Research

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.

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Building the Analytics Organization at Michelin North America

By Robert Morison, Aug 28, 2019

Available to Research & Advisory Network Clients Only

In the context of a major corporate reorganization, the Chairman/President and leadership of Michelin North America recognized the need for an organization dedicated to analytics. Over the course of 2018, the company formed an analytics department with innovative and integrated structure, methods, and values. The department reports centrally, with members sharing identity, purpose, and key objectives. Most of the staff are embedded in the business in cross-functional, autonomous, long-lived “squads” aligned with major processes and business domains. Led by product owners and scrum masters, the squads develop and maintain analytics products rather than executing projects. Communities of practice foster experience sharing and learning across the department. The leadership team and management processes focus on enabling the squads to create business value.

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Mapping an Information Economy

By Doug Mirsky, Aug 16, 2019

Available to Research & Advisory Network Clients Only

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.

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Creating A Data Engineering Culture: What it is, why it’s important, and how, and how not, to build

By Jesse Anderson, Jul 31, 2019

Available to Research & Advisory Network Clients Only

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.

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Mastering the Art & Science of Storytelling

By Brent Dykes, Jul 26, 2019

Available to Research & Advisory Network Clients Only

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.”

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Delivering Data Science at Southwest Airlines

By Robert Morison, Justin Bundick, Jul 19, 2019

Available to Research & Advisory Network Clients Only

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.

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Portland 2019 Analytics Symposium: Event Summary

Apr 17, 2019

Available to Research & Advisory Network Clients Only

IIA’s 14th Analytics Symposium was held in Portland, Oregon, on March 12, 2019. This Symposium brought together leading analytics thinkers on the future of AI, data engineering, and analytics, along with analytics leaders from different industries, functions, and geographies to share insights and best practices.

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Nearly 200 of IIA’s clients, analytics experts, and members of the analytics community gathered in Portland, Oregon this week for the spring Analytics Symposium. IIA also hosted its first Women in Analytics networking event, an interactive Analytics Workshop, and introduced two tracks of sessions to bring the most value to attendees. This blog covers key themes of the conference and highlights from each session.

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Inquiry Response: Analytics for People Safety

By IIA Expert, Jan 21, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We’re beginning to use prescriptive analytics to help us increase people safety for our workers. What should we be looking at? Where’s the best place to start? Innovations and challenges?

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BI ≠ Analytics: Analytics Takes Flight at Honeywell Aerospace

By Abhi Seth, Jan 16, 2019

Available to Research & Advisory Network Clients Only

In fewer than two years, Honeywell Aerospace turned a functional BI competency into an enterprise business analytics team—and created a competitive advantage. This 75-person analytics team has transformed the company’s operations, developed analytics products for customers that Honeywell is monetizing, and created well over $100 million in aggregate value with an impressive ROI. Perhaps Honeywell Aerospace’s most important lesson is leveraging BI to evolve to analytics. Analytics experts know that BI is not analytics, yet many non-experts don’t realize this; they see BI and analytics as the same. Honeywell Aerospace hasn’t argued about it, but has focused on delivering value. This meant starting with BI opportunities, creating value, building trust, and then proceeding to deliver even more value by incorporating advanced analytics to deliver insight beyond the expectation.

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