Research

CAO Perspectives: Ideal Analytics Organization

By Doug Hague, Nov 13, 2019

Available to Research & Advisory Network Clients Only

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.

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Detroit 2019 Analytics Symposium Video: ANNY Presentations & Awards

Nov 05, 2019

Available to Research & Advisory Network Clients Only

Ford Motor Company won the Excellence in Analytics Award for its Global Supply Chain Fitness Analytics initiative that supports their Manufacturing, Purchasing, Material Planning and Logistics divisions.

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Inquiry Response: Expanding Your Analytics Ecosystem With Community Outreach

By IIA Expert, Oct 14, 2019

Available to Research & Advisory Network Clients Only

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?

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The Fuzzy Line Between Good and Evil Data Science

By Bill Franks, Sep 12, 2019

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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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Mapping an Information Economy

By Doug Mirsky, Aug 16, 2019

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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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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.

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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

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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

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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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Sanford Health Becomes Data-Driven

By Robert Morison, Jun 26, 2019

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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.

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Not long ago, the role of Data Scientist was what most companies wanted to discuss with me in terms of roles they needed to understand and add to their organizations. Then, the role of Data Engineer became a big topic of discussion. In the past year, there has been a massive increase of attention being paid to yet another role that is still new enough that its title hasn’t been standardized. This role is referred to by a range of names from Analytics Translator, to Analytics Catalyst, to Analytics Liaison, and more.

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