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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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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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

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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Inquiry Response: Where Machine Learning Can Help with Decision Making

By Blake Johnson, Jun 10, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

What are use cases where machine learning (ML) delivers the best decisions and performance? Vendors are peddling their ML solutions, and because it’s sexy right now, they’re gaining traction with executives being told outside ML algorithms will outperform on every data problem.

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Portland 2019 Analytics Symposium Video: Marc Demarest

By Marc Demarest, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Information Economy Mapping

Every organization has a naturally occurring information economy. The rules of other economies hold: there is supply and demand, supplier and buyer power, competitive alternatives, infrastructure, regulation, taxation, and more. Two important rules of thumb: demand always finds a way to get its needs met, and there are legitimate, necessary restrictions on freedom.

Roughly 80% of organizations have a Soviet-style, state-controlled information economy. In the other 20% it is a laissez-faire, demand-style information economy. In every organization it is important to know where you are and where you want to go. The right answer is always something other than a command economy or an unfettered laissez faire economy. It is analytics professionals’ job to figure out the optimal state by balancing those requirements and brokering solutions that are palatable to all.

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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: Ideas for Improving Forecast Accuracy

By IIA Expert, Mar 18, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We want to dive deeper into our forecasting processes, looking at both the demand side and the supply side. We have a standard forecasting system coming out of sales and operation planning, but is there more we could be doing to increase our accuracy?

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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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