Tag Archives: partner

Working Together: The Analyst as a Consultant and Partner

A few weeks ago, I attended the American Statistical Association’s second annual Conference on Statistical Practice in New Orleans. While there were many fascinating presentations covering all kinds of applications of statistical methods, there were two in particular within the “Communication, Impact and Career Development” (i.e., “soft skills”) track that went hand-in-hand with each other, and that I believe contain essential information for all working analysts.

In the first talk, Todd Coffey of Seattle Genetics stressed the need for analysts and statisticians to create lasting partnerships with their clients (which, for those of us working within an organization, includes internal “customers”). His approach was straightforward: 1) help the client understand exactly what it is they want to learn; 2) drive their agenda by both answering their question and helping them achieve their goal; 3) speak to their understanding by conforming your language to theirs and avoiding statistical terminology as much as possible; 4) seal the deal by becoming a “salesperson” and demonstrating the value you are providing; and 5) go the distance by doing whatever it takes to be successful.

Dovetailing Todd’s presentation, Phil Scinto of the Lubrizol Corporation spoke about the “face” of the statistical consultant, in which he made the case that statisticians and data analysts are not always seen in a positive light (the derisive Mark Twain quote, “There are three kinds of lies: lies, damned lies, and statistics,” is still rather indicative of the opinion most people have of the profession). To construct a more positive “face”, Phil recommended that each of us form a “consistent core philosophy” that is unique to what we as analysts want to be known for. Some of the core items he mentioned should really come as no surprise: putting the customer’s needs ahead of your own, being efficient and effective, and having a positive impact.

Based on my experience, I couldn’t agree more with both Todd and Phil (as I have alluded to in a previous post). First and foremost, analysts provide a service to their clients, which Phil so eloquently defined as “[the] solving [of] important problems facing customers, organizations and society based on gleaning fundamental knowledge through the collection, analysis and unbiased dissemination of data”. As such, we should always consider our role from the standpoint of how what we do can help others succeed. As you apply Todd’s method, continually focus on the other person’s needs and objectives—assuming they are ethical!—and ensure that your efforts are aligned with them. I also advocate a follow-up meeting after completion of a project to ensure that their needs were fully met, as well as to uncover any opportunities for improvement for future projects. This will not only give you useful feedback, but will also demonstrate to the other person that you are still committed to their success.

Phil made it clear that following his advice to construct your “face” will take some work (and some soul searching) to make it your own, but the thought process can be guided by asking yourself some or all of the following questions:

  • What do I ultimately want to be known for?
  • What is my overall career objective?
  • What are my strengths and weaknesses, and how can I effectively manage them both?
  • What are my organization’s/client’s highest priorities?
  • What positive role models/mentors can I potentially model myself after?

As I mentioned in regard to Todd’s presentation, I would also advise seeking feedback from your boss, clients and/or colleagues as you work to implement Phil’s recommendations, as they can help you identify areas in which your current “face” falls short of your desired one. You should also prioritize these areas for improvement based on 1) the largest observed gaps between your current and desired “faces”, and 2) the significance of the person offering the feedback (i.e., your boss’ opinion would likely have more weight than that of a colleague). Feedback is very important for one simple reason: you can’t resolve an issue you’re not aware of.

Most data analysts and statisticians spend years learning how to properly apply various statistical methods and techniques to solve specific problems, but often spend very little time—if any—learning how to be an effective contributor to the success of their clients, colleagues and organizations. I believe that a focus on development in this area should be considered a key concern of the Evolving Analyst.

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