International Institute for Analytics

Inquiry:

As we work towards our goal of deploying more machine learning (ML) models into production, how do we position our MLOps needs to the organization? What needs to happen for MLOps to be successful?

Inquiry:

Our data science team works alongside a strict Agile data engineering team and we’re finding collaboration to be difficult. Do you have any suggestions?

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We plan to transition to a data lake to generate future business value by enhancing analytics and reporting. Unfortunately, we’re struggling to see how to get this done and the end value.

Inquiry:

We’re finally at a point where we have data scientists within the sales organization who can work on advanced analytics. However, we’re struggling with how to get the business knowledge to the data scientists and move forward. How have you solved this dilemma?

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We are working on a data consolidation project in which we’re replacing four old ERP systems with a new ERP system. We’re concerned with managing customer data from the different systems and prepping ourselves for advanced analytics in the future.

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We’re working on a new centralized data platform so that we can perform effective analytics with a quicker turnaround. We’d like to verify some aspects of a new architecture that we’re considering. Can you provide feedback?

Inquiry:

We’re trying to bridge the gap between IT and analytics. The analytics team can complete small-scale projects, but we need the IT team for bigger efforts. Unfortunately, our IT team doesn’t have the resources to support analytics yet. How can we work around this limitation?

Inquiry:

We have a diverse analytics ecosystem across the enterprise with people using many different tools at various levels of competency. We want to provide more access to data in a self-service capacity, but in a targeted way. How do we decide who to target first?

Each year, the International Institute for Analytics ends the year with a look at the latest analytics trends and the most pressing analytics challenges currently facing organizations. Our predictions are based upon our day-to-day work supporting and advising analytics leaders and organizations. We take advantage of the breadth of expertise and cross-industry perspectives we encounter every day from our clients, partners, and members of the IIA expert network. This is our 10th annual look toward the upcoming year, and our annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular content of the year. This year, we’ve stuck with our approach of augmenting each of our predictions with a specific priority for leaders to focus on as they attempt to address that prediction. As a result, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

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.