Skip to 0 minutes and 4 seconds It’s a bit about the people as well. So the types of skills we’ve got their methodical, that they’re there, they have a logical approach there. In some cases, they’re good with detail. But they are open to new ideas. And they will work collaboratively those characteristics is aware of what you’re looking for across all the roles. And if you can get that balance right, then then you’re going to get a good team. Data science is well named and method there is an element of scientific methods and experimentation to what you’re doing, you’re looking for the unknown, using the resources that you have available to either create something new or or develop some new insight.
Skip to 0 minutes and 43 seconds So if you’re looking for them to produce sort of reporting, and things like that, that’s not really at all for a data scientist and unless you you really have very complex data or very voluminous data, you may be able to get what you need with somebody who’s more of an analyst, who can approach it with slightly more of a business mindset to it. But the data, it’s not to say the data scientists aren’t needed, but their best deployed where you’re trying to look for something new, something very different. And rather than maybe just trying to improve something that already exists.
Skip to 1 minute and 23 seconds In terms of what you get them doing, as I said, before, it’s about getting, having that information available for them to work on the tools available for them to work on. They will always have to do some cleaning up and tagging of data and so on. But if that’s 80% of their job, then you’re you’re paying the wrong person to do the job. And typically, they won’t have the technical skills needed to deploy what they’ve built and to care and water. So if you build models that you’re going to run your business on, you have to look after them and know how you’re going to manage them through their life cycle.
Skip to 1 minute and 55 seconds That typically again wouldn’t be the data scientists, they’ll create it in the first instance. But they won’t necessarily look after it once it’s, once it’s been deployed and that and that’s where you need a different sort of operational mindset and set of skills, within your organisation. That’s where working closely with the IT function is really required as well that’s more their, their bag. It might be a cliche, but having committed stakeholders behind you, it helps in any organisation that helps you secure the basic resources and kind of funding that you need in order to take something forward. So it’s not just getting hold of technology, you need the internal resources available to help you get the data that you need.
Skip to 2 minutes and 37 seconds So those those series of resources, the whole piece that you would need to start your project off. And then and then hopefully also having in place already good working relationships with all the different teams the business experts that are really going to give you the, give you the input to help you understand the data and then also to help you understand whether the insights you’ve devised are sensible or not, or you know what they actually mean in in the in the business terms and that’s where the collaborative element really comes into it. So I think you really need to have those in place before you start maybe looking for a data scientist to come in and do a project.
Skip to 3 minutes and 10 seconds Know what you want to do, have the resources. It sounds very simple, and I know it’s not as easy as that but have the resources in place before you start really.
Setting up a team
I also asked Rhona MacLennon (Principle Solutions Architect at The Data Lab) to share her views on skills and team setup given her experiences across many companies and industries.
I found that fascinating, specifically about the need for a hybrid team that allows you to access a broad range of skills, skills that hopefully align with what people enjoy and have an aptitude for. And it was great to hear about the different data roles and how they work together including data analyst, data engineer, data scientist and data architect.
I also liked how Rhona described some of the leadership skills required, especially about the need to galvanise & drive a team toward success. And doing it with empathy & integrity.
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