Research

The Fuzzy Line Between Good and Evil Data Science

By Bill Franks, Sep 12, 2019

Available to Research & Advisory Network Clients Only

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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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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Inquiry Response: Occasion-Based Segmentation

By IIA Expert, Mar 25, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We’ve gathered occasion-based customer segmentation data through a third-party survey firm, but now what? How we can better design and use surveys in general?

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Inquiry Response: Including Customer Lifetime Value

By IIA Expert, Jan 07, 2019

Available to Research & Advisory Network Clients Only

Inquiry:

We have models to show propensity to buy, and now would like to include customer lifetime value. Do you have any suggestions on how to make this happen?

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Inquiry Response: Personalized B2B Sales Strategies

By Scott Friesen, Sep 10, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We’d like to use data to increase efficiency within our global B2B sales team, both on the internal and the external sides. We’re interested in developing a personalized contact and sales strategy, moving beyond simple segmentation.

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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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Inquiry Response: Using Social Media to Inform Customer Segmentation

By IIA Expert, Feb 19, 2018

Available to Research & Advisory Network Clients Only

Inquiry:

We’re keen to understand the needs and attitudes of our clients so we can better tailor our communications and suggest additional services. How have others used third-party data to better understand their customers?

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Disintegrating Castles & Category Kings

By Geoffrey Moore, Oct 03, 2017

The most prevalent impact of digitalization on the structure of markets has been to reduce the barriers to entry for a whole raft of established categories—as it has, for example in media, retail, consumer packaged goods, fast food, and transportation. A flood of small but numerous new entrants, individually nothing more than minor nuisances, become collectively a real presence. This shows up in market-share pie charts where the catch-all category Other is growing faster than the market as a whole. The result in each case is category fragmentation, and the big loser in each case is the currently reigning category king.

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