When advising executives on team structure for their Big Data strategy, I often emphasize data scientists and managers that can effectively work with data scientists; however, there’s one role that I rarely mention–the business analyst.

Unlike data scientists who conduct advanced, sophisticated analysis to foster information innovation, business analysts bridge the gap between this advanced analysis and a business need by developing requirements. That I rarely mention business analysts in my strategic discussions doesn’t dilute the importance of the function they perform.

When translating business requirements into scientific hypotheses for data scientists to prove, the devil’s in the details–or lack thereof. Surveying the landscape of business analysts to augment your team of information specialists reminds me of Goldilocks: some analysts are too scientific, some analysts are too economic, and some are just right.

This business analyst is too scientific

Some business analysts are too scientific. Often times, they are former data scientists who desire a change of pace. Like any other job, advanced analytics for some becomes boring and perfunctory. You can identify these people by looking through their resume or LinkedIn profile.

They will undoubtedly have an advanced degree in physics, math, or something similar, and have decades of work experience as a hard-core super-geek. Then–quite suddenly–they will take an interest in business management: perhaps undertaking a late-in-life MBA program or seeking a lateral transfer with more of a business focus.

With an acknowledgment of their enthusiasm for growth, you should approach this situation with great caution. If a business analyst cannot communicate effectively with the business community, they will be quickly alienated from their primary source of value.

In general, where strong analysts excel in problem solving, they fall short in people skills like effective influence and communication. To mitigate this situation, I would pair this emerging business analyst with a more experienced business analyst that can not only bridge the gap in function, but also mentor the eager protege.

This business analyst is too economic

An opposite and equally dangerous situation is a business analyst that is too economic or business-minded. This is the mirror image of the previous situation in many ways. These are typically business powerhouses who feel they’re being underpaid and recognize the mega-trend associated with advanced analytics; however they don’t have the skills necessary to compete with veteran math wizards.

So, they conclude that becoming a business analyst for data scientists is a happy medium. That may be the case for them, but there’s nothing happy about this arrangement for the leader who’s trying to execute their business strategy.

Like their zealous counterparts, you must approach this situation very carefully. A business analyst that cannot communicate with data scientists will only cause confusion and frustration. Unfortunately, this is very common in Corporate America because you have like-minded managers making the hiring decisions. In this case, both the leader and the business analyst underestimate the level of detail that’s required to effectively communicate with data scientists.

The result is a sparsely documented requirement that raises more questions than answers. The mitigation is the same; use the aspiring business analyst as a protege and add a seasoned business analyst as a mentor.

This business analyst is just right

As you may have guessed, if you employ a business analyst on your strategy team, they must have a good mix of both scientific and business skills. The challenge however is in the amount of skill that’s necessary in each half of the equation. Most leaders err in the sophistic reasoning that a fifty-fifty mix is adequate.

Employing a resource like this on your team is perhaps the worst move you can make. Analysts who fall into this category don’t have enough business savvy to work with the product divisions and they don’t have enough analytic skills to work with the data scientists. They’re absolutely worthless to your team aside from moral support and comic relief.

The only business analysts who can add value to your strategic effort are analysts who can fully function as a data scientist and a business powerhouse. These resources could effectively play any one of three roles on your team: data scientist, business expert, or business analyst. Of course, these resources are very difficult to find and may be superfluous to your existing resources–which is why I rarely talk about them.

Who needs a business analyst anyway?

To be honest, my recommendation is to forego the idea of using a business analyst. Candidate business analysts are typically too scientific, too economic, or just plain unusable. However, the function a business analyst would perform still must be covered on your team.

This is accomplished with advanced collaboration architecture. Colocation is best; however, dispersed and virtual teams can succeed provided there’s a high degree of communication through wide open channels. This means taking extra measures to not only fortify the technology, but to also develop a common language that’s understood by everyone on the team.

Take some time today to assess your capability in translating business requirements into scientific problems. If you’re weak in this area, your strategic vision just might be a fairy tale.