International Institute for Analytics

Inquiry:

We have our data lake in order so that we can perform more forward-looking analytics to drive business value. Now we’re struggling with how to start and what to prioritize first—any ideas?

As internally developed analytics solutions become increasingly sophisticated, analytics teams are faced with many of the design challenges seen in commercial, analytics-driven software. After years of working with a variety of different clients on analytics-driven software products, the display of quantitative data, and dashboards, Brian O’Neill developed a set of axioms you can ask yourself to help you begin evaluating the design of analytics solutions for internal customers.

Everybody is talking about it. Mine your data for insights. Use your digital exhaust to feed machine learning programs to develop algorithms that will transform your productivity. Amazon does it. Google does it. Microsoft does it. All three offer cloud services to help you get started. Shouldn’t you be doing it too?

Inquiry:

We’re working on our career development and career paths for our data science and analytics team. We have forty people, and we’re growing. Our concerns include low attrition and the fact that not everyone wants to be a manager.

Inquiry:

We’re implementing a data governance program to support our larger analytics capabilities. What are some of the first steps to start building a data governance strategy? What “gotchas” should we keep an eye out for along the way?

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.

IIA’s David Alles made his semi-annual trek to the Strata Data Conference, and returned with insights into emerging technologies, data science techniques, case studies, and key learnings.

Is the analytics and data science market starting to settle down? Our recent analysis of job change and tenure shows an interesting new development, which certainly suggests that things are leveling out a bit.

Inquiry:

We are working on a platform to empower data scientists across the enterprise to build and manage their own machine-learning (ML) models. Do you have any ideas for how to approach this project?

In November, I gave the opening keynote at an event about the future of commerce at the Rotman School of Management in Toronto, where I shared four insights outlined in this blog and also heard a great anecdote from an audience member.