International Institute for Analytics

Inquiry:

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?

Inquiry:

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.

Inquiry:

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 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.

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 vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

Information Economies in Organizations

The data warehouse revolution began in 1991 when Bill Inmon published Building the Data Warehouse. Inmon observed, early in that book, that every organization has a naturally occurring information economy, and that most naturally occurring information economies were inefficient, duplicative and prone to produce suboptimal decisions.

This observation of Inmon’s has not gotten anywhere near the credit, or attention, it deserves. A decade’s worth of collective practice in advanced analytics should tell us that everything we know about real-world economies applies to our information economies. There is demand for information by people and functions in an organization, and there is a supply of (some of) that information. There is (some amount) of technical and procedural infrastructure – some kind of market — to bring demand and supply together in an organized way. That “market” infrastructure is often partial, fragile and in some cases ineffective. There are competitive alternatives (like cloud service providers and SaaS vendors), over- and under-regulation (various data governance models), excessive demand-side taxation (cost allocation strategies), failure to invest in infrastructure, and all other elements of economies.

When organizations are planning strategy-driven large-scale advanced analytics programs, they should begin their planning by characterizing their as-is information economy.