Analytics in Pursuit of Quality

By Anne Milley, Jul 17, 2013

Quality in many forms is something we all seek—quality products and services (whether we are offering them or consuming them), quality experiences, quality time, and quality of life.  To achieve any of these in any measure we have to make decisions, and for those to be good decisions we need good information.  What makes “good” information?  Quality data and quality processes.

Quality and reliability engineers, six sigma and other advocates for quality tend to take a process view of the world.  Quality improvement is often described as a systematic approach to continuous learning to make things better.  Quality improvement employs a variety of analyses to help better understand processes, improve them, as well as to make the output of those processes better.

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The Language of Science

By Anne Milley, May 23, 2013

The first time I heard statistics described as “the language of science” was many years ago in a conversation with, author of The Lady Tasting Tea and first statistician Pfizer ever hired. To expand on that, to be more scientific in any decisions you make — in science, in industry, in government — you will need statistics! And statistics needs you! Robert Tibshirani, eminent statistician at Stanford University, is quoted in The New York Times Bits blog last year: “Statistics is unusual.  … It’s a service field to other disciplines. It doesn’t rely on its own work. It needs others.”

It wasn’t too long ago that universities required you to meet foreign language requirements, especially for graduate degrees in the sciences. It now appears that a new language — statistics — is working its way in to the curricula for degrees in science as well as business. Recently, a proposal was made to establish a statistics curriculum within the chemistry departments of US colleges and universities. In addition to the evolving curricula in business schools to include more statistics, data mining, predictive analytics (and offering new degrees in these areas), even the hard sciences are incorporating more statistics to better prepare their graduates for jobs in industry.

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The Well-Appointed Analytic Workbench

By Anne Milley, Apr 23, 2013

What do I mean by “analytic workbench?”  Basically, the compute-resource environment with which data analysis takes place.  How would you describe some of the analytic workbenches in your organization? Not everyone is a power analyst, so not everyone requires power tools. But all of us deal with data at some level, and all of us can benefit from making sense of our data more efficiently and effectively. See if there are opportunities to make the analytic workbenches in your organization more productive.

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Analytical Cultures:  How to Nurture Yours

By Anne Milley, Mar 21, 2013

One of the biggest mistakes organizations make that prevent an analytic culture from taking hold is not investing in ongoing education/training. Early in my career at a large retailer, I asked to take a SAS class that I would pay for myself. I’ll never forget the response to my request: “Well, we can’t afford to let you take the time.’ So, I learned from the software manuals what I needed to know, but could probably have learned a lot more a lot faster in a structured hands-on class. When an organization pays (with time and/or money) for an employee’s training it sends a signal that the organization values that employee. It shows that the employer wants to invest in sharpening the employee’s mind so that they can be prepared to better solve new problems that come their way.

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Getting to know the data is key to gaining insight and realizing value. More dynamic and interactive graphics greatly facilitate the ability to more quickly explore and understand the data’s structure, patterns, relationships, trends, and anomalies, which is even more important in this era of big data. By marrying the power of high-speed computation and displays to render such dynamic and interactive graphics with our high-speed visual bandwidth to navigate them, we gain several benefits.

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Get Ready to Celebrate!

By Anne Milley, Dec 19, 2012

As we wind down in 2012 and reflect on the past, we also want to look ahead. Next year will be an even bigger year for analytics. 2013 is the first International Year of Statistics, a worldwide celebration of statistics.

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2013 Predictions for Analytics

By IIA Faculty, Dec 11, 2012

Available to ERS Clients only

This powerpoint presentation is the companion to the 2013 Predictions for Analytics webinar featuring IIA Research Director Tom Davenport and IIA’s world-renowned faculty group. This presentation reviews IIA’s predictions from 2012 and expectations for 2013.

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As someone who studied economics, I came to appreciate the amount of information that can be nicely distilled into a well-chosen graph — relationships with price and quantity, changes in supply and demand over time, efficient frontiers, and more. With so much data and so much potential value to be gained from data, it is good to see that we are poised to take greater advantage of our amazing visual bandwidth through more graphic encoding of data. The trend of greater “visual literacy” or graphicacy is apparent.

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Analytics in the Enterprise: Symposium Review

By Robert Morison, Anne Milley, Jul 10, 2012

Available to ERS Clients only

At the recent Analytics Executive Symposium, speakers and faculty members termed this “the era of analytics.” In this era, analytical leaders will help business executives make better decisions that improve their organization’s bottom line. Analytical leaders will focus on projects with measurable value, help their organizations become more analytically mature (by following frameworks such as the Analytical Maturity Model and the DELTA Model), and develop analytical teams with rich analytical talent. Analytical leaders will also lead the way by building robust analytical infrastructures for their organizations while also developing critical soft skills that help them advance in their careers.

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Big Data Déjà Vu

By Anne Milley, Apr 19, 2012

More than a decade ago when data mining was relatively new, many were advocating to mine all of the data, and had to be educated on the concept of sampling, which is still considered a best practice.

While it is certainly a good thing that we can do more and do it faster than we could before on more data, it’s worth a moment to revisit some basics.

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