Steve Miller, co-founder and president of OpenBI (www.OpenBI.com) wrote a blog post for Information-management.com on April 19, 2010, titled “BI, Analytics and Statistical Science“. He writes that “I think the list provides a foundation of what it takes to succeed in BI” (Business Intelligence). Steve holds degrees in Quantitative Methods and Statistics from Johns Hopkins, the University of Wisconsin, and the University of Illinois, and he acknowledges that his list is statistics/research-centric. I my view, that is not a disadvantage.
I think the list is also very valid for somebody working in an analyst function within for example competitive intelligence or governmental open source intelligence analysis:
General Skills
- 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.
Technical Skills
- 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.