

By Josh Gray, Jun 19, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We have fragmented data everywhere, much of it traditionally structured but using dozens of different ERP systems and data warehouses and data sources. How might we proceed so we can actually make timely use of all this data? Would we be better off with a data lake rather than a traditional data warehouse?

Read More »

Deep Learning: Einstein or Savant?

By Bill Franks, Jun 08, 2017

Artificial intelligence is one of the hottest topics in analytics today. Currently the most popular member of the AI family, deep learning is solving some very difficult problems very well. Best known for image recognition, it is now being applied to a wide range of other problems. Given the success of the approach, it is easy to forgive people for thinking that deep learning is incredibly intelligent. However, once you dig into deep learning, you’ll find that as opposed to being a generally brilliant algorithm akin to Albert Einstein, it is much more akin to a savant like the famous movie character from Rain Man.

Read More »

Inquiry Response: Migrating to New Data Platforms and Data Sources

By Mark Molau, Jun 07, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

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?

Read More »

Getting Real About Autonomous Cars

By Thomas H. Davenport, Jun 01, 2017

I attended the MIT Disruption Timeline Conference on AI and Machine Learning. There was interesting content on a variety of topics, but a primary focus was on when specific AI capabilities might become generally available. One particular technology addressed was autonomous vehicles. The key question was when 50 percent of vehicles on US roads would be fully autonomous.

Read More »