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Skip to 0 minutes and 4 secondsMy data journey, I guess, starts, six years ago, I did a master's degree in Business Analytics and big data. Previously, I was just at a marketing consulting firm and realised data is kind of the future. And I want to know more about it and how to use it, and how to really make good decisions with it. So I did that masters, then found myself in Scotland for a couple of years as the only data scientist at a startup that was working in marketing and retail. And that was a really great experience because it really gave me a hands on practical experience, understanding how modelling works, and how those kinds of results end up actually used in a business environment.

Skip to 0 minutes and 42 secondsAnd then from there, I moved over to TV Squared, where hilariously, my actual job title at the time was the Director of Client Engagement and there was no data science team at TV Squared when I first started, we had a couple of data scientists working on a very specific project, but they weren't really a team. My job was essentially to help them translate what it was that they were doing to the client, so the client would actually know how to use, what it is that they've been building and that gap was very prevalent there, which is why they hired me.

Skip to 1 minute and 15 secondsHowever, we quickly realised that TV Squared is a software as a service company, the work that they were doing was very much consultancy oriented, and wasn't sustainable for a scalable, replicable product and the company didn't realise it. So part of my role was also explaining why this was such a difficult thing to do and such a difficult thing to replicate. And a lot of it has to do with the data and the data sources and working with clients and how so much of it is, despite what people say, a lot of data science can be an art more than a science.

Skip to 1 minute and 45 secondsAnd it's that mix and that balance that you kind of have to get right which this product that we were trying to do just wasn't. So we ended up stopping that product and I think my work in communicating what it was that the data science team needed and what they were doing, was what really made them realise, okay, we need someone who actually knows about this stuff to help us make better decisions with it. And once we kind of got through the problems were, made some suggestions. The team kind of slowly started forming itself as a new product research team.

Skip to 2 minutes and 21 secondsSo rather than being told, we're going to build this, data scientists go build it, it's, we need to research this first and you guys are best positioned to understand the data that we have, what the problems are, what our objectives are. So let's actually give you the resources and support needed to do that. And that's somehow kind of worked out over the course of six months to a year and now I'm here. Yeah, I think the the hype versus value question is one that comes up a lot. And I probably fall to the side of there's probably more hype in the beginning than value, especially if you don't really know what you're doing.

Skip to 2 minutes and 53 secondsI'll probably be the first to say that if you're first starting off in kind of this data science industry, you probably don't even need a data scientist, having one is going to be a little useless for you. Because I think what you first need to do is understand the data that you have and the questions that you want to answer with that data, getting in a senior data analyst who can manipulate some data and answer some questions and get an idea as to what they have and historically, what's been happening, is going to be the highest added value that you can do in the beginning.

Skip to 3 minutes and 20 secondsAfter that, once you have a better idea, probably the next step, and it's a very wide generalisation, but getting a data engineer in, to work with the data that you actually have and get it into a format that's easily used and easily manipulated and easily cleaned. And then and only then, once you have the data and know what you're trying to solve, or trying to improve, or try to do, get your data scientist in.

Skip to 3 minutes and 45 secondsOtherwise, I think it's going to cause a lot of frustration on both sides because of the high expectations of the company and the completely opposite expectations of the data scientist, where they would assume everything's kind of ready for them to come in and get their hands dirty. So I tend to think that's not the right first step for most companies, can be different depending on, what what you're trying to achieve.

Data Leadership - Regina Berengolts, Head of Data Science at TVSquared

I’d like to introduce you to Regina Berengolts, Head of Data Science at TVSquared.

Regina is a recognised data leader within The Data Lab network and has provided great insights to many organisations on how to succeed with data science, and how to lead in the data space.

People often struggle to know when and how to utilise Data Science. I asked Regina, as a data science leader, if she could share some of her experiences of how her organisations overcame the hype around data science to best drive value. To set the scene, I first of all asked Regina to give an overview of her journey to becoming a leader in Data Science at TVSquared?

Regina has high credibility with stakeholders and her team given the journey she took to becoming a data leader. The observations that really resonated with me were some of the leadership responsibilities she discussed, including:

  • Ability to explain value to customers

  • Communication across all stakeholders

  • Expectation management

  • Phased introduction of data capabilities in line with your needs.

Appointing a capable leader is necessary if you really want to drive change. And, especially in the early days, the leader needs to protect their team from organisational “noise” so that can focus on adding value from data.

Regina also spoke eloquently about some of the different data roles an organisation may need (data analysts, data engineers and data scientists) and this is something we will explore further later in the week.

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