Some Questions I’m Frequently Asked

Many of my contacts are former colleagues or current business partners but some are not. Following the suggestion of one contact, I decided to put together this brief post describing my company’s services and my work arrangement.  

I’m a statistician and data scientist working in marketing research and other business analytics areas. Data and analytics are hot now but were little talked about just a few years ago, and much confusion remains regarding statistics, data science and even data.

I have written a number of short articles I hope will demystify these topics. Most business people have taken Stats 101 in college but have had no education in statistics beyond that and, given all the big data hype in recent years, confusion is to be expected! 

For background, I work by myself from my home office and established my company, Cannon Gray LLC, in January 2008. I sometimes work on retainer but am completely free-lance. There are no potential competitive conflicts I am aware of that should be of concern to any party.

My work is global and not tied to any one country and nearly all conducted remote by email, phone, Skype or Zoom. I have extensive experience working remote and working internationally dating back to my roles as a marketing scientist with Nielsen Customized and Research International (Kantar). It wasn’t easy at first, particularly with the technology we had to struggle with at the time, but I’ve learned how to make it work. It’s a highly efficient arrangement.

There is almost never any need for me to be physically present at client meetings and, if necessary, I can be patched into meetings or even presentations. That is seldom needed, however, since part of my job is coaching on how to respond to queries from clients regarding methodology and how to interpret the results of my analyses. (Hint: Don’t sell methodology or statistical techniques!)

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With rare exceptions, I cost per project and my fee is provided up front. In some cases, I will provide several methodological options with different fees for each. There are also multi-phase projects requiring separate costings for each phase. For example, Phase I may be exploratory data analysis, while Phase II consists of Option A and Option B if Phase I suggests there would be value in developing a predictive model or a deeper analysis. 

I do all my own work and do not outsource to a “delivery center.” That sort of arrangement would slow me down and increase my costs. All my work is customized to individual client needs and I do not have a one-size-fits-all canned product I sell. Because of this, there aren’t that many statisticians or data scientists with the necessary depth and breadth of experience who would be available for outsourcing. The Law of Diminishing Returns does not apply to professions such as mine, and I am now a much better researcher and analyst after 30 years’ experience than I was after 20 years’ experience!

Because my work is customized, I normally ask many questions at the proposal or discussion stage regarding the raison d’être of the analytics or research, such as who will be using the results, and how and when they’ll be used. I also ask for information regarding the end-client’s business and other necessary background that I cannot find through their home page and simple searches.

An important part of my career path over the years has been learning what questions to ask and how to ask them. How vital is sector knowledge and local country experience to an analytics professional? I’ve worked in scores of sectors for clients located in scores of countries for more than two decades and, in my experience, they are important. Just as critical, however, is knowing which questions to ask, how to ask them, how to interpret the answers to these questions and how to act on these answers.

There have been times when I’ve been given data and asked to “find something,” which can be an uncomfortable (though not uncommon) position for a statistician. Which Comes First, the Data or the Egg, which looks into this topic, may be of interest to you.

Analytics is not just plugging numbers into formulas or computer algorithms, and the experience and judgment of the analyst plays a crucial role in all but the simplest and most mechanical of analyses. It is much broader than data mining and predictive analytics. Very often I am involved in research design, and Preaching About Primary Research offers some thoughts regarding the importance of primary research skills. Automating My Job explains why I have failed at automating my own job!

My simple DIY homepage provides more background and information about me and my company, as does my LinkedIn profile. I’ve given some background on my transition from the corporate world in Doing Your Homework: Tips on Telecommuting and How to be a Home MonkeyLife as a Subcontractor: A Few Tips answers some questions I do not have space for in this post.

 

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