71 episodes

The Business of Data Podcast is dedicated to providing a voice to the Global Data & Analytics community. Each episode is focused on a specific topic area, uncovering the most pertinent issues facing global data & analytics leaders.

The Business of Data Podcast Business of Data by Corinium

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The Business of Data Podcast is dedicated to providing a voice to the Global Data & Analytics community. Each episode is focused on a specific topic area, uncovering the most pertinent issues facing global data & analytics leaders.

    Lin Yue: An Outsider’s Take on Data-Driven Innovation

    Lin Yue: An Outsider’s Take on Data-Driven Innovation

    Goldman Sachs Executive Director Lin Yue is a first-generation immigrant to the UK. She shares how her experiences in a foreign country have shaped how she applies data to business challenges
    This week’s Business of Data podcast is unlike previous ones. Where we usually hear from data leaders, in this episode we hear from a business stakeholder who uses data heavily in her role at Goldman Sachs.

    Lin Yue works as an Executive Director of UK Institutional Business at Goldman Sachs Asset Management. As a first-generation professional working in the UK, she believes her cross-cultural experiences have helped her develop a unique perspective on solving business issues with data.

    Citing a Harvard Business Review study, Yue says that almost half of the companies in the Fortune 500 were founded by immigrants or their children. Yue says this ‘outsider mindset’ helps people see situations as easily changeable and not ‘set in stone’.

    “There’s value in being an outsider!” she argues: “If you think about what's driving innovation around the world, very rarely do we hear of a brand-new idea. Innovation is what you get when you look at things from a different perspective.”

    Understanding Global Markets with Data
    Yue goes highlights some of the major growth disruptors she helps investors navigate with data, such as generational gaps between consumers, and how they influence the way organizations perceive their markets.

    “Millennials and Gen Z are coming into society’s highest income years,” she notes. “Their consumer behavior will be different to previous generations’. They're willing to try more things and they're much more focused on the sharing economy and on having experiences.”

    “Look at China,” she adds. “Its 400 million millennials are the largest generation, whose aggregate income has exceeded the previous generation’s average. [Three quarters] of consumption in the country will be driven by them by 2025. But companies are not adapting to these behavior and consumption patterns because they think those millennials are still too young and that they don’t have money. That is all very out of date because this group is defining the consumer landscape.”

    “Let’s use [luxury fashion brand] Burberry as an example,” she continues. “In the West, its typical first-time buyer is probably in their late 40s or 50s. Whereas, in China, that first-time buyer is in their 30s. So, if a company doesn’t understand something like this, it would already be failing in that market.”

    For this reason, Yue says it’s vital that global enterprises make use of company and third-party data to understand the markets they operate in. These insights should then be used to optimize their business strategies in each of these regions.

    “Companies usually join a new market and use the same product or service [they offer] in other markets,” she concludes. “They believe one product is enough. But maybe, because it knows it isn’t the dominant culture across the world, Chinese companies tend to start by adjusting the offering for each market. It’s a difference in mindset regarding the way data’s used.”

    Key Takeaways

    There’s value in being an outsider. Innovation happens when people bring fresh perspectives to old problems
    Let data lead. Applying data to business problems can help companies optimize their strategies to market conditions
    Follow China’s example. Using data to tailor products and services for different markets can drive better company performance

    • 30 min
    Richa Sachdev: The Whole Enterprise Plays a Role in Making ML Ethical

    Richa Sachdev: The Whole Enterprise Plays a Role in Making ML Ethical

    Richa Sachdev, Head of ML Engineering at Vanguard, discusses her approach to ensuring ML models are developed ethically and used responsibly
    As organizations get to grips with the practical issues around ensuring AI and ML is used ethically, a lot of effort needs to go into helping business stakeholders understand these technologies. In this week’s Business of Data podcast, Richa Sachdev, Head of Machine Learning Engineering at investment firm Vanguard, shares how she’s ensuring her team puts ethical data at the center of its strategy.

    Principles for Ethical Model Development
    Sachdev’s team’s primary role includes  developing recommendation systems for funds and using data to track customer interactions to support Vanguard’s sales and marketing functions. For Sachdev, doing this ethically means focusing on issues such as privacy, explainability and bias.

    “As engineers, we can be proactive about governance by redacting unnecessary information when we’re creating a model,” she says. “Of course, we don’t want to redact everything because the model will lose value. But I don’t need a person’s Social Security number, their religion or their criminal history.”

    “We have to ensure that we are not introducing any known or unknown bias in our model baseline,” she continues. “There are a lot of statistical tests that are available in our toolkit for training or testing models. So when we get the outputs, we can compare results to see if something applies to a general population or just a small sample to avoid problems downstream.”

    Everyone is Responsible for Using AI Ethically
    Sachdev is proud of the strides her organization is making towards data analytics maturity. While there are still departments that don’t understand analytics function, many are making the most of it.

    Leveraging analytics cannot be a standalone function, she says. But at the same time, everyone who uses AI within a business has a role to play with respect to ensuring those systems are applied ethically.

    “There isn’t a single party that can ensure that everything goes well with ethical data,” Sachdev notes. “Achieving this should be part of the CDAO’s strategy and part of leaders’ key responsibilities. Everything should be connected by a common thread.”

    She concludes: “I was in an internal conference, hosted by my department and the data and governance department, where we discussed what ethical AI really is. A lot of deliberate work needs to go into bringing everyone to the party.”

    Key Takeaways

    Consider the ethical implications of each use case. Behaving ethically will often require data scientists to redact unnecessary personally identifiable information (PII) or build explainability into models
    Proactively combat ML bias. Enterprises should develop processes to search for and remediate the many kinds of bias that can lead to unfair model outputs
    Everyone is responsible for using AI responsibly. Stakeholders much be educated on how to get the most out of AI systems and how to do so ethically

    • 27 min
    Ioannis Gedeon, Unicef: Analytics Leaders Must Ask the Right Questions

    Ioannis Gedeon, Unicef: Analytics Leaders Must Ask the Right Questions

    Ioannis Gedeon, Unicef: Analytics Leaders Must Ask the Right Questions

    • 23 min
    Justin Smith: In-between Transformations

    Justin Smith: In-between Transformations

    Justin Smith: In-between Transformations

    • 27 min
    Sean Durkin: What School Won’t Teach You About Being a Data Scientist

    Sean Durkin: What School Won’t Teach You About Being a Data Scientist

    Sean Durkin, Head of Barclays’ Data Solutions Center of Excellence, shares what he’s learnt about making the jump from academia to working as a data scientist
    Today, Sean Durkin is the Head of Barclays' Data Solutions Center of Excellence. But when he started out as a data scientist in 2011, the field was in its infancy. Looking back, he realizes it was naïve to think university had given him everything he needed to survive the world of work.

    In this week’s Business of Data podcast episode, he reflects on the culture shock many data scientists feel when taking their first steps into the business world and why the learning doesn’t stop when you leave university.

    “There's still a great deal of learning,” Durkin says. “And it’s a different kind of learning, because you need to wrap your head around those non-academic, non-technical skills. For example, you need to learn to work with different personalities and to manage internal dynamics, such as people pulling in different directions.”

    “With the help of great mentors, it became apparent to me that it doesn't matter what you can do technically, you still need to develop soft skills,” he argues.

    The Value of Data Science Mentorship
    Mentorship is widely regarded as a way to accelerate career growth. Durkin encourages upcoming data scientists to accelerate their professional development by finding a mentor, embracing failure and making the most of the opportunities their employers offer them.

    He says: “If you’re fortunate enough to find yourself in an organization that offers mentorship or leadership programs, get on the programs! It's worth doing. And like anything worth doing, there’ll be hard work. But I promise you, when you look back, you’ll be glad you went through it.”

    “If your company doesn’t offer mentorship, approach people several levels above you,” he continues. “If your goal is to reach a certain level of seniority, seek guidance from people above that level. Those are the people who’ll probably be on interview panels or the decision-makers for the role that you want to get into. Ask them what they look for in a leader.”

    Trust Your Instincts
    One of the biggest adjustments for those leaving university for the business world, Durkin says, is that there is no answer sheet at work.

    “It’s quite a shift for people who are used to trying to solve problems that someone has already solved perfectly,” he reports. “[At university], your solution will be compared to the one in the textbook. If it’s not the same, you’re penalized somehow. So, you start off at work thinking your solutions will be torn apart.”

    This mindset shift form perfectionism to one that prioritizes delivering some value quickly and improving things iteratively over time can be challenging for fledgling data scientists. But Durkin encourages anyone who may be finding this paradigm shift unsettling to be confident in the decisions they’re making and to remember they were hired for a reason.

    “You won’t always know what the correct answer is,” he says. “Nobody has a crystal ball. But when you were hired, you were declared the best person for the job. It means I stand behind you and the organization is behind you.

    “Expect to make mistakes. But when you look into the organization and you see how things are done, remember that someone made a decision for it to be that way. It’s your job; just go for it.”

    Key Takeaways
    Formal education and work experience complement each other. As different as the two worlds may be, formal education gives you the basis to make informed choices
    Reach out to potential mentors. Professional mentors can help you plan your future and offer the guidance and support to accelerate your career progression
    Make the most of the opportunities available to you. If your organization offers leadership or other professional development programs, make use of them

    • 31 min
    Premal Desai: 2021 Wrap-Up: Taking Stock of The Gym Group’s Data Strategy

    Premal Desai: 2021 Wrap-Up: Taking Stock of The Gym Group’s Data Strategy

    Premal Desai, The Gym Group’s Head of Data and AI, shares some of his key lessons from the past year and how the experience will shape his team’s strategy going forward



    Gyms have been among the hardest hit businesses through the pandemic. Factors such as on-and-off lockdowns and social distancing have tested businesses and left them with valuable lessons.




    In this week’s Business of Data podcast, Premal Desai, Head of Data and AI at The Gym Group, talks about the challenges he’s faced and what lessons he's drawn from them for the future.




    One of Desai’s biggest takeaways during this time includes not only relying more on organizational data but, most importantly, pairing analytics insights with employee feedback to make the most of the information at hand.




    “The power of data comes when you mix the two – it’s the Holy Grail,” he says. “We’re trying to merge the two as much as possible because, while data gives you one perspective, it's not always a holistic view.”




    This feeds into the need for data literacy within organizations such as The Gym Group, something Desai thinks will only grow in importance over the coming year.




    “I’d love to focus more on data literacy,” Desai notes. “It’s something I’m passionate about; bringing different parts of the organization up to speed and convincing others that there’s value in literacy. It means creating an organizational standard of how to use data and thus enable people to do better jobs, enabled by data.”




    Navigating a Tough Environment with Data



    Having a flexible team and finding team members who are comfortable with uncertainty has proved essential throughout the pandemic.




    “[When the pandemic struck] we were trying to predict things,” Desai says. “There was a lot of planning needed around things such as capacity and space utilization while maintaining health and safety standards. It became important to take care of our members using our data.”




    But Desai considers The Gym Group’s ability to lean on data as the thing that helped the company navigate the pandemic best, answering the questions no one had answers to.




    He says: “There were some things that needed us to dig deep into our data landscape and there were things where we were lucky enough to have the info readily to hand. This can be credited to both luck and skill. But the fact that we were able to respond and maintain some agility proved the case that there is value in data infrastructure governance.”




    While working with the data helped The Gym Group navigate 2021’s tough business climate, Desai says that the need for agile team members during this time cannot be understated.




    “Data teams are used to operating in a stable environment, but clearly you need to be ready for a topsy-turvy environment, too,” he concludes. “There are people who can handle that challenge, and those who can’t. This adds to the challenge of selecting new talent as they should be able to deal with ambiguity and remain flexible. These soft skills are proving to be almost as important as having the core skills of a data analyst or a data scientist.”




    Key Takea

    • 24 min

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