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Your Data Science Journey

01 August, 2019 | 7 min 47 sec
Podcast Host Tania Salarvand | Podcast Guest Rachael Hadaway
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Brief Summary

Technology is reshaping customer expectations at an unprecedented rate and to keep up, we must harness our data assets to gain real customer insight. Rachael Hadaway, Senior Vice President of Products, Strategy, and Design at 84.51°, a data and analytics firm owned by Kroger, shares how your data science practice could be serving you better. If you are a business or tech leader, wanting to use data to create a culture of continuous intelligence, this is the podcast for you.


Highlights


There’s no such thing as an ‘average shopper.’ To be successful need to understand each individual customer and we need to create a personalized experience for them, which means average data is not good enough. 


Challenges have always been about getting something implemented. Getting a big enterprise to take a big leap of faith that the data was showing a different path was challenging. Focusing on science and tech only gets you so far, you also need a storyteller to bring people along on the journey and make a different decision.


The biggest factor for creating a good culture for Data Science to thrive is trust. And that goes both ways- from the team embarking on, or trying new techniques, through to the business stakeholder that needs to trust that they will do the best thing for the business and their customers. So anything you can do to build trust is key. 


Trends that surprised me most is the wideness of data. Data can come from everywhere and is more trackable than ever. That data combining together to create new data has allowed us to create models that are much more accurate and interesting than ever before. 


The relationship between consumers, data and trust is a constantly moving target. People were initially wary with proving organizations data, then got more comfortable because they saw better experiences, but now starting to be wary again. We have to constantly evaluate if we are providing enough value to customers to make it worthwhile for them


Finding someone who has the ability to tell a story through data is gold. We have lots of tech talent, but harder to find someone who can turn boring/ flat data into something with texture and connect to the human experiences. Those are the unsung heroes of data science


Business and technology challenges pale in comparison to people challenges. When we have strong self-directed teams we can accomplish anything. Focusing on the team, not the business problem has been the biggest unlock for me. We need to hire great people, empower them and watch them do great things.


We should have No fear of AI/ML taking over the industry. These concepts need human to make them smart and meaningful. Humans will have an even stronger role in future. It's about taking the things that humans are uniquely qualified to do and elevating those things. 

Podcast Transcript


Tania: You know when you're at a conference or listening to a presenter, you look around and half the audience is on their phone. Others are nodding off. Other times everyone is fixated on the speaker, hanging on their every word. Now imagine they're talking about data.


Rachael Hadaway is one of those rare individuals who can talk about data and actually create energy in the room. I love her fresh, no-BS perspective on the power and implications of data and how we use it. She is the Senior Vice President of Products, Strategy and Design at 84.51, a data and analytics firm owned by Kroger, the largest grocery retailer in the United States.


Welcome to Pragmatism and Practice, a Podcast from ThoughtWorks where we share stories of practical approaches to becoming a modern digital business. I'm Tania Salarvand, Global Head of Marketing for ThoughtWorks, a global software consultancy.


Rachael and I sat down during ParadigmShift, our annual executive conference, to talk about how 84.51 is using data and good data science practices to create more personalized experiences for 60 million Kroger shoppers. Enjoy.


Rachael Hadaway: The thing that we've learned is that there is no such thing as the average shopper. To be really successful, we have to get underneath the skin of averages and understand each individual customer, and create a personalized experience for them, which means that average data is not good enough, and our data and science goes to the individual and explores what makes them tick and what matters to them.


Tania: Which really reflects on some of the things you mentioned about data assets not being just your data, but the insights that you gain from them. What have been some of the challenges that your teams come up with that maybe you weren't expecting on this path of really discovering data, how to use it, and then the outcomes of that?


Rachael Hadaway: Yeah. The challenges have all been about getting something implemented. Kroger is a large traditional retailer with hundreds of years of heritage. Many cases, they were doing things that they had done the previous year and the decade before that. So getting them to take a leap of faith that the data was showing them a different path was really challenging.


I think in the earliest days we were focused on the science and the tech, and what we were missing was the storytelling that was required to get somebody to make a different decision and to go with us on that journey.


Tania: That's interesting actually because essentially, if I'm not misrepresenting it, you're a startup within an enterprise. With that comes, I'm sure, some benefits of having investment capital and support, but also maybe some other things that are not benefits because you're trying to act like a startup. Are there any things you can reflect on in your experience with that?


Rachael Hadaway: Yeah. It's actually probably the best of both worlds. We're small. We're less than a thousand people working to influence a 300,000-person larger company.


And so, we have the scale, and we have the funding that we need to do really cutting edge things, but we're able to retain a culture that's really nimble and really agile. That allows us to kind of go where the data takes us and go where the customers take us.


Tania: Speaking about culture, you definitely reflect on, it's not just the tech and the data, but really the culture that makes a difference. What is the type of culture that you've tried to create there to enable and empower your teams?


Rachael Hadaway: Yeah. The biggest factor that creates a good culture for data science to thrive is trust. That goes both ways. It goes within the teams that are really embarking on new things and trying new techniques all the way through to the business stakeholders that have to trust you, that you're going to do the right thing for their business, their categories, and their customers.


Anytime you can build and improve trust is really key. That doesn't mean always doing what the easy thing is. In many cases, it's about shifting into a different gear. But when you have trust, it makes those conversations go a lot more smoothly.


Tania: In general, data science is a bit of a buzzword right now, and probably becoming more of one as people are starting to discover how to leverage data in new ways. What are some of those trends that you've seen actually come to life within your space, and has really excited maybe yourself or your teams?


Rachael Hadaway: Yeah. I think the trend that's probably surprised me the most over my career is the wideness of data, and how data can come from almost every place. It's more trackable than ever before. That data coming together to create new data and that combinatorial factor of data has allowed us to create models that are just so much more accurate and so much more interesting than they've ever been before.


Data will continue to come out of the woodwork as things get digitized and pretty much everything gets connected. I think that's going to be a real difference maker for what you see in the industry.


Tania: As you reflect on that, I think you said before that this conversation is a piece of data, and how we take that and do something with it is also interesting.


There was a nugget that you mentioned around trust within your teams and the culture of your teams, but how does that play in the relationship to data and access to data?


Rachael Hadaway: Yeah. It's actually a really interesting and ever-moving topic. I think people initially were really, really wary about providing corporations data. And then, they got comfortable with it because they saw better recommendations, better sales, all those things that made life easier. But I think they're starting to get wary again.


What does all this mean? Am I trackable now? Do people know where I live? I think that relationship between consumers and data and trust is going to be a moving target. We're going to have to constantly evaluate whether or not we're providing enough value back to customers to make it worthwhile for them.


Tania: And of course, a big part of that is the culture that you're creating, the talent that you're bringing in. What are some of the things you're looking for in your talent outside the obvious, of course having data strategy, engineering or PhD experience? What else are you looking for to make kind of the ideal team?


Rachael Hadaway: Yeah. I would say that the talent or the skill that makes a team really come to life is storytelling. We have a lot of great tech talent. We have a lot of great technology, but that ability to actually tell the story through data is really challenging.


When you find people who are really talented in that space, they're absolute gold. They turn something that's boring and tends to be fairly flat into something that has a lot of texture and allows people to really get on board with where we're going.


Tania: Which is interesting because I think traditionally when people think of data, they think of charts and graphs, and that's how we portray our story. But that's really not the story.


Rachael Hadaway: Absolutely. In fact, many of our most important recruits are people that are not coming out of data science engineering programs or even have data science engineering backgrounds. We're looking for people that have life experiences that can really connect to the human experience. Those are the unsung heroes of data science, but I think we'll be difference makers for companies that really win in this space.


Tania: Lastly, if you reflect back to maybe your first 90 days taking on this role in this endeavor, based on what you know today, what would be the advice you give yourself from 18 months ago?


Rachael Hadaway: It's really interesting. The business challenges and the technical challenges pale in comparison to the people challenges. When we have strong, really self directed teams, we can accomplish anything. Not focusing on the business problem and actually focusing on the teams has been the biggest unlock for me.


If I could go back then, I would have put all of my focus into the team because I would have gotten to a different spot with my business challenges much quicker. It's all about hiring great people, empowering them, and then watching them do really creative things.


Tania: That's a great reflection because I think a lot of folks have fear of ML/AI taking over their jobs and the people side of it. But I think what you're reflecting on is actually that's going to be the difference.


Rachael Hadaway: Absolutely. If there's one thing that I know from my job, it is that we should have no fear of machine learning or AI taking over the industry. They're dumb concepts. They need humans to actually make them smart and make them meaningful. I think we'll find that humans have an even stronger role. It's all about doing the things that humans are uniquely qualified to do, and really elevating those things.


Tania: Fantastic. Thank you so much for your time, Rachael. We really appreciate it.


Rachael Hadaway: Thank you for having me.


Tania: Thanks for listening. Be sure to check out. Rachael Hadaway's presentation at Paradigm Shift, making your data work at thoughtworks.com/paradigmshift.


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