International Institute for Analytics

Last week I had the pleasure of meeting with two promising new(ish) data analytics companies that are worth exploring: NGDATA and Podium Data. Both have established and tested products, clear value propositions, and a strong list of initial customers.

There is a lot of serious talk in America these days about improving the state of our manufacturing sector. Smart products, Internet of things, robotics, predictive maintenance—all great stuff. But none of it addresses the most fundamental challenge facing the sector: how to deal with a demand/supply inversion which has made the customer king.

Last July, I suggested that well-established companies could no longer sustain competitive advantage through using their size and market reach as barriers to entry. The unprecedented assault of new waves of digital disruption have enabled companies of any size to penetrate some portion of well-established companies’ value chains. Taken together these disruptions are making what was scarce and expensive now ubiquitous and cheap as the chart below illustrates. Simply put, companies must now find totally new ways to compete or risk a major wipeout.

Inquiry:

We would like to create a rotational training program for our in-house analytics professionals. New hires would rotate through various other business units to gain a broader view of the overall business, while bringing the data science perspective into those units.

Questions:

• Would a rotational program like this appeal to recent analytics and data science graduates?
• How does a rotational training program for analytics professionals benefit business?
• What are some best practices for rolling out a successful rotational training program for analytics professionals?

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.

Inquiry:

I’m interested to discuss methods/options for scaling analytics across our highly matrixed organization. We have done a lot of work building out our analytics strategy, but now need to get more tactical. How can we make this transition while maintaining the current systems, building future systems and constructing a bridge between the two?

I attended the MIT Disruption Timeline Conference on AI and Machine Learning. There was interesting content on a variety of topics, but a primary focus was on when specific AI capabilities might become generally available. One particular technology addressed was autonomous vehicles. The key question was when 50 percent of vehicles on US roads would be fully autonomous.

Inquiry:

How do we assess the readiness of the talent in the organization for our future analytics capability needs?

Inquiry:

We want to improve our analytics capabilities across the organization, but need better ways to justify continued analytics expansion to the enterprise. What are some ways to begin to get buy-in from leadership for our analytics initiatives?

As the leader of a world-class service organization, I’m on the road a lot meeting with research clients and prospective customers alike. Each week I try to meet or speak with at least one team at IIA’s ever-growing group of high-performing research clients. I thought it was time to start sharing some field notes of what I’m seeing and hearing. Last week I finished a trip to New Zealand and Australia, visiting with 10 companies from four distinct sectors in three major cities.