Masters of Business Analytics: Sponsored by SAS

From Industry-Generic to Industry-Specific Analytics

Historically, the analytical tools sold to a customer in one industry were the same tools provided to those in another—there was little or no tailoring of tools for particular industries. A chi square was a chi square, a logistic regression in retail was a logistic regression in banking. It was up to smart analysts to figure out how data from a specific industry could be analyzed in a meaningful fashion for that industry.

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From Multi-Purpose to Focused Analytics

I argued in my last post that the analytical technology environment will change dramatically over the next several years. One key change will be a shift from multi-purpose technology environments to focused applications and data.

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Needed: A New Technology Environment for Analytics

I think we’re on the verge of a dramatic change in the technology architecture for analytics, and I plan to write several posts about it. If you think about it, we’ve had virtually the same technology environment for 20 years or so. It involves such features as:

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Agile vs. Industrialized?

James Taylor (http://jtonedm.com/about/), who is passionate about automated decisionmaking, has a new blog post (http://smartdatacollective.com/jamestaylor/26598/its-time-industrialize-...) in which he argues that analytics is not a “cottage industry” and needs to focus on “industrialization.” He writes: There is a feeling that, because what analysts do is complex and hard for others to understand they should be allowed to swan around picking their own tools while being given lots of autonomy and plenty of freedom to experiment. This is, I believe, a very dangerous idea.

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Agile Analytics, Pt. 2

In this post I promised I would give some examples of “agile analytics.” One of the people who turned me onto this topic was Anne Robinson at Cisco Systems. She led a project to implement a new statistical forecasting project at Cisco (http://agendabuilder.gartner.com/bi7/WebPages/SessionDetail.aspx?EventSe...), and she used an agile approach from the beginning. The project was planned for 18 months, but it was broken up into several short-term deliverables.

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The Rise of “Agile Analytics”, Pt. 1

A fair amount has been said and written of late on “agile BI,” or the use of agile—evolutionary, with frequent, small, and iterative outputs—methods to produce Business Intelligence outputs, specifically reports. It is difficult to disagree with this concept, since BI was originally supposed to be largely about businesspeople producing their own queries and reports—presumably using agile methods. Certainly there is little doubt that most people don’t know exactly what information they want in what format until they actually see it.

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Social Media Analytics Pt. 2: Managing in the Early Days

As I mentioned in my last post, it’s really early days for social media analytics. But that doesn’t mean that organizations shouldn’t get started in this domain. Herewith, a few suggestions for how to manage this emerging set of activities:

• Companies think social media are free, but they’re not. To use these tools effectively, you need to spend some money on people and tools to manage and monitor your social media presence. If you spend nothing, you’ll get no value.

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Social Media Analytics Pt. 1: It’s Really Early Days

I’ve been doing some interviews with companies that are toying with social media analytics. The idea, of course, is to measure aspects of social media—blogs, tweets, Facebook wall-writings, and so forth—to determine what consumers think about a company or brand. There are a few available tools and services from established analytics companies like SAS, and new vendors like Radian6. I’ve made a few initial observations:

1. It’s really early days. Most companies are just getting started and are only toying with the technology and the approach.

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Putting Your Scorecard to Work, Pt. 3

This is Part 3 and the last post in a discussion of how to make corporate performance reporting more analytical. If a multivariate analytical reporting model is the ultimate in corporate performance management processes, what, then, are the stages by which organizations would achieve that goal? Of course, all such maturity models have to have five stages, so I will stick to that convention.

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Putting Your Scorecard to Work, Pt. 2

In my last post I described the need to move toward performance management that is more analytical, rather than just reporting-based. Not many firms are doing it yet. A few leading companies, however, have made moves in this direction, although their efforts still fall well short of the ideal. They have at least some level of awareness of some quantitative relationships between nonfinancial and financial performance. Examples of these firms include:

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Putting Your Scorecard to Work, Pt. 1

The process in which companies monitor and report their performance is due for an injection of analytics. Over the past 15 years, the most important innovation in this process has been the balanced scorecard, popularized by Robert Kaplan (http://en.wikipedia.org/wiki/Robert_S._Kaplan) and David Norton (http://www.juergendaum.com/news/07_18_2001.htm). This innovation consists of reporting multiple nonfinancial performance indicators along with financial performance indicators on one sheet or screen.

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There’s Still Plenty of Unexplored Analytical Real Estate

Just when I start thinking that analytics are everywhere, I realize that we are just scratching the surface of what can be done with them. Yes, many industries have considerable analytical activity under way, and more and more companies are embracing the competitive potential of analytics. But there are far more that haven’t even begun to compete analytically.

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Part 3 of 3: Some Data and Analysis on Analytical Organization

Last year, my co-author Jeanne Harris and some Accenture colleagues of hers did a survey of quantitative analysts within large organizations. This was the first of its kind, to my knowledge. The report (http://www.accenture.com/Global/Research_and_Insights/Institute-For-High...) sheds some interesting light on which organizational models for analysts are the best.

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Part 2 of 3: Five Models for Analytical Organizations

In my last post, I described some general principles for organizing quantitative analysts. In this one, I’ll describe five different organizational models; surely one of them will fit your organization.

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Part 1 of 3 : A Big-Picture Look at General Principles

I’ve found that one of the most popular topics in the management of analytics is how to organize analysts. So I’m going to do a 3 (count ‘em) part series on that topic. This post will be about general principles for organizing analytical activity. In the next post I’ll talk about alternative organizational models, and in the final one I will actually advance some data on the topic!

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The Roots of Analytical Silos

In my last post I described a variety of new analytically focused degree programs that are emerging at some universities. In this one I want to caution readers against the problem in universities that led us to the problematic analytical situation we are in today. It’s the issue of analytical specialization, and it’s endemic within most institutions of higher learning.

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The Roots of Analytical Silos

In my last post I described a variety of new analytically focused degree programs that are emerging at some universities. In this one I want to caution readers against the problem in universities that led us to the problematic analytical situation we are in today. It’s the issue of analytical specialization, and it’s endemic within most institutions of higher learning.

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The New Analytics Education Programs

You may not yet have noticed, but a flurry of new analytics education programs have arrived or are being created. The first of these was at North Carolina State, which happens to be the university where SAS started and the alma mater of its CEO Jim Goodnight—as well as many other SAS executives. Goodnight is passionate about education at every level, and he felt that some innovative new analytics offerings were needed at the graduate level. So he contributed enough money to get State’s (that’s what they call it in the Raleigh area) attention.

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Every Number Tells A Story

One of my analytical heroes is a guy named Joe Megibow. He’s the VP of Global Analytics and Optimization at Expedia. I’ve never worked with Joe, but I’ve interviewed him several times for various research projects. Every time I talk with him, I think, “This is a man who gets it.”

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The Geographical Spread of Analytics

I’ve been accumulating lots of frequent flyer points lately traveling around the world talking about analytics. In the last month I’ve been in Europe (twice), Brazil, Mexico, Canada, and various places in the US. One thing that has occurred to me as I travel around and talk to different companies is the incredible geographic variation in analytical approaches within the same company. You find some impressive analytical work going on at corporate headquarters, and then you discover that it’s not present outside the home country—or sometimes it’s vice-versa.

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