Your Team Had the Skills to Build Business Analytics

With all the news and excitement about self-service IoT and big data analytics, it’s easy to miss what could become a major stumbling block when it comes to successful deployment of these new tools: namely, the skill sets of the “citizen-analyst” teams that will be charged with building and running the applications themselves. Ideally, you’ll be looking for personnel who can bring special domain knowledge, whether in business, data science or development, to their projects.

These teams have plenty of help, with graphical dashboards, wizards and various low-code development options, but at least as important will be the abilities of the humans to put those tools to their best use. As a business unit manager there’s plenty you can do to support and encourage your teams, but most important is to understand and be able to identify the skills that will put your people – and you – in the best positions to succeed with their projects.

Mixing Business and Tech Can Be Tricky
No doubt you’ve heard the phrase “Leave your ego at the door.” This applies in spades to mixtures of technical and non-techies, and it’s an obvious starting point in character assessments of would-be team members. But it’s only a starting point, and a simplification, of the qualities you should be looking for. Here are some others:

  • Willingness to communicate – This isn’t as simple as it may sound. It requires some measures of both fearlessness and patience. A business-domain person shouldn’t be reticent to ask so-called technical questions, nor should the tech expert, be unwilling to answer such questions with clarity. That works both ways, as well, with the business person willing to take the time to help the data scientist understand the subtleties of a particular project.
  • Ability to see – and tell – the big picture – Team members should be enthusiastic about communicating to others outside the team, whether other teams’ members or other business-unit or management groups. They should be able to put their knowledge into a larger context, say for a report to C-level executives. This means that team personnel should be willing and able to learn the organization’s business goals and strategic interests, and to know how their work fits into these larger contexts.
  • Curiosity tempered with skepticism – Curiosity largely goes without saying, but it should be tempered with healthy doses of skepticism about the data that’s being analyzed. Does it represent a true picture? Are shopping-bots or other artificial influences present? Is there sufficient data to populate the model, or do we need more?

What You Can Do
With your teams up and running, there’s plenty you can still do maximize their success. You should, for example, make a point of matching analysis tasks with the skill sets of team personnel, and support team members’ efforts to develop new skills through training and education.

You should also encourage standardization where possible, in fostering cross-team consistency in data sets and reporting styles, for example, and in building libraries of reusable routines and templates. Don’t be averse to bringing in outside consultants, either, if they can help your teams through professional guidance.

Finally, look for opportunities to communicate your teams’ efforts outside of the business unit. Encourage teams to present their findings and methods to other business units and management groups, and look for opportunities to develop analysis synergies within these groups. And, of course, look to augment your teams’ skills as you make new hires in the future.

Check out Vitria WHAT IF white paper to learn how service operations performance can benefit from an advanced analytic solution.

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