Research

It used to be that a doctor was a doctor for the most part. Even a century ago, unless you lived in a large city, people likely had a town doctor who handled most every type of ailment and guided most any type of treatment. Given the limited medical knowledge and lack of sophisticated treatment options during this time, these generalists could often provide a level of care that was comparable to the best available. Today, that is no longer true in medicine and a similar trend is playing out in analytics.

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Inquiry Response: Visiting Faculty Program

By Achille Ettorre, Mar 09, 2017

Available to Research & Advisory Network Clients Only

A Healthcare client needed to effectively stratify a patient population, predict readmission, and reduce clinical errors through advanced analytics, which was becoming an everyday occurrence. To augment the establishment of an Analytics Center of Excellence (CoE), Enterprise Data Warehouse and effective Information Management Governance, the client wanted to establish a “Visiting Faculty Program” aimed at educating its Analytics CoE staff through face-to-face learning sessions with industry experts.

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Three Paths for Aligning Analytics to Business Strategy

By Daniel Magestro, Jack Phillips, Feb 27, 2017

Available to Research & Advisory Network Clients Only

As organizations strive to build their analytics capabilities, an unexpected challenge has plagued many efforts: The activities of analytics teams and the investments made to support them aren’t in sync with what executives expect or desire. On the surface, it might have seemed straightforward for “business analytics” to be in sync with the business’s strategic needs. After all, the decision to invest in the first place was driven by the business’s needs, right?

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Inquiry Response: Self-Funding Analytics Groups

By IIA Faculty, Feb 24, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

A manufacturing client was contemplating how to approach the creation of a self-funding analytics group with their organization.  In this conversation, the client was interested to learn of potential group structures, how to increase stakeholder buy-in, personnel capabilities, and appropriate metrics to measure success.

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Talent Analytics, Corp. has a unique approach to workforce predictive analytics. At our firm, we measure success by how our projects quantifiably benefit the line of business. We watch it, track it, and report success. Our algorithms get better and smarter using the best data science methods available. I’ve been involved in the predictive workforce arena for almost two decades. I have to admit I’m surprised at how many vendors claiming to reduce employee turnover or increase employee performance do little more than offer a solution that “sounds” effective. They say the right predictive analytics buzzwords – without proving that their solutions actually work for their customers.

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Revisiting Common Obstacles to Analytics Success

By Robert Morison, Daniel Magestro, Feb 13, 2017

Available to Research & Advisory Network Clients Only

In 2013, IIA published a popular and useful research brief in response to a client’s general question: What are the common hurdles encountered when putting analytics to work in a business, both in developing analytical models and applications and in building enterprise analytical capability? For us, this question is so central to IIA’s mission of helping organizations navigate the many challenges to achieving analytics maturity, that we decided it was time for an update and a re-evaluation of common obstacles faced by analytics leaders and practitioners. This update is informed by major forces and trends in play over the past few years.

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Six Very Clear Signs That Your Job Is Due To Be Automated

By Thomas H. Davenport, Feb 07, 2017

In H. G. Wells’s classic The War of the Worlds, the narrator pauses a moment to rue the fact that he didn’t react sooner to the arrival of an “intelligence greater than man’s”—in his case, Martians landing on earth. Comparing himself to a comfortable dodo in its nest, he imagined those ill-fated birds also dithering as hungry sailors invaded their island: “We will peck them to death tomorrow, my dear.” And what about you? As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should? To help you get the head start you may need, here are the signs that it’s time to fly the nest. All of them are evidence that a knowledge worker’s job is on the path to automation.

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Inquiry Response: Scaling Analytics Talent Growth

By Greta Roberts, Feb 06, 2017

Available to Research & Advisory Network Clients Only

We are strategizing how to scale our analytics talent over the next few years, and looking for insights on how to determine how many resources are enough.

Questions:

  • What business drivers would cause our organization to grow our talent base? Do we have enough talent as it is?

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Technology-enabled innovation is not only disrupting the competitive landscape, it is redefining the user experience value proposition across a multitude of industries. It is also putting companies on notice that if you can’t successfully engage your customers in this new digitally mediated world you are on your way to being Uberized. The five waves of digital disruption (social, mobile, cloud, data analytics and connected devices) are completely altering how people connect, communicate and discover information. What these individuals are looking for are “friction-free” user experiences that delight and inspire them. These new tools affect how customers make decisions which affect their entire customer journey which ultimately affects their customer lifetime value. Simply put, it defines the differences between the traditional customer and the new connected customer.

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The Myth of the Data Scientist Shortage

By Thomas H. Davenport, Jan 24, 2017

Technologists and business folks alike overstate the shortage of data scientists. They just need to know where to look. Here are some common excuses companies use for not employing data scientists, and why they’re no longer valid.

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