International Institute for Analytics

Project prioritization is one of those activities that seems simple and straightforward on the surface, but scratch at it just a little and hidden complexities are quickly revealed. While there is general agreement that “good” prioritization contributes to the overall effectiveness of an analytics function, rarely is any effort taken to define “good” and map out an agreed-upon approach to get there. The obvious outcome of a prioritization process that lacks intentionality is general chaos — analytics teams are overwhelmed and expressing a need for more resources, business leaders are frustrated that their needs aren’t being met, the loudest voice often gets his or her way, and the enterprise isn’t optimizing the return on its investment in analytical talent.

Procurement Integrity (PI) represents a broader problem and bigger opportunity than most businesses recognize. Comprehensive PI programs continuously validate purchasing transactions, using data and analytics to trace patterns, spot anomalies, and reduce fraud, waste, and abuse. The problems uncovered range from occasional opportunistic fraud to ongoing organized fraud, from duplicate invoices and other improper payments to regular kickbacks, from conflicts of interest to ongoing collusion with suppliers. Continuous monitoring of anomalies in procurement and supplier due diligence processes reveal potential problems, including data issues and process breaches, and help focus the efforts of audit and other investigative staff.

This is our 11th annual look toward the upcoming year. We are pleased that our annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular pieces of content of the year. This year, we’re once again augmenting each of our predictions with a specific priority for leaders to focus on as they attempt to address each prediction. As a result, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

From its early days in the 1970s as a pioneering discount brokerage, Charles Schwab has grown into one of the largest asset managers in the world, with over $6 trillion in client assets and 28 million brokerage accounts. A market innovator from the start, Schwab credits its continued success to its relentless commitment “to put the client first” and “to champion every client’s goals with passion and integrity.”

Cleveland Clinic is one of the nation’s premier health care institutions. With over 66,000 employees, including over 4,300 physicians and scientists, it operates 18 hospitals in Ohio, Florida and Nevada, as well as Abu Dhabi, China and U.K. Annual operating revenue is about $10 billion, and operating margin is 2.5%. As is common across the industry, financial performance is very volume sensitive, and in early 2020, patient and service volumes were in for a shock.

An analytics capability road map (ACR) defines the development of competencies and technologies over time to address known and expected business needs in a future state.

Ally Bank was created in 2009 when GMAC Bank transformed into a new company based on three simple principles: do right, talk straight and be obviously better. Ally has grown to offer a product portfolio that includes mortgage lending, investment management (both managed and self-directed), microlending, and other banking services.

As with many terms in the analytics space, “self-service” tends to have many meanings, depending on the vendor using the term. Self-service is used to describe both business intelligence and (advanced) analytics, and is frequently co-mingled with a number of other terms, including “data democratization,” “citizen data scientist” and, more recently, “data literacy.”

In general, this collection of terms points in the direction of a conscious strategy to have more employees in an organization, with broader and deeper access to data, use those data sets to make better, more timely data-driven decisions with little or no intervention from a centralized BI or analytics function, or IT professionals.

Fusion centers have evolved far beyond their original role in counter-terrorism to safeguarding and serving the public on many fronts. They continue to evolve rapidly as they opportunistically and, in the COVID-19 pandemic, necessarily make their operations more virtual. The core challenges are also changing, but they still revolve around the three imperatives of integrating data, deploying technology, and building trust. Three experts – Juan Colon, National Director of Opioids and Illegal Drug Solutions, SAS Institute, John Gillon, Industry Expert, Public Security, SAS Institute, and Gretchen Stewart, Public Sector Data Science Director, Intel Corporation – share their experience and perspectives on how fusion centers succeed in their missions today and tomorrow.

There is a tendency to jump into something called a “data strategy” without understanding what it is at its core. In IIA’s view, at the center of a data strategy is a schematic of an entity’s information economy1 with in-depth awareness of the constituent needs on the demand side of the economy — the data consumers. Because ultimately the primary purpose of a data strategy is to solve questions about how to improve the availability, timeliness and quality of data, in that order of priority, for the constituencies demanding it.