Research

Humans tend to rely on available information while completing complex tasks. But what happens when information is presented by human and automated sources? And what happens if those information sources conflict?

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Much of the business world still operates using a “gut-based” approach rather than the “data-driven” approach to problem solving. But why, in this day and age, is this still the case? Let me offer three reasons.

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Approach Big Data Analytics Like A Lego Kit

By Bill Franks, Apr 14, 2016

When it comes to Lego’s pre-packaged building kits, there is one aspect that ties closely to the needs of big data analytics. Namely, Lego provides consumers kits that use a combination of both custom pieces and standard pieces to create awesome models which children (and some adults!) can’t wait to get their hands on.

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Business Analytics: An Overrated Sham

By Gary Cokins, Apr 01, 2016

Gary Cokins offers his hot take on business analytics and big data, with an unexpected twist at the end.

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Analytics Journal: Privacy and the Customer Contract

By Daniel Magestro, Mar 22, 2016

The capture and usage of customer data are front of mind for European analytics leaders in many industries. The “customer contract” goes beyond privacy laws that govern the use of customer data to include the implied expectation that their data will be used to their benefit.

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Employee Engagement is a Tricky Predictive Metric

By Greta Roberts, Mar 15, 2016

In my day job, my work focuses on using predictive analytics to decrease employee turnover or increase employee performance. One topic that frequently comes up is employee engagement data, and whether it is meaningful to the analysis. There can often be years of employee engagement data, and the data is typically in HR’s control.

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One of the biggest hurdles faced in trying to purse the AoT is actually just a new iteration of a common, recurring problem that has vexed analytics professionals for years. Namely, we can’t analyze anything related to IoT until the infrastructure investment is made to create, acquire, and make available the data necessary for analysis.

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One of the most common questions I get from analytics leaders is “How do I create an analytical culture?” This is usually voiced by leaders whose firms are starting out on their analytical journeys. There are a few moving pieces here. Bear with me as I unpack them.

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Transparency is the new Sustainability

By Robert Handfield, Feb 23, 2016

Sustainability is less about multi-tier audits – it is more about knowing who you are buying from, and the implications of your behavior when you choose to purchase a product or service from someone.

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Business First, Analytics Second?

By Thomas H. Davenport, Feb 18, 2016

You may feel that “business first” is an obvious approach to take with analytics, but I assure you that it is anything but ubiquitous. It means that business objectives drive the business domain to which analytics are applied (what I have usually called “targets”), there are business objectives in place before the analytics are generated, and business considerations constrain the time and expense that are devoted to the analytical exercise. That may sound less fun than analysts running wild in an analytical sandbox, but it is generally the most effective and efficient approach to analytics.

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