Taylor Rodgers
  • Home
  • Books
  • Free Tools
    • Requirement Doc Templates
    • Quality Checking Guide
  • Blogs
    • Data Science
    • R Programming
  • Consulting
    • Statistical Consultant
  • Contact
  • Home
  • Books
  • Free Tools
    • Requirement Doc Templates
    • Quality Checking Guide
  • Blogs
    • Data Science
    • R Programming
  • Consulting
    • Statistical Consultant
  • Contact

6 Types of People Who Will Succeed in Data & Analytics

9/22/2019

0 Comments

 
Picture
Companies are investing heavily in data. That means they need to hire more people to work on that data. There’s not enough talent in the industry to fill all the open positions, so employers naturally have to start from scratch with some people.

Since you can’t always hire for experience, hire for personality types.

There’s six types of people who will do well in data and analytics, even if they have no BI experience. My handy list below tells you what those six types are. It’s unlikely you will find someone with all these characteristics. But the more of these traits a person has, the better they’ll do.

1. Skeptics

Skeptics are some of the best people you can hire in business intelligence. They’re never satisfied with surface level answers. They have trouble believing any claim until they have the facts to prove it.

During a job interview, they’ll ask “so how does that actually work? Don’t you ever have an issue with such-and-such happening? What does that mean for the business?”

It’s easy to spot these people internally at your company. They’re usually the subject matter experts who tell you what’s wrong with your own data. They sometimes know the data better than you do. They can pull up their source tool and show you the exact discrepancies. (These have always been my favorite subject matter experts).

2. Math Geeks

Math geeks are the people who majored in math, economics, physics, or some other STEM degree in college. These are easier to hire for because their degree reflects that skill. If you’re ever in a position where you need to hire an entry-level person, a STEM degree can quickly narrow down who would be good at this job. I don’t think I’ve ever met someone who was great at math who was terrible at business intelligence.

Learning to read mathematical notations prepares you for learning to code. Mathematical notations describe the type of manipulation that would occur to numbers (i.e. data). That means math geeks can envision what’s happening to data inside their heads very easily. That skill translates perfectly to any BI development.

The other benefit to hiring math geeks is they intuitively think about upper level statistics and how they can be used. While a multiple linear regression is not something that will be used everyday, standard deviation and measures of variance have very powerful and underused applications in BI. It’s far more challenging to train people who don’t like math in these methods than it is to simply hire math people who already know how to use them.

3. Tinkers / Coders

Tinkers and coders are the cousin to the math geeks. They're the people who would fix their own refrigerator at home or can fix their own car. They may have started programming in their early teens or late twenties, but once they did, they became hooked.

They love figuring out the new tips and tricks with every new release of a software and will frequently have light bulb moments about how to apply it.

Ideally, you’ll want at least one tinker on your team and one math geek. Their collaboration can produce amazing results.

4. Consensus Builders

Consensus building is the most underrated skill. I think for someone to get started in data, they need to be a math geek or a tinker, but long-term success depends on their ability to build a consensus. Projects typically have multiple stakeholders with competing viewpoints. BI professionals simply can’t wait for one of those stakeholders to write a requirements document themselves and hand it over. BI professionals have to be able to step forward and lead the meetings themselves.

If you don’t have at least one person on your team who can do this, your solutions probably won’t ever get anywhere.

It’s challenging to identify these people in interviews, but you can spot them internally in your company. If they set a meeting, they always include an agenda and a list of must-have decisions for the meeting to be effective. During those meetings, they’ll ask opinions of the quieter people in the room and bring up counter-points as a devil’s advocate. All of this ensures everyone had a voice and all viewpoints were considered.

5. Creatives

Creatives are the people who always have ideas. While I think there’s a few too many idea people in the world (we need more people to implement those ideas), it’s still a great skill to hire for. These folks will think of solutions no one ever thought of before. They’ll have a light bulb moment mid-meeting and pitch it on the spot.

They still need a strong foundation in math or technology. A person who is all creativity might propose cool ideas that are unrealistic. That can frustrate stakeholders.

6. Storytellers

It’s become a cliche in this field to say “we tell a story with the data,” but it’s a cliche for a good reason – stakeholders want it.

A good storyteller can take complex material and make it accessible to a general audience. Instead of building PowerPoints with data dumps, they think about how to use data, visual aids, and their own verbal communication to engage the audience. Instead of building a dashboard with lots of KPIs and pie charts, they’ll scale back and focus on business questions.

You can spot storytellers internally by looking at those people who build engaging presentations during meetings. Are their presentations plain bullet point lists? Or do they follow communication best practices? That gives you a clue as to whether they're a storyteller.

It’s also easy to spot these characteristics in external candidates during the interview process. They'll naturally create a narrative while answering your questions about past experiences.
0 Comments



Leave a Reply.

    ABOUT

    A blog about the non-technical side of data science.

      SUBSCRIBE

    Confirm

    ARCHIVES

    April 2022
    March 2022
    October 2021
    September 2021
    August 2021
    March 2021
    February 2021
    January 2021
    November 2020
    August 2020
    June 2020
    May 2020
    April 2020
    February 2020
    January 2020
    December 2019
    November 2019
    October 2019
    September 2019
    August 2019
    July 2019
    June 2019
    May 2019
    March 2019

    RSS Feed