Video Transcript

Hey everybody this is Hobbs and welcome back to season 2 of This Not That!

This Not That is a blog that we do focusing on data best practices across the entire life cycle of business data. For Season 2 we're going to do some things differently. First- We got some feedback from our users and I’m excited to do videos answering questions that you guys had and addressing some good ideas you suggested. Second- I’m going to bring some people in from my network and from the company here at Valorem, to talk with me. We'll do back and forth on a given topic related to data science or data pipeline, something that our guest is an expert in.

I'm excited to get another season started!

Welcome back everybody! In this episode of TNT we're going to talk about a best practice that spans the whole data life cycle through your business. And it has to do with team composition. So I was reading an article recently in the Harvard Business Review about these people they call unicorns. Unicorns are technical individuals, often data scientists, with really unique, highly-valuable skill sets. They're incredibly difficult to find because once you hire one, you want them to do everything, you want them to handle all of the things related to your data, data science, etc.

Now this can be really tricky. The article talks about it from a communication perspective, but it expands outward beyond that and I have what I’m going to propose as a better system. Instead of trying to find a unicorn, the perfect individual who will do absolutely everything that you need them to do, instead try and hire a team of people. Then split up those individual skill sets with backup and repetition between individuals.

I’m going to call this the Chimera. For those of you who are not Greek mythology nerds, the Chimera was a creature in Greek mythology, that was a hybrid of three diff animals: the lion, the goat and the snake. You don't have to remember that, there won't be any test. But for our purposes, when we're talking about building out teams, what you want to do is make a list of the functional skills that you want to fill. Then instead of trying to find a single individual who can fill all of them, break that out into individual job titles and make sure that two or three people share the same skill set.

So for example, if you're going to have a backend developer doing a lot of data science, don't necessarily expect that same person to do the data visualization or the presentation to the executive team. Find those skills elsewhere and hire a team of people who can all come together to fulfill your BI needs. Now the tricky part there is making sure, as I said, that you've got repetition and backups. Let’s say that the person who does the usual communicating with the executive team is out of office for some reason. You need to have another person who can fill that exact same role and has that same skill set. Then, when you're looking at projects, you can go through and figure out which individuals you need on the project based on the key skills they provide.

To recap that idea, you shouldn't be focused on finding a single person who can fill all of your needs. That's really difficult to find and everybody is after those people.
Instead build out a well-balanced team where everyone involved can provide something that you need in your business.

Thanks for watching everyone, if you enjoyed today's episode follow us on social media, head to the blog, head to the website, find us on YouTube. Additionally, if you have an idea or best practice you think we should talk about, I would love to hear that from you. Leave a comment on this video and I’ll see if I can't squeeze that into this season or the next one.

If you're listening to these talks and finding that you've got some skill gaps in your company, we would love to come along side you and help you as best we can. Whether that's through executive round tables or 2-3 day, focused workshops on data visualization, data pipelines, etc. If you need some technical help for a short or extended period of time, we would love to help you solve those problems at your business.

Thanks again for coming everyone and I will see you next time.