A few months ago, I received an email from a young ex-colleague asking if I'd write a letter of recommendation for him as part of a grad school application. Remembering an excellent consultant, I immediately agreed. That he was applying to an MS program in statistical science made my task even easier. I didn't ask, but would like to think my time as mentor influenced his path at least a little.
Before I crafted the recommendation, I took a look at the web site of his highly-rated program to see what he was signing up for, and how it differed from my experience 30 years ago. The prospective program curricula was not unlike mine back in the day, but had an additional emphasis on computation and simulation, obviously reflecting the huge developments in statistical computing over the years.
The program page also had some nice bullet-points on the skills that made for good “statistical scientists”. While business intelligence and analytics are not the same as statistics, there are areas of overlap. And I've decided based on frustrations with my college recruiting trips last Fall to update the job description for BI consultants I send to schools where OpenBI conducts interviews. So I “borrowed” a few of the observations on the program web page, changing statistics to BI/analytics, adding and subtracting points I felt appropriate.
What I ended up with is a first pass of BI recruiting talking points for technical BS and MS students. I certainly don't expect 22-24 year olds to check off all bullets, but think the list provides a foundation of what it takes to succeed in BI, and hopefully communicates that effectively to candidates. I'll use these to update the OpenBI job descriptions on school recruiting sites in coming months. The expectation is that candidates would bring many of the noted skills to the job immediately, developing most of the remainder in the first 3-4 years of their consulting careers.
Acknowledging that my list is statistics/research-centric and that OpenBI is a professional services firm rather than a corporation, I'd be interested in reactions from readers contrasting these with their organizations' BI/Analytics skill reqs.
- Strong interpersonal and communication skills,
- Customer-facing personality,
- Ability to work productively as an individual or in collaboration with others,
- Ability to write/communicate clearly, accurately, and effectively,
- Ability to think analytically,
- Data centricity – obsession with evidence-based problem resolution,
- An understanding of the scientific method – theory, hypotheses, testing and learning,
- Ability to use the scientific method to conceptualize business problems,
- Orientation to business and one or more business processes – either vertical or horizontal,
- Commitment to life-long learning.
- Intermediate programming and computation skills,
- Facility with logical and physical relational databases (SQL),
- Understanding of the economic approach – “the allocation of scarce means to satisfy competing ends” – to problem solving,
- Facility with standard statistical/BI packages to perform analytic calculations,
- Ability to interpret the the results obtained from these packages,
- Facility with a variety of graphical/visualization techniques for exploring and presenting analytic data,
- Understanding of the principles of management, accounting, finance and marketing,
- Understanding of the meaning of business optimization,
- Ability to recognize the nature of, and to model, the random variation underlying given business data,
- Understanding the nature of statistical inference – its scope, limitations and proper role in the process of business analytical investigation,
- Ability to express a generally-posed business problem in a statistical context; ability to translate business concepts for measurement.
- Understanding how to obtain a suitable sample from a population and how to make inferences from that sample,
- Understanding of experimental and quasi-experimental designs for BI,
- Ability to provide advice on the design of business analytic investigations,
- Understanding of a variety of commonly-used analytic techniques and the models underlying them,
- Conversance with the mathematical underpinnings of often-used analytics techniques to facilitate simple modifications in appropriate situations,
- Understanding of alternatives to traditional statistical modeling from computer and mathematical sciences,
- Comfort with Internet research,
- Obsession to stay current with the latest analytic methods/techniques.
Steve also blogs at Miller.OpenBI.com.