5 Things New Analytics Leaders Should Do to Succeed

By Bill Franks, Thomas H. Davenport, Jul 19, 2017

Available to Research & Advisory Network Clients Only

There is a fair amount of management research suggesting that the first 90 days or so are the most important time of a leader’s tenure. It’s when you establish your reputation and it determines what people start to think about you in your role. It’s often hard to change those first impressions. Therefore, IIA held a webinar to discuss this very important period for senior analytics leaders like a Chief Analytics Officer, Chief Data Officer, VP of analytics, or similar senior role. This paper captures the key elements of the discussion between Bill Franks and Tom Davenport, which focused on five essential things new analytics leaders should do to set themselves up for success.

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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.

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Five Big Data Analytics Pitfalls to Be Aware of (And Avoid!)

By Bill Franks, Jun 28, 2017

Available to Research & Advisory Network Clients Only

Many people think that in the age of big data, we always have more than enough information to build robust analytics for almost every situation. Unfortunately, this isn’t the case. In fact, there are situations where even massive amounts of data still don’t enable basic predictions to be made with confidence. In many cases, there isn’t much that can be done other than to recognize the facts and stick to basic analytics instead of getting fancy. However, it is critical to recognize the situation before expending a lot of effort in a wasteful attempt to get predictive analytics to work in a situation where success isn’t in the cards. This challenge of big data that can’t be used to predict seems like an impossible paradox at first, but, as you’ll see, it is not.

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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.

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Any new leader in any field will have to face several challenges in the first few months on the job if he or she is to succeed. On May 11, IIA hosted a webinar where co-founder Tom Davenport and I discussed some of the challenges analytics leaders face and what they can do to ensure success. While the action steps apply broadly, we focused on how they apply specifically within the realm of analytics. This blog explores the key themes at a high level.

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Balancing Analytics Agility and Stability

By Bill Franks, May 11, 2017

There have been many science fiction stories (as well as video games!) that revolve around the tradeoffs between powerful, strong, hard to harm combatants and those that are small, nimble, but easy to harm. Both have their merits and both can be useful in different situations. However, the same profile doesn’t work best in every situation.

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A New Chapter In The Analytics Journey

By Bill Franks, Apr 13, 2017

As I write this, I am finishing a major leg of my personal analytics journey. As many readers are likely aware, I will be leaving Teradata this month. I had a terrific 14-year run with Teradata where I made a lot of friends, worked with some amazing clients, and got to witness firsthand how the world’s largest organizations have dealt with the rise of big data and analytics. Teradata treated me well and I like to think that I, in turn, contributed a lot to the company. It wasn’t an easy decision to leave, but I came across a great opportunity and every good run has to end at some point.

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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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Last month, I wrote about why simply making predictions isn’t enough to drive value with analytics. I made the case that behind stories of failed analytic initiatives, there is often a lack of action to take the predictions and turn them into something valuable. It ends up that identifying and then taking the right action often leads to additional requirements for even more complex analyses beyond the initial effort to get to the predictions! Let’s explore what that means.

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Why Predictions Are Not Enough

By Bill Franks, Jan 12, 2017

Almost by definition, advanced analytics or data science initiatives involve applying some type of algorithm to data in order to find patterns. These algorithms are typically then used to generate one or more of the following: Predictions about future events. For example, who is most likely to respond to a given offer? Forecasts of future results. For example, what sales can we expect from the upcoming promotion? Simulations of various scenarios. For example, what will happen if I shift some of my budget from paid search to television advertising?

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