International Institute for Analytics

Inquiry:

I’m leading the effort to embed analytics teams into other business functions, but I want their allegiance to be to analytics rather than the function they support. How do you create enough autonomy to ensure integration with the business units, while at the same time ensuring the teams don’t go native?

Given the potential benefits of data science and analytics to any organization and the limited talent pool of skilled resources, it’s critical to examine your interviewing process and determine whether it’s truly designed to source a top-tier data team. With the recent demand for data analytics and data scientist skills, it has become an increasingly daunting task for managers to adequately test and qualify candidates. This research brief includes a simple data exercise that can be completed by any candidate for a data-related role, regardless of experience or education levels. This test has been administered to dozens of candidates across a variety of industries.

Inquiry:

We seem to be missing the mark with our data science and analytics rotational program; we aren’t attracting as many recruits as we would like and would like to redesign our program. Right now our program lasts for three years with three 12-month rotations.

Inquiry:

What are the best practices for anonymizing workforce data such that we can share the data with analysts outside the HR organization who can help our analysts solve business problems?

Data-savvy hybrid professionals are needed because AI-based task automation can lead to the transformation of jobs in many domains, including the law. These transformed jobs encompass a new mix of tasks, requiring regular interaction with intelligent machines and the underlying data. Who will fill these transformed jobs and new jobs in the law?

During the Organizing Analytics Teams webinar, Bob Morison and Bill Franks discussed organizational models and techniques for analytics groups. Interest was high and more questions came in than we had time to cover completely. This blog covers nine of the unanswered ones.

IIA attended the Predictive Analytics World (PAW) show in Las Vegas, June 6–7, 2018. This year the show had a new format called “Mega PAW” where a number of (previously individual) PAW shows were all housed together under one roof. Some of the keynotes were shared by all attendees, while the breakouts were segmented by topic. All of the sessions IIA attended were in the business track. In this event summary, the key points from each session attended will be provided, as will some commentary on IIA’s thoughts about the topic.

It’s no surprise that many of the inquiry questions IIA receives from research clients relate to analytics and data science talent. Where should we be looking? How do we attract the candidates? How much should we be paying? And, once we have recruited the talent, how do we keep the work interesting enough so they stay around?

Reflections from Strata Data Conference 2018 and the emergence of data science engineering

The complexity of deploying advanced analytics and artificial intelligence (AI) into production is changing the nature of analytics and data science. As the size of data continues to expand and the pace of innovation accelerates, new roles are emerging. While advances in AI continued to headline the discussion at O’Reilly’s most recent Strata Data Conference (San Jose, March 5 – 8, 2018), the emergence of data science engineering as a new discipline is a clear trend and the primary focus of this brief. The objective of this report is to highlight and summarize the themes and trends supporting the emergence and importance of data science engineering. This report features a mix of unique IIA perspectives coupled with supporting thoughts from Strata presenters. This report also builds on key takeaways from our last 18 months of technology conference coverage

Analytics leaders and practitioners need to be prepared both to defend analytics and AI where appropriate, ensure that you’re not contributing to issues like how to prevent algorithmic bias, what industries would be least likely to do harm with analytics, and how to reduce the societal damage from AI.