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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Inquiry Response: Thoughts on Analytics Maturity in the New World of AI

By IIA Expert, May 27, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

My organization sits at the upper end of the analytics maturity curve: we’re doing predictive and prescriptive work, particularly with image recognition technology. I’d like to have a better sense of how we compare to others, especially now that approaches have become more advanced across the board.

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Portland 2019 Analytics Symposium Video: Mark Madsen

By Mark Madsen, Apr 17, 2019

Available to Research & Advisory Network Clients Only

The Black Box: Interpretability, Reproducibility, and Responsibility

Historically, a model produced a result that was interpreted by a person who made a decision. In recent years, as the amount of data and number of decisions have grown, agency has been taken from humans and given to machines, which make decisions in a black box. Black boxes raise issues around explainability (or interpretability)—being able to explain how a decision was made—and reproducibility —being able to use the same data and model to make an identical decision.

The reality is that being able to explain complex decisions is extremely difficult, and may not be necessary. And, being able to reproduce decisions is also very challenging, as data, tools, software, models, and environments change. Any single change can have a ripple effect that changes everything. The real issues are trust, reliability, and repeatability, particularly in high-stakes decisions. Building trust starts with IT policies, governance, and infrastructure, to enable preserving history and allow for understanding and reproducing decisions. This is the key to gaining trust and scaling analytics.

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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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Building The Analytics Factory at Deere

As a 180-year-old company with 65,000 employees in 30 countries, Deere is the stark opposite of a digital native. Incorporating analytics into different parts of the company has required significant transformation on both the technical and people sides. But all changes have been grounded in the company’s foundational values.

Transformation has required partnerships between the analytics function and other stakeholders, including IT, manufacturing, sales, legal, and more. Partnerships and flexibility have been necessary in reworking traditional processes to become faster and more iterative, and in revising governance and decision making.

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Portland 2019 Analytics Symposium Video: Jesse Anderson

By Jesse Anderson, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Creating A Data Engineering Culture

Data scientists get the glory, but when they experience success it is due to a data engineering culture. In a data engineering culture, the value and importance of data engineering are recognized throughout the organization.

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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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