Research

The analysis of Internet of Things (IoT) data is quickly becoming a mainstream activity. For this blog, I’m going to focus on a few unique challenges that you’ll most likely encounter as you move to take IoT data into the AoT realm.

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Inquiry Response: Approaching EDW and Analytics for Hospitals on Epic

By Gwen O’Keefe, Aug 07, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We’re considering integrating Caboodle and would like to learn from other healthcare organizations using Caboodle if it is their core enterprise data warehouse (EDW), or if other solutions have been supplement to meet their needs.

  • What role does Caboodle play in your overall enterprise data strategy?
  • Does Caboodle operate as your core enterprise warehouse, or are supplemental solutions being used?
  • What data are you collecting in Caboodle as opposed to supplemental systems?

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Inquiry Response: Tips for Building Your Analytics Toolbox

By Mark Molau, Jul 31, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We are considering having our analysts use the same set of tools and are considering MicroStrategy because it is readily accessible. What should we take into consideration when selecting analytics tools to support the business?

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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.

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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?

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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.

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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.

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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?

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Understanding Power in the Digital Economy

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.

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Move Your Analytics Operation from Artisanal to Autonomous

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.

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