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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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: Pricing Analytics Function in Retail Organizations

By Mike Gamage, Jul 01, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

What’s the right approach to pricing as an analytic function within a large retail organization? We refresh our elasticity models every few years, but is that enough?

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Portland 2019 Analytics Symposium Video: Matt Levinson

By Matt Levinson, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Nike Gets Up and Running With Machine Learning and AI

Embarking on an AI journey starts with executive leadership and strategic vision. It requires alignment of the culture and capabilities. At Nike, the key elements have been business leaders wanting to be data driven, demanding deeper information, and being committed to enabling the organization.

The first step in getting up and running at Nike was unification of data science activities. For example, previous efforts were in silos by product group, such as a running app. This resulted in consumers having multiple Nike digital IDs. Having one ID per person was essential. Also important was unification of reporting so everyone at Nike was looking at the same numbers.

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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: Developing a Global Insights Team

By IIA Expert, Feb 25, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

Recently some of our large global franchisees have reached out to headquarters for help getting their analytics organizations off the ground. The challenge is that these franchisees are independent companies with their own C-suites, so from a reporting structure they don’t work for us or have to listen to us. Do you have an advice for how to guide them?

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This article describes the potential for AI to augment risk estimation for both individual investors and financial market assets. AI processes vast amounts of a variety of data to identify patterns underpinning processes and metrics. Evolving data resources including digital touch points provide AI with attributes that can enhance risk estimation to ultimately augment elements of modern portfolio theory.

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Operational Excellence is the New Customer Intimacy

By Geoffrey Moore, Jul 24, 2018

Michael Treacy and Fred Wiersema argue that market leaders achieve competitive advantage by excelling in one of three value creation disciplines: product leadership, customer intimacy, or operational excellence. This blog discusses how digital transformation is rocking this institution and allowing operational excellence to trump customer intimacy.

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Predictive Analytics World 2018

By Bill Franks, Jun 26, 2018

Available to Research & Advisory Network Clients Only

IIA attended the Predictive Analytics World (PAW) show in Las Vegas, June 6–7, 2018. This year the show had a new format called “Mega PAW” where a number of (previously individual) PAW shows were all housed together under one roof. Some of the keynotes were shared by all attendees, while the breakouts were segmented by topic. All of the sessions IIA attended were in the business track. In this event summary, the key points from each session attended will be provided, as will some commentary on IIA’s thoughts about the topic.

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