International Institute for Analytics

To set the stage, the analytics organizational structure I’m presenting below pertains to an analytics organization between 60 and 120 people; this is the size that seems to be a sweet spot for an effective and efficient team (large enough to have specialized skill sets, but small enough to effectively demonstrate the benefits of the team). Moreover, I’m presenting such an organizational design in consideration of an analytics effort at an established, traditional corporation, not a digital native. Digital natives will break down differently with more need for data science and data management. With 60 to 120 people, I prefer a centralized organization with P&L Analytics/Ad Hoc Analysis dotted-lined to their business partners.

Inquiry:

We have a data science team that supports our engineers, with the goal of using analytics to help them make engineering decisions that produce better products for the field. Do you have any insights on how we can work with engineering?

While early adopters of AI technologies are projected to share a global profit pool valued at $1 trillion, more than 95% of companies have not yet embraced AI technology to reinvent how they do business. A key reason for slow adoption is a shifting and uncertain regulatory landscape. Global, national and jurisdictional regulatory rules about data and AI are unclear and companies are rightly concerned about reputational and legal consequences associated with a possible misstep such as a privacy or security breach.

Having just come out of stealth mode in July, University of Michigan Professor, former Ford control algorithm innovator and now startup co-founder and CEO of Refraction, Matthew Johnson-Roberson has jumped into the autonomous vehicle race. But he has a particular angle that has yet to be addressed – delivery robots that can operate in rough weather, such as Michigan winters. Matthew will talk about Rev-1, his new delivery robot, and the decisions that went into defining a market opportunity that leveraged the analytics potential of what could be developed.

Many companies are dipping their toes into artificial intelligence, but only a few are attempting to put AI at the core of their strategies and business models. As with analytics, taking a leading position on AI is likely to be rewarded with competitive success. Tom will describe what companies that aspire to be “AI First” do, and how other companies can learn from their pioneering approaches.

Since the dawn of Wall Street, investors have sought an edge: a source of advantage they could leverage to beat the market. It’s the only way to win in the zero sum game of institutional investing. Today that edge comes from information found within the exabytes of data we all create every single day of our lives; something made possible by the technology boom of the 90s along with Moore’s Law of improvements in processing power and data storage. The prevailing belief is that the predictive signals buried in the “data economy” have the power to move markets. This is the age of alternative data, and investors are racing to get their hands on it. With an estimated market size of $7 billion by 2020, alternative data is transforming capital markets. Abraham will walk you through how investors are finding, leveraging, and profiting from alternative data today.

As companies reach a level of effective analytics maturity, the challenges shift from “How do we make this work?” to “What do we need to start doing now to stay ahead and prepare for tomorrow?” Join IIA’s New Executive Director of the Analytics Leadership Consortium as he leads a discussion with two of the leaders in their industries to explore their thoughts on preparing for what’s coming next.

Ford Motor Company won the Excellence in Analytics Award for its Global Supply Chain Fitness Analytics initiative that supports their Manufacturing, Purchasing, Material Planning and Logistics divisions.

Artificial intelligence has become the hottest commodity in recent years, and business, academia, and government have embraced it to propel complex use cases. As AI becomes more woven into the fabric of organizations (and its criticality increases), enterprise infrastructure becomes essential. AI is only as strong as its weakest link. The ability to build out use cases, deploy into production, scale, and secure all relies on the supporting solutions and infrastructure. There are many different decisions to make when choosing the right solutions and infrastructure: On-premises or off? GPUs or CPUs? Which storage system and framework to use? The list goes on. Drawing on real-world considerations, use cases, and solutions, Nick Curcuru discusses different decisions—and the associated considerations and best practices—you need to exercise to build and deploy a successful AI.

The lifespan of a company on the S&P 500 and Fortune 500 has plummeted from nearly 70 years to around 15. And attempts to innovate fail more than 95% of the time. But the best companies survive by balancing a portfolio of innovation approaches. Based on 10 years’ research and interviews with corporate innovation leaders around the world, this talk offers a model for managing and measuring new initiatives that is concrete enough to put to work immediately.