Takeaways from the IIA Symposium

Takeaways from the IIA Symposium

Last week I attended the International Institute for Analytics (IIA) Symposium in Portland, Oregon. IIA is an independent research and advisory firm that works with organizations to improve analytics performance. The semi-annual conference brings together a community of analytics practitioners and thought leaders from across the country to share new ideas, trends, and best practices in the field of analytics.

The Symposium provided a great opportunity to network with leaders from across a variety of companies and I always learn so much! These leaders challenge me to expand my thinking and inspire me to continue learning so I can better help my clients develop their analytics talent. Below are a few of my takeaways from the event.

New Analytics Roles (Analytics Translator & Data Engineer)
I continue to observe the evolution of analytics roles as organizations mature. The positions needed for a “start-up” analytics team are quite different from those needed for a more mature team that has been around for 3-4 years. This is because the needs of analytics teams shift over time as the rest of the business increases its capabilities.

Two specific roles I heard mentioned at the conference were the “analytics translator” and “data engineer”.

• The need for an “analytics translator” role was mentioned throughout the conference. Several presentations referenced the importance of this function in the form of a business liaison/project manager on the team. Many companies are adding this role under a variety of titles such as analytics translator, analytics consultant, or analytics program manager.
• There is also a realization of the importance of having a “data engineer,” and not just a data wrangler. Jesse Anderson, Managing Director of the Big Data Institute and an expert on this topic, shared how the addition of the data engineer can increase the effectiveness of other analytics team members. For example, the data engineer ensures data is properly structured and ready for analysis which increases the efficiency of the work of the data scientist.

Retention Matters:
I found myself sitting on the edge of my seat for several presentations that highlighted the benefits of retaining and training employees. I’m passionate about retention because there is a lot of time, effort, and cost invested in finding the right talent. I believe the real value of that investment comes when companies make the long-term commitment to support their employees once they are on the team.

I appreciated the conference’s emphasis on some important retention topics: partnering with HR to align talent management processes with overall analytics initiatives; identifying the critical need to support individual development; and how creating an analytics community takes time but can be a highlight of an employee’s experience with your company.

Here were a couple of my favorite speakers:
• Zack Anderson, Chief Analytics Officer and Senior Vice President Electronic Arts (EA), Redwood, CA

Zack talked about the drastic reduction in turnover that EA has achieved in its analytics department over the past couple of years. The company has reached this mark using several of the same techniques I suggest to my clients. This starts with getting your job family matrix in place, and then helping employees create a career development plan by providing them with growth opportunities and ensuring that conversations to support these opportunities are actually happening. When the audience was asked about the number of people who currently have a career development plan, I was shocked to see that only about 30% of the audience raised their hands! I have learned that it is highly beneficial to know where your organization is headed next (or at least what options you are considering)!

• Michael Li, Founder & CEO of The Data Incubator, New York, NY

Michael talked about the need for different learning tracks for different employee groups (i.e., analyst vs. data scientist vs. data engineer). He spoke about the critical shift in skill set needed to maximize opportunities to work with big data, and to clarify the analytics team’s focus on specific skills. For example, although data analysts may benefit from a deep-learning class, data scientists can likely implement the learning more quickly and there are other skills such as data wrangling that analysts can benefit from.

Humans and AI:
I continue to be amazed at the capabilities of AI, but I was reminded, once again, how our greatest resource is our people. Although systems continue to improve and we get closer to “human-level AI,” there are still limitations in AI capabilities and probably always will be (such as bias, lack of common sense, and being able to “transfer learning”). I was shocked at some of the advances in hacking that occur with imaging, especially medical images on MRIs and X-ray machines, that can cause AI analysis to be completely wrong.

I believe the future will continue to be a world where AI enhances an employee’s abilities to do his or her job, increasing efficiencies and productivity, and in only a few cases will it actually replace specific job positions. The future of AI is about combining the use of this amazing technology with our unique human capabilities to really make a difference!

As I settle back into my routine in Des Moines, IA, I am grateful for the continuous opportunities to learn, to meet amazing experts in the field of analytics, and to continue to deliver value to my clients by helping them retain and develop their talent for years to come.

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