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.

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.

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.

Inquiry:

Is there a way to tie ROI for Tableau dashboard sales/marketing reporting to incremental revenue? I’m trying to gain additional funding from the enterprise so we can drive advanced analytics beyond descriptive. We use an AWS data mart and the cloud.

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.

Inquiry:

We have a strong data structure and high analytics demand. Unfortunately, the business most often comes to us for help with hypothesis confirmation for decisions they’ve already made. How can we inspire them to improve their decision-making processes by engaging with the analytics capabilities we can provide?

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.”

Southwest Airlines recently launched an Enterprise Data Science Center with the objectives of expanding data science capability, deploying it broadly across the company, and creating competitive advantage. Design of the Center relied upon a series of strategic and tactical conversations with IIA Experts on analytics organization structures. Today, Southwest’s Center features disciplined delivery processes performed by data scientists in clearly defined roles who engage with the business in flexible ways. IIA’s Robert Morison collaborated with Southwest’s Justin Bundick, Director of the Enterprise Data Science Center, to capture the story.

Sanford Health is a major health system with over 49,000 employees, 187,000 health plan members, and $6.1B in annual revenue. The organization has been pursuing a growth strategy for the last two decades. Milestones include: merging with a series of regional health providers, incorporating the North Dakota Public Employees Retirement System (NDPRS) members into its health plan, and most recently the 2019 merger with Good Samaritan Society, the largest not-for-profit provider of senior housing and services in the United States.