Why Predictions Are Not Enough

By Bill Franks, Jan 12, 2017

Predictions Are The Starting Point…

Almost by definition, advanced analytics or data science initiatives involve applying some type of algorithm to data in order to find patterns. These algorithms are typically then used to generate one or more of the following:

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Over the past 30+ years, businesses have spent billions on talent assessments. Many of these are now being used to understand job candidates. Increasingly, businesses are asking how (or if) a predictive talent acquisition strategy can include the use of pre-hire assessments? As costs of failed new hires continue to rise, recruiters and hiring managers are looking for any kind of pre-hire information to increase the probability of making a great hire.

For All of the Marketing Hype, all Predictive Analytics Projects Have 3 Very Simple Steps:

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Analytics Journal: Being Smart About Agile

By Daniel Magestro, Jan 05, 2017

In the last few years I’ve observed an increase in interest and attempts to implement an “agile” methodology in business analytics projects. This interest reflects the rapid growth of agile in IT development, where TechBeacon reports that two-thirds of surveyed IT professionals are either leaning towards agile or have fully adopted agile in their companies. Less than 20% of those companies had adopted agile methods just five years ago.

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IT Organizations: The Shoemaker’s Analytical Children

By Thomas H. Davenport, Dec 20, 2016

For the great majority of years in the past decade, Chief Information Officers named “business intelligence and analytics” as their top focus in Gartner Inc. annual surveys of technology priorities. That set of technologies moved to number one in the survey in 2006 and stayed there until 2009. It fell to fifth in 2010 and 2011, but was back on top in 2012 and has stayed there ever since.

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Improve New Product Development with Predictive Analytics

By Thomas H. Davenport, Dec 13, 2016

Recently on this site, one of us wrote about the new product development analytics used by Netflix. In a nutshell, the company classified the key attributes of past and current products or services and then they modeled the relationship between those attributes and the commercial success of the offerings. This produced a predictive model that provides the company with guidance about how likely a new product or service is to be successful.

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When Big Data Can’t Predict

By Bill Franks, Dec 08, 2016

Most people think that in the age of big data, we always have more than enough information to build robust analytics. Unfortunately, this isn’t always the case. In fact, there are situations where even massive amounts of data still don’t enable even basic predictions to be made with confidence. In many cases, there isn’t much that can be done other than to recognize the facts and stick to the basics instead of getting fancy. This challenge of big data that can’t be used to predict seems like an impossible paradox at first, but let’s explore why it isn’t.

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I work with many clients who are trying to effectively adopt advanced analytics – data mining, predictive analytics, data science. One of the biggest problems these clients face is to how to get everyone – business, IT and data science professionals alike – on the same page.

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Observations from AWS re:Invent

By David Alles, Dec 06, 2016

Amazon launched Amazon Web Services (AWS) 10 years ago with a similar customer-centric, expansion minded approach to information technology. Today, there is no greater force in the technology industry than AWS. AWS will collect over $13B in revenue in 2016, a 55% increase over 2015. It contributes more than 100% of the operating profits of its parent company, as the other business units operate at a loss. Well known for providing the underlying infrastructure for digital native companies like Netflix, Uber and Airbnb, AWS is now heavily targeting the enterprise IT market – specifically Big Data, BI and analytics.

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Many times when I speak with analytics managers or business people interested in analytics, they tell me that performing some analytics on data is not the primary problem they have. “We have to get the analytics integrated with the process and the systems that support it,” they say. This issue, sometimes called “operational analytics,” is the most important factor in delivering business value from analytics. It’s also critical to delivering value from cognitive technologies – which, in my view, are just an extension of analytics anyway.

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AWS re:Invent

By Jack Phillips, Nov 29, 2016

Can’t make it to AWS Reinvent in Las Vegas this week, but want to keep up with what’s going on? A quick reminder that IIA will be covering AWS Reinvent for our research clients, as we did for O’Reilly’s Strata conference in New York earlier this year. This coverage of the major analytics technology events is a new feature of IIA’s flagship Research and Advisory Network (RAN) service in 2016 and includes access to both a written “Cliff’s Notes” summary and a hosted phone discussion around the most important trends, announcements and new technologies being showcased at each event.

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