International Institute for Analytics

We co-founded the International Institute for Analytics in 2010. Since it’s now 2020, our sophisticated math skills tell us that IIA has been around for about a decade—although our first full year of operation was in 2011. We thought it might be interesting to reflect on the state of the field that IIA addresses and how it has changed over time.

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.

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.

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.

Inquiry:

We’ve created a data science institute and one of our mandates is community outreach through: local universities, community partners, and K-12 programming. Do you have any tips for engaging the broader community?

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.

We’ve had technical people focused on the ingestion and management of data for decades. But, only recently has data engineering become a critical, widespread role. Why is that? This post will outline a somewhat contrarian view as to why data engineering has become a critical function and how we might expect the role to evolve over time.

Why do some analytics projects succeed while so many fail? According to Gartner analyst Nick Heudecker, as many as 85% of big data projects fail. However, the ROI from the other 15% that succeed is incredibly promising. With such a clearly high barrier to competency in executing big data strategies, there remains significant opportunity for first-mover advantage for enterprises that can crack the code to improving their outcomes.

So, what can organizations do to increase their chances of big data success? Part of the answer lies in creating a data engineering culture. This is the necessary foundation underpinning a big data analytics proficiency and enables companies to outperform the competition.

Analytics experts love data. But just presenting raw data or even insights derived from data isn’t good enough. To create business value from data requires that analytics professionals develop skills at data storytelling. This entails telling persuasive stories, tailored to a specific audience, that combine data, narrative, and visuals effectively.

Why Storytelling?

Human beings love stories. In fact, author Philip Pullman has written, “After nourishment, shelter, and companionship, stories are the thing we need the most in the world.” And scriptwriting expert Robert McKee has said, “Storytelling is the most powerful way to put ideas into the world today.”