International Institute for Analytics

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.

Inquiry:

Leadership is disheartened because they don’t see enough movement with advanced analytics and artificial intelligence (AI), and we’ve been mandated to set up an AI COE. The problem is that their examples of AI are really examples of automation. There’s a gap between technology awareness and reality. How do we work around this?

Technologically, law enforcement is an exciting field these days. Vast new sources of electronic data and advanced analytical methods offer opportunities not only to resolve individual investigations in record time, but also to discover patterns of activity to exploit in crime prevention. To seize these opportunities, many agencies are modernizing their information and analytics platforms. To explore the pragmatic challenges and potential benefits of modernization, IIA spoke with Steve Shirley, Head of Customer Advisory for the SAS Justice and Public Safety Team, and Captain Steve Serrao, Senior Customer Advisor for the SAS Justice and Public Safety Team.

Optum has launched a number of initiatives to boost analytics fluency, especially among its business leaders and team members. The goal is to equip individuals in business units, operations and other key parts of Optum with the knowledge and skills needed to effectively engage, employ and capitalize on analytics. While our efforts are a work in progress, we view analytics fluency as a critical prerequisite to “competing on analytics” and key to our mission of transforming health care. The next sections provide an overview of Optum and the challenges we’re addressing in health care, while subsequent sections describe the motivation for and our experience with fluency-building initiatives to date.

GE aspires to be an algorithmic business, but recognizes this transition will not occur overnight. It will occur in stages as the company develops new capabilities and implements multiple emerging technologies. This transition requires building solid foundational systems and encouraging broad experimentation and innovation using new analytics technologies.

Beyond getting experience with next-generation technologies, transitioning to an algorithmic business requires cultivating an enterprise-wide data culture and changing how people work throughout the company, particularly on the front line.