Neale 00:00
Welcome to another episode of the Another Bright Spark podcast, my guest today is Emma Eatch from Woots. Woots? Boo-Woot-a-Boo. Boo-Woot-a-Boo.
Neale 00:18
Welcome to the next episode of Another Bright Spark podcast. I’m Neale Mighall and with me today is Emma Eatch, who is a Head of Data Analytics at Walgreens Boots.
Emma 00:27
Hello.
Neale 00:27
How are you doing? You’ll be fine. Clive Humby said that data is the new oil.
Emma 00:36
Yes, it is.
Neale 00:36
The problem is not the amount of it, it is what you do with it, how you refine it. You’re doing some really exciting stuff. Tell us all about it.
Emma 00:47
Gosh, where to even start? I hate the term ‘data is the new oil’ because that suggests the commerciality to it. And that bothers me. And I think it bothers a lot of people, that kind of concept that you’re kind of using my data to eke money out of me. So I get it, but I don’t like it. And the reason why I’m so enthused about the stuff that I get to do in my role is because it’s clinical. And because we can genuinely make people’s lives better.
Neale 01:14
Use it as a force for good?
Emma 01:15
Yes. Yeah. It’s not about data, it’s about what data can do for people, which I really love about it. And so we do everything from recalling you for your eye test, because it’s really important that we see you every two years, because we can diagnose things like diabetes and glaucoma and stuff like that, all the way through to things like clinical trials and trying to expand our reach into populations that aren’t typically involved in a clinical trial. So if you think about kind of our teeny tiny little Boots stores and kind of really underprivileged communities, they’re people who typically would never even consider joining a clinical trial, because they think that it means that you’re going to grow three heads and kind of have something that’s never been tested on anyone, which isn’t the reality.
Neale 02:02
There’s also, I think, a resistance against like government authority. There’s a suspicion there they, you know, why would they give their data? Why would they trust an institution that they might not look up to or feel fondly of?
Emma 02:15
And I think we’re just really, really bad at explaining that at the moment. It’s kind of like the people are so used to giving their data away for things like…
Neale 02:28
A free milkshake.
Emma 02:28
Free milkshake, stuff like that. But they’re really resistant to giving their data for things like health initiatives. And I can understand why, if I say to you kind of as a loyalty card scheme, can I use your health data? Are you going, oh, no, because that feels really intrusive.
Neale 02:44
Really personal.
Emma 02:45
Doesn’t it? Yeah, it feels like it stepped over a creepy line. Whereas if I say to you, can I use your health data to monitor your retail purchases? Because through that, we might be able to diagnose a condition. It suddenly has purpose and meaning. And you understand what I might be doing with your data. And you’re much more likely to go, well, yeah, that feels really relevant. Of course you can. Yeah, absolutely. But I think as kind of an entire industry, we’re just so data hungry, that we’re not very good at explaining why we want your data and what we might do with it. And given that’s a fundamental underlying article within the Data Protection Act, it’s a bit crap. As the entity that’s so data heavy, we’re not very good at explaining it.
Neale 03:24
No, and I think that communication is very important. I told my mum about this. So my mum came to visit the weekend, and I told her you were a guest. And she was like, that’s what I was doing in the week. And how’s the podcast going? All that good stuff. So I said, you were coming. And she has been using Boots and a loyalty card for the years and years. And she went on a beauty, she used to be a hairdresser, and she went on a beauticians course back in the 80s. And she was always told, this isn’t an ad for boots, but this is promoting Boots, that the No7 moisturiser is the best, you can buy a lot more. But yes, the No7. And she was saying that she gets stuff to the post about offers and that kind of stuff. But you guys are taking this a step further, aren’t you, you’re really digging down. Tell us about the Cambridge research and the and how you’re helping people even further.
Emma 04:10
Yeah, so we partnered with I say partnered with we totally just helped Imperial College over the last few years. So back in 2020, there was a cancer research UK initiative to kind of look at what innovative ways are there potentially out there to diagnose cancer earlier. And one of our data scientists at the time literally over a beer with some friends from the University of Nottingham and Imperial College. Well, what if…
Neale 04:35
How the best the best questions are over a beer? Yeah, yeah.
Emma 04:39
Which became an actual study. And so it’s called the Cancer Loyalty Card Study or CLOCS for short. And it basically takes retail data to look at ovarian cancer symptoms. So there was this hypothesis that they came up with over beer, that because ovarian cancer presents like IBS, actually, are we seeing an increase in gastrointestinal and not prescription, just general painkillers in the run up to a patient being diagnosed? Well, common sense is yeah, probably. But actually, yes, absolutely up to nine months before a human’s diagnosed, they were seeing changes in shopping habits. And to put that in context, if you’re diagnosed with ovarian cancer at stage one, your chances of survival at five years are 93%. If you’re diagnosed at stage four, which most women are is 13%.
Neale 05:29
That’s incredible.
Emma 05:30
So to bring that diagnosis forward, it has massive life changing potential, which is amazing. But I think the our part in the study was very much data donation for customers who are willing to donate their data. But for me, the reason why I’m such a huge advocate of it is what was so exciting about that is it wasn’t Boots doing it. And it wasn’t Tesco’s doing it. The it was us coming together to join data sets up. And that to me is what’s really exciting about the industry at the moment. So if you think about your pharmacy or your supermarket, your optometrist, everyone can see cut or even the NHS, everyone can see this much of your data. But actually, if we start to join those sources up, you suddenly see this much of your data and it becomes really predictive and really powerful. And I appreciate for some people, that’s really terrifying and completely understand why.
Emma 06:23
But kind of for ethically sound studies, where data is governed in a really responsible way. And we put them into federated data centers. And it’s not about anyone’s commercials. It suddenly becomes lifesaver. Which is awesome.
Neale 06:36
Yeah, no, it’s incredible. I mean, you think back, you know, you think about watch true crime documentaries and you think how did they get away for so long, they moved to different parts of the country, if that data was linked up, it would be beneficial. You’re absolutely right, though. I think there is a big fear about people who’s got my data, you know, where is it going to end up? What sort of safeguards and you know, how do you ensure that it is kept safe? And how can you offer that reassurance?
Emma 07:00
Honestly, it all starts with the Data Protection Act, like the we have a joke at work that I am like DPA junior, because the you have to know when you work in this field, you have to know that law inside out, particularly when you work in the clinical space because so much of if you use someone’s health information or you’re inferring something about their health you’re then into special category data and that has to be you have to have even more consent and for me this is where partnership’s really important again so if I take the loyalty card study the Imperial College collected consent from people who were participating whether that was a cancer patient or a control patient that we could then send their loyalty card data they knew exactly what fields we were going to send and what it was going to be used for and how long it was going to be retained for and all of that good stuff and yes we saw their card information to be able to send that data back but Imperial themselves never knew anything more about the customer on our side and we never knew as soon as we’d done that match we removed that customer’s personal data so it becomes kind of a true pseudonymized case control and for me that’s what’s really interesting about health longitudinal studies the actually we forget that you are a person effectively.
Neale 08:16
You become a number.
Emma 08:17
You become a number but that’s really important to start to remove bias and all of that kind of stuff and actually it’s probably really important to you as an individual to know that no one’s kind of sieving through your data going ‘Oh, I found out about this person!’ and it is fundamentally all based in data protection law you know that is what it’s there for and the reason why we work with people like academia and the NHS and government is because we’re all trying to work towards these really secured federated data centers where your data’s there but ultimately it’s there for good and not there for you as an individual unless you actively opt in to wanting to know the outcome.
Neale 08:56
Okay it’s there’s a I don’t think it’s a moral line but there’s certainly a line there, isn’t it? You’ve got you’ve got this data now you can you can tell if someone’s buying paracetamol then ibuprofen then maybe you know Rennies or whatever there are other anti-acid tablets out there but you can buy Rennies you know gastro whatever that is you have that information now and you have that knowledge. Where is the line? I mean what’s to stop you contacting all your customers who have this buying pattern and say ‘You should get yourself checked out’?
Emma 09:30
There’s definitely an ethical line there, there’s the whole just because we could, should, should we? And it’s something we talked about at length. There were conversations about could we do this in-house? Well, we don’t, the whole joy of this study is kind of that bigger longitudinal data set.
Emma 09:46
And we probably could do it in-house, but we wouldn’t diagnose anywhere near as early as a bigger study.
Neale 09:52
So how would you even have that conversation ‘Hello, you might want to go to the doctor’s’?
Emma 09:55
Can you imagine like getting an email or a text or like even a direct mail landing on your doorstep that’s like, you might want to…
Neale 10:02
You might be, hello, you say you might, oh, madam, you might be so, well, a very encountered, definitely madam. Hello, madam, you might be severely ill.
Emma 10:08
Yeah. It’s horrific, isn’t it? The, and there’s definitely ways and means to do it, which is why I think it’s important. There’s these sorts of studies that people can opt into to explicitly say, I understand you’re going to monitor my data and I understand you might contact me. And then we started to talk about things like, well, who should make that contact? Should it be the study? Should it be someone like Macmillan? Should it be CR UK? How do we do that in a really responsible way? Because it’s going to be a terrifying moment, even though it means you might get diagnosed nine months earlier. Yeah. It’s still going to be a terrifying moment. And the, what’s to say, when you then ring the doctor’s and they’re like, oh, we’ll see you today versus we’ll see you in two. It’s got to be so carefully managed.
Neale 10:50
And also the, just the blowback of you’re obviously trying to do the right thing. You’re going to worry someone, panic someone, they go to the doctors, get checked out and there’s nothing to worry about. The comeback on news and organization would be huge.
Emma 11:05
Yes. And that’s why these studies are still so early days. You know, they’ve got such promising results, but actually they do need to be redone on larger populations. We do need to think about things like contact and, and how we manage kind of that longitudinal surveillance for want of a better word, which I don’t like. Cause it does sound like the Big Brother thing. But you know, people have actively got to opt into this. The, I don’t think we should ever be in a state where we’re just randomly ringing customers and be like, you should get yourself checked out. It is really wrong.
Neale 11:37
It is really wrong. I think also as well as the, you know, how you go about it, there’s, there’s the linking up with different organizations as well. So I’m going to mention my mum again. Thanks, Sue. Cause I told her about the study, what we’re going to talk about and she’s a No7 customer, you know, she, she buys that, but she pointed out I’d never buy my pain medication from Boots. So there’s elements where, you know, she, she goes to B&M or she’ll pick up when she goes to Asda. So there’s, there’s missing links in the chain as well. You know, is there, is there a potential to build this out to other organizations sort of get that data. Have you got plans for that? How?
Emma 12:11
We specifically haven’t, but I know things like UK HSAs and the government are talking about things like federated data centers specifically for this purpose.
Emma 12:22
So I think in niches we’re seeing really cool stuff happening. Like the doctors already have algorithms that pick up series of different conditions or series of different complaints that that’s dependent on you actually going to see your GP. Whereas what we found out from the CLOCK study was that you might actually be showing predictive symptoms nine months before you even get anywhere near a GP. And for me, that’s, that’s where clinical data and predictiveness of clinical data really differs from retail data, because there’s a No7 customer, we can do really cool stuff. Like, you know, this shade is the shade for you. And there’s these complimentary products and other customers like you like this stuff. And it’s really cool. And it does, you know, genuinely helps us all when we’re shopping day to day, right? The kind of the Amazon you might like type model. But for me, what’s really different about clinical data is that thing of you might be buying your painkillers over here. Or you might be by having your prescriptions over here, and your doctor here and your optician might be an entirely different company. And actually, until we become more altruistic with that data and join it all up, we’re not going to recognize the true power of it.
Neale 13:32
No, I don’t think it’s right. It’s using it as a force for good because you start right at the start, this new commercialization and for, you know, using it to better people’s lives. I think it’s really important. You said right at the get go, you struggled to communicate what you were trying to do and achieve. In an ideal world, how would you see that rolling out? What would you like to see?
Emma 13:54
For me it’s a huge public education piece. I think it’s still now you say the word clinical trials and we’ve all seen the horror movies where you know someone genuinely does grow a third head and stuff or turns into a zombie or whatever else.
Neale 14:06
Have you had many zombies appear from Boots yet?
Emma 14:08
No, no, surprisingly not.
Neale 14:09
Okay that’s good to know.
Emma 14:10
I think it would be somewhat career limiting if I turn people into zombies but it’s how do we change that public perception, how do we educate in what’s the difference between a stage one clinical trial that genuinely is kind of still really hypothetical to a stage four clinical trial where it’s already been tested on thousands and thousands of people who’s probably licensed in another country because the risk profiles are so very very different but consumers don’t understand that differing risk profile and how do we understand that concept of using data for good? We’d love to monitor your retail data and actually not necessarily to tell you you’ve got a condition but actually to find new medications to support other people. I think people would be a lot more open to sharing of their data if they truly understood the power of it and how it’s going to be used and how it’s going to be kept safe and all of those other good things that surround it but without the fear of you’re being watched by a big brother.
Neale 15:06
I think on the other side as well that you’re just thinking on a geographical scale you could sort of identify areas where health is particularly poor and then you’re making investment decisions on government or whatever level it’s like okay well those guys in the northwest quite seem quite healthy the northeast is struggling a little bit, let’s divert funds and let’s sort of focus on that area get the tone by the message right and yeah and benefit.
Emma 15:28
And there’s other really cool stuff so during Covid a lot of retailers were asked to donate retail data during Covid to help with the pandemic monitoring and what the university found was that actually in areas where cough and cold meds were going up in sales you had a two-week lag period before the hospitals would get hit with an increase in respiratory disease so that actually gave hospitals two weeks to free some beds up and to move oxygen in and that kind of stuff. It’s that stuff that’s really interesting to me.
Neale 16:00
Yeah, absolutely. Okay, so Emma, we have here 2024, 20 women in data and tech. 2025, a woman to watch in retail.
Emma 16:08
Yes.
Neale 16:09
A lot of pressure?
Emma 16:10
Yes. I’m so acutely aware that we don’t get many women in STEM careers. And there are some incredible women that have paved the way for me, but I feel the weight of paving the way for the additional women. And because there are still so few of us, I get so many requests for things like mentoring, and I can’t mentor everybody. And it’s like, how do you pick the handful? I can’t pick the handful. That’s probably where Chat GPT comes in.
Neale 16:41
Put in all the CVs, which ones are worthy of my time?
Emma 16:44
But I also hate that. So I completely find the recognition really, really awkward, really, really awkward. Because to me, I’ve just gone to work every day and done my day job. And I know we’ve done some really cool stuff, but to me, it’s just my day job. But I also recognize from other people that I talk to that actually to them, I am a role model. And when I felt the weight of that, I shouldn’t be anyone’s role model. I still build Lego on the weekend, but I don’t know what I want to do when I grow up. So yes, lots of pressure, but in a really wonderful way.
Neale 17:20
That’s great. How are things changing? You’ve been in data for 16 years, well coming on 16 years now, I did my research. And a lot of things have changed the world at that time. But in the workplace and STEM for women, what’s different from where you were to where things are now?
Emma 17:24
Terrifyingly, not always a lot. But things are definitely changed. Some industries faster than others. I guess I was really naive when I moved into data, I can remember saying that I find women in data initiatives really cringy. And my boss was like, is that because you genuinely find them cringy, or you’ve never been a victim of misogyny. And he was so right. But I’d never experienced misogyny until I moved into a more senior position, and was exposed to kind of a wider group of stakeholders and different cultures and all of that kind of stuff. So you see it more in some organizations than other. I can remember going to a day track here awards in like 2018 2019, we were literally the only women there. And everyone was like ‘Oh, what are you doing here?’ And it was really
Neale 17:24
Serving drinks that kind of
Emma 17:24
Be nice to make the coffee.
Neale 17:24
Yeah, that kind of rubbish.
Emma 17:24
And but it is changing. For me fundamentally, though, it’s not changing because we’re not doing enough at the primary and secondary school levels to keep girls in STEM subjects. And so really cool. Have you heard about the Scully effect?
Neale 17:24
Are you, Dana Scully? Yeah, yeah. About women who wanted to get into, yeah. Big Gillian Anderson fan.
Emma 17:24
Similar, and from Jurassic Park and Laura Dern, there was a spike in people studying paleobiology, that kind of stuff. And weirdly, Jurassic Park is one of my favourite films, I can neither confirm nor deny that I wanted to be Lex. But the I think by being portrayed in the media, there are more women in STEM in the media.
Emma 19:08
And that’s amazing. And but we’ve got to do more to keep girls invested in science topics at school, and particularly in underprivileged areas, you see even more heavily, they exit at GCSE level. I’ve spoken to plenty of people, we’ve got a couple of astrophysicists in the team, we’ve got kind of kind of quite a diverse set of backgrounds. And they both left because of how male dominated the field is and how much misogyny they experienced.
Neale 19:36
Okay, so representation is clearly really important. If people are leaving jobs, what do organizations need? What do they need to do? There’s clearly an issue.
Emma 19:46
There’s so much. There’s certainly like where do you even start? The women who go on maternity leave tend to progress slower in their careers than women that don’t. We see so many women leaving the industry at menopause age so actually we’re not seeing as many women going into kind of exec and CEO roles because at the age they would typically do that. They’re struggling with menopause and then they end up exiting the workplace. So it’s how do we create change at an entire community level to better support people through health conditions and then it’s how do we support people in general, particularly in the analytics community. I think we have a ton of neurodiversity and in a really cool way because they’re curious and kind of they’re passionate.
Neale 20:29
Great with numbers, logical minds, we have the same thing as obviously we’re a team of engineers, we have a lot of ‘neurospicy’ people in the building.
Emma 20:36
But you need to cater for their neurospiciness and say we have Tony and Chica sign up in our area that says no grapefruit because the amount of us that are on SSRIs but it’s kind of actively recognising that neurospiciness and making it a safe place to talk about it and to be your authentic self and I think we’re still kind of behind in that respect. I think we’re tons better than a lot of other countries are but I think there’s still space to go and really stupid things like job adverts. A man will apply if he can tick three of them, a woman will only apply if she can tick all ten. So it’s changing the words for things like nice to have versus must have softening language, making it more accessible. But for me there’s also things like making the application process more accessible to neurospicy individuals because actually going for what seems quite a technical field can be quite intimidating if you’re neurospicy and so how do we make that more accessible? How do we make it more transparent what the interview process is going to look like? How do we make sure it’s a fair playing field? I never ever want to be the only woman on an interview because I’m there to tick diversity. I want to be there because I deserve to be there but at the same time we’ve got to make it more accessible to get those people into those positions.
Neale 21:52
Yeah absolutely. We made a few changes ourselves here. One of the things when we do job interviews is we prep people so we tell them the initial interview is typically a cell phone call, a video call and we say look these are three things we’re probably going to ask you.
Neale 22:05
It’s going to last this long if it lasts longer it’s probably a good thing you know just a complete reassurance and then I think I shared it with you directions how to get here. So this is this is where you can park, this is the buzzer, there’s a fridge, help yourself to a drink. But I think it’s the interviews are such an unnatural thing that generally and it doesn’t just suit you know the neurodiverse it suits everyone because you have people who get nervous in job interviews so if they’re prepped these are the three questions that all these you know this is the sort of thing we’re going to ask you because in the workplace you don’t turn up and go right so today we have to put you to do this you know in advance what’s going to happen it’s completely false atmosphere so that’s that’s great. Neurospiciness aside obviously you’re a mentor people look up to you, a respected woman in the industry, if someone’s thinking to get into data you know into analytics they’re excited by this field what sort of traits do they need?
Emma 22:42
Honestly what I look for more than anything else is curiosity you know if you were the kid that was like why why why why we want you and so we talk a lot about kind of the unicorns in the industry and kind of the people of course there are times when we need that unicorn who has the PhD who really understands the technical statistics and the different types of modelling but for the vast majority of our team we all came from very very different backgrounds I don’t have a technical background I came from stores and a healthcare background but I have a logical approach from dispensing and kind of that kind of stuff the what we look for more than anything else is are you curious are you going to ask why are you interested in people because actually if you’re not those things it doesn’t matter how good you are at the data science you’re not going to flourish in an insights type career because you don’t have that natural curiosity and they want to go out and talk to people and understand what it is they’re after I can teach you how to code I can teach you how to do a presentation I can teach you how to tell a story the I can’t teach curiosity we’ve tried I failed lots of times.
Neale 22:42
Be interested in this thing!
Emma 22:42
Curiosity genuinely be curious reach out to people ask questions.
Neale 22:42
Sounds it’s a lot more because I would I’d imagine obviously quite falsely that it’s a lot of data crunching it’s a lot of you know logistics but it’s actually going out there looking for answers and and it’s people skills, isn’t it?
Emma 22:42
Yeah fundamentally it does come down to the why why are you interested in that why do you want to know the answer to this question what are you going to do differently if I can answer this question for you. If you don’t understand that why you can do all the number crunching you want but you’re probably not going to give the person the answer that they were actually interested in. Yeah. So for me it’s that fundamental curiosity that’s the base of any really good analyst.
Neale 22:42
Interesting. So you’ve covered off ovarian cancer with uh with the study.
Neale 24:53
Are you looking at any other health conditions? Is there going to be more research? What can we expect over the next…
Emma 24:57
Yeah so Boots are continuing with kind of supporting academia and including a follow-on to the loyalty card study and it’s about to go into recruitment so please google CLOCKS but we’re hoping to not only do the study on a larger cohort to kind of take it to the next level but also to look at other cancers that might have OTC type indicators things like bowel cancer, lung cancer, pancreatic cancer is very close to my heart and then also clinical trials recruitment as well so how do we reach minority populations that wouldn’t necessarily be engaged in trials to make sure they are fairly represented and they get the changes to medication and the access that they need. Something we think about a lot is digital poverty and there’s all this really cool stuff happening in society and yes you can now book your doctor’s appointment online you’ve got freedom of choice through your hospitals and all that. What if you can’t afford a phone? If you can’t afford a phone how do you access it? So something we’ve been talking about an awful lot is you have something as simple as how do we understand what languages the local population speak so we can make sure we have leaflets to support them. How do we get kiosks into stores where people can do that stuff on the tablet that they need to do if they can’t access it themselves and so I’d say the things to watch out for are probably how we’re supporting data for good and but also how we better support local communities with their health.
Neale 26:25
It’s funny isn’t what you say about like local languages I won’t name the person I won’t name the organization I was having a conversation with a friend of mine who works in UX design they worked for an organization and they wanted the business as a business objective they had England covered Scotland covered and they wanted to move into Wales all that research and they launched the website and there was no Welsh language version and there was no plans to and the designer was like ‘Oh, we need this because I don’t know what the percentage is but it’s a high population a high percentage of people speak Welsh they need Welsh access’ and the answer from the top was no we need to just launch it English is fine and it’s you’re missing a trick surely.
Emma 27:05
Yeah and there’s no excuse now there are so many off the shelf tools that you can put over the top of a website to do translation. Your Teams can do live translation now where you pick your language. I just don’t think there’s any excuse for not thinking about people as individuals anymore.
Neale 27:23
So you work with a lot of data and we talk about AI quite a lot on this podcast. Are you using any large language models to help you pull through that? What are the benefits? What are other efficiencies? How are you how are you using AI?
Emma 27:38
We’re starting to. It’s still really early days and there’s some cool stuff happening in terms of kind of chatbot on the Boots website and using that to answer customers questions so they get a better experience.
Neale 27:51
Self-service sales.
Emma 27:52
Yeah we’ve used it for things like summarisation of data sets, almost sense checking the analyst’s thoughts, the let’s face it we’re all human and we’re all going to have unconscious bias actually in running a data set through a language model. It might pick out interesting things that the analyst had never thought of or noticed because of their inherent biases. So that’s kind of the initial uses of it and there’s certainly a lot more we could do. I guess it’s back to the ethics question of should we? But if I think about more industry-wide, even in the last week there have been studies coming out that shows that if you use AI to read mammograms they can detect breast cancer up to five years earlier than the human eye.
Neale 28:36
Yes, they know its patterns in cell structure, don’t they? It was incredible. Yeah, it picked it up long before the natural cancer cells form.
Emma 28:43
Yeah to me that’s the really cool uses of AI. I completely get kind of commercially there are tons of things you can do in terms of recommendation engines and large language models and making it feel like you’re talking to a human and all of that kind of stuff. But actually for me the most exciting innovation in the clinical space at the moment is actually the stuff that hits the clinician directly and those sorts of models that can be used to complement the clinician’s skills. I know a lot of professionals are concerned that AI is going to take their jobs and the way that we’ve been looking at it is actually it really complements their role because the AI can pull out the important details that then allows them to have a better patient conversation. And for me that’s the type of AI that’s really about the potential to reduce the NHS waiting list and save the NHS money and get people treated earlier so it becomes more preventative healthcare than responsive healthcare. You know imagine if we could reverse your diabetes before you even had diabetes which is totally possible now with the AI that’s coming online if we use it in the right way.
Neale 29:46
You’re really passionate I can see you’re really passionate about um you know using data as a force for good um you know helping people and it’s I can just see you’re beaming when you’re talking about it. You work for a big commercial organization are you a massive hippie at heart?
Emma 30:02
Yes yes used to wear flares and everything, yes. What gets me up in the morning is the data for good stuff. It’s probably one of the smallest parts of all of my team’s roles, but it’s the stuff that we’re all most passionate about. But we do all recognize that ultimately, unless we make commercials, no one pays our wages and we can’t do that cool stuff. So do I still need to sell flu jabs? Yeah, categorically. And is it the thing that gets me up in the morning? Not entirely.
Neale 30:32
That’s good. You’re using your position to, your powering and your position to benefit the wider society. That’s great. So another quote, I think it was John Naisbitt, who said back in like 82, data is self-fulfilling.
Neale 30:50
At the minute you’ve got a phone, Mark’s got a phone, I’ve got a phone, we’lre signed up to everything. We can record what I had at Nando’s. I don’t want to keep on going back to food. But purchasing, driving behaviour, just everything. The danger is in the amount of data, it’s drowning in it, that’s going to be the issue. How do you keep focus and how do you sort of go about making sure that you’re recording and reporting on the right stats?
Emma 31:17
For me, it’s not just the risk of drowning in it, it’s the risk of bias in it too. And in terms of the drowning in it, again, I go back to being DPO junior, the Data Protection Act says that we should only collect the minimal data necessary for our requirements. And actually, if you really think about the data you actually need for a purpose and really minimize that data set, then actually you’re drowning in lots of it.
Neale 31:41
Well, the irrelevant data is just going to, you need to be focused, I get it.
Emma 31:45
I guess the risk of that is, if you minimize the data set before you even have some initial direction, you’re potentially eliminating really important predictors. And so there’s definitely a balance, but I think it’s once you really understand the data you need, you only collect that data, you have really strong data governance in terms of retention rules and that kind of stuff. But for me, it’s also the things around bias. One of the things we’re really passionate about in recruiting is what does this person bring to the team that we don’t already have? Because otherwise you just create the echo chamber. And you’ve got everyone thinking exactly in the same way. And actually it’s those debates that are driven by slightly different personalities, slightly different approaches to thinking that really creates the innovation. And so I would say actually bias for me is probably more important to look out for than volume.
Neale 32:40
Fantastic. It’s interesting that we had a guest on Helen Carrie and she was a documentary maker. She worked on the Philomena Cunk. So they got comedy writers and documentary makers. And she said, when you cross-pollinate the different mindsets, the potential for creating better work was undeniable. Yeah, when I hire as well, when I first started off building teams initially, I’d go, oh, we get on really well. And I think they’re a good person, that they can do the job. But actually you sort of, as you say the biases, you’re leaning towards someone that you want to work with. Well, actually we probably wouldn’t get on that well or hang out socially, but you’re far more organized than I am. You can actually benefit the wider team. So no, it’s definitely important. Okay, so what’s next for you and the team then? In general, what are you looking forward to in the next coming years? What’s cracking off?
Emma 33:35
I think so much is gonna change in the next 12 to 24 months in this space. If I think about how much data has changed and the focus on data has changed since I moved into it as a career, it’s phenomenal.
Emma 33:46
But actually I think the pace at the moment is faster than ever. And I think a lot of organizations now are focused on it in a way that they haven’t done previously. That Chief Data Officer has only really existed as a role in most organizations in the last 5 to 10 years. I think organizations are truly beginning to understand the power of data and software is coming out of everyone’s ears now, right? We kind of, you know, we’ve got so many different database options and so many different query language options and AI is accelerating at pace. The, I think my honest answer is I don’t know what’s coming in the next 12 to 24 months because the industry is changing. So rapidly. So rapidly, quickly. And that’s probably what’s exciting about it at the same time. It’s kind of a step into the unknown.
Neale 34:32
Fantastic. Thank you for joining us today.
Emma 34:33
You’re very welcome.
Neale 34:34
You’ve been great.