Posts tagged analytics teams
Spring Analytics Symposium in Review – Portland, OR 2019

Nearly 200 of IIA’s clients, analytics experts, and members of the analytics community gathered in Portland, Oregon this week for the spring Analytics Symposium. IIA also hosted its first Women in Analytics networking event, an interactive Analytics Workshop, and introduced two tracks of sessions to bring the most value to attendees. This blog covers key themes of the conference and highlights from each session.

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Three Steps for Creating the Analytics Dream Team

Building your team is hard work. Finding the right skills, the right personalities, the right types of motivation to build a team that works well together like a well-oiled machine… feels almost impossible at times. So what do you do? We at International Institute for Analytics (IIA) have been answering that question for our clients for as long as we can remember. In this blog, we are going to share three key strategies we have learned working with analytics teams of all sizes and maturity.

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Article Review: The New Job Description for Data Scientists

The New Job Description for Data Scientists, an InformationWeek article by Rich Wagner, outlines important trends about the future of data scientists and their role in analytics functions. This blog covers key takeaways from Wagner’s article and three recommendations for analytics leaders based on the trends discussed.

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Don't Mistake a Legacy Skillset for a Legacy Mindset

It goes without saying that the analytics and data science space is undergoing change at an unprecedented pace. At the same time, many organizations have a lot of people who are still working in the same way using the same skills they have had for years. Organizations certainly need to move beyond those legacy skillsets and the need for a skills update is real. However, I see many organizations assuming that those with legacy skillsets are no longer valuable and must be replaced. This is a mistake. There is a huge difference between a legacy skillset and a legacy mindset. The two are not the same!

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Do You Need a Ph.D. to Run Analytics or Data Science?

While we are supportive of companies’ efforts to hire quantitative Ph.D.’s to practice data science, we believe that most firms are better off hiring people with other types of training and general management skills to manage analytics and data science groups. Why? Because there are a series of traits that make for effective managers of such groups, and most Ph.D.’s don’t tend to have them. We describe ten of those traits in this blog, and the reasons why they are unlikely to be found in the average doctoral degree holder. The list of traits may be useful for anyone seeking to hire a leader of analytics or data science functions-whether they are considering Ph.D.’s or not.

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5 Things New Analytics Leaders Should do to Succeed

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. IIA 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.

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A New Chapter in the Analytics Journey

Every individual and enterprise travels a unique journey in the pursuit of analytics. In my case I could never have predicted how my journey would unfold when I first entered the workforce over 20 years ago. The rise of analytics as a strategic imperative and the explosion of career opportunities within the field far surpass what I expected coming out of school. I feel very lucky to have had such a terrific journey so far and will be interested to look back 20 years from now and see what happens from here.

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How Analytics is Evolving Like the Medical Field

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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