Category Archives: Leadership

How to Become an Effective Champion of Analytics

As an analyst, you are obviously aware of the power of data analysis. You know that the application of appropriate analysis techniques to a well-constructed, meaningful dataset can reveal a great deal of useful information—information that can lead to new opportunities, improvements in efficiency, reductions in costs, and other advantages. While many organizations have adopted analytics on a wide scale, several others still employ it only in certain areas, and some (believe it or not!) rarely use it at all. If you’re like me, you often get excited thinking about new ways of applying analytics in your organization, and are eager to share your excitement with people you think would benefit. This puts you in the role of being a “champion” for analytics in your company—an evangelist preaching the gospel of how data can be used to solve widespread problems. And you want to convert everyone!

Don’t be surprised, however, if others don’t respond in kind. Depending on a person’s background, they may not understand what analytics is, in which case they simply can’t know of its benefits (until you tell them, that is!). If they do have some degree of understanding of it—at least in principle—they may believe that there is no use for analytics in what they do, or they may be intimidated by what they perceive is a highly complex, incredibly arcane set of algorithms that in no way relates to their daily work. Regardless of the reason, such a response can quickly kill your enthusiasm, which is not only frustrating for you but also detrimental to the organization.

When this happens, you may need to find a better project, or you may just need to build some trust with someone so that they understand that you are there to help them. Following are four actions you can take to overcome objections and thus increase your effectiveness as a champion of analytics in your company:

1. Focus on the person’s greatest challenges and most burdensome tasks. Everyone has something about their job that is a source of frustration, no matter how much they love what they do. For the person you’re working with, a meaningful application of analytics is one that relieves his or her frustration or minimizes it as much as possible. As long as the application is also important to the overall business, this is a great way to begin to show someone the true value of analytics. It’s also a good idea to start small and then work your way up to bigger projects later, so that you’re not overwhelmed and thus don’t run the risk of not being able to deliver.

2. Incorporate their knowledge and expertise. You may be an expert on the application of analytics, but you are most likely not the expert on every functional area of your organization—not even the CEO can make that claim! Therefore, you must rely on the wisdom of others to help you understand all of the intricacies that cannot be contained within a dataset, including any legal, ethical or other considerations that must be taken into account. What’s more, you are demonstrating respect for their specific knowledge, which will help build trust and make them more eager to work with you.

3. Learn to speak their language. Being able to understand and communicate in the nomenclature used by the people you’re working with will demonstrate that you are willing to meet them on their terms. It’s not a two-way street, however: avoid using analytical and statistical terminology as much as possible. If necessary, practice finding ways to explain difficult or complex concepts in an easy-to-understand manner (metaphors often work well for this!).

4. Publicize your victories—and share the credit! Once you have successfully completed a project, be sure to tell your boss. Ask him or her to spread the word throughout the organization and externally if possible, but make absolutely sure that the credit is shared with those you collaborated with and assisted you in the project. This will help build attention to the power of analytics within the organization, as well as make those people you’ve just worked with feel rightfully appreciated and respected.

If you look closely at these four recommendations, you’ll notice they all have one thing in common: they put the focus on what you can do to help others. Whether you follow these specific tips or not, as long as you promote the use of analytics as a service that can help a person solve a problem that is important to them, you will go a long way toward fostering a positive attitude toward analytics throughout your organization.

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.