Truth Be Known

This episode features an interview with Nasim Khoshkhou, SVP of Analytics and Data at Synchrony, a premier financial services company with one of the industry’s most complete digitally enabled product suites. At Synchrony, Nasim is responsible for driving data-driven decision making across the organization as well as leading both the analytics team as well as the business insights and solutions team. Prior to Synchrony, Nasim spent 12 years at Argus Information and Advisory Services where she started as an Analyst and worked up to become President of Argus Portfolio Management.  In this episode, Nasim discusses her top three priorities as a data leader, personalizing the user experience through data and AI, and how to be a strategic partner to the business so that you’re delivering more than just the baseline. Quotes *”AI has been used heavily in a number of use cases around credit and fraud. And there's always a trade off and an analysis that has to be done, which is how much incremental benefit does it bring to a particular use case, versus the complexity. But more and more because customer interactions are digital and they're more in real time, we can use tools and partnerships that we have in the digital space that enable us to customize to the particular user. What is the most effective, for example, banner ad that they should see? And so, those kinds of things are a really effective use of AI because you absolutely would not be able to, as a human, react quickly enough to interfere with that.” *”Curiosity and creative thinking about how we can apply solutions is really beneficial to delivering more than baseline. Because we don't want to just deliver baseline. We want to deliver something that's helping the business leaders to look around the corner at the next problem.” *”One of the things that's really important for me as a leader is listening. Sometimes it's about listening to your team. Sometimes it's about listening to the client. It internally helps to make sure that we're not making decisions in a silo, or just based on our first instinct of what we think is the right action or right next step. So I think that's really important for leaders.” Time Stamps [2:00] How did Nasim first get involved in data and analytics? [2:51] How is Synchrony a data-driven organization? [3:45] What skills does Nasim look for when hiring for her team? [5:18] How does Nasim’s analytics team act as a business enabler? [9:29] On the road to becoming data-driven, how does Nasim handle change management? [13:42] How does Nasim’s team leverage AI in her work? [17:50] What was the most difficult decision Nasim has ever made in her career?  [20:57] How does Nasim measure success? [23:23] What resources does Nasim use to stay on top of her game? Links Connect with Nasim on LinkedIn Check out Synchrony Connect with Faisal on LinkedIn Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Navid Alipour, founder and CEO of AI Med Global. AI Med Global is focused on Artificial Intelligence Technology that improves healthcare and helps save lives. It’s made up of two companies Navid co-founded: CureMetrix and CureMatch.  Navid is a long-time entrepreneur in the AI space with an emphasis on combining AI and the life sciences, known as Wellness Science. He seeks to identify scientists and domain experts that solve massive pain problems to take to market by building brand new companies. In this episode, Navid describes AI’s role in medical imaging to detect cancer with more certainty, and using AI in precision medicine to match patients to personalized cancer therapies. Navid also talks about responsible use of data and how doctors can save valuable time with new technologies. Quotes “We can never have enough data. And there's that saying that data is the 21st century oil.” “It's important to train your data sets, because at the end of the day, artificial intelligence–as much as some will say the robots are gonna take over the world–we have a ways to go before that happens. You still have to train it. Machine learning is: you feed it the data, you clean it, you process it, and that's why we say garbage in, garbage out. You have to clean the data and collect it in an elegant manner to then train the algorithms to detect what you wanted to detect… You still have to train it to specifically detect what you want to detect, recommend, predict, or forecast.” “If a doctor wants to recommend a three drug combination, there's literally over four and a half million combinations. So it's beyond human cognition to process that. That's what we do, based on the person's lab work, their next generation sequencing lab work. That's our input. And we'll say, out of the millions of combinations, here's the recommended three drug, two drug, one drug combination.” “We have data, we have papers that have been published and we can show definitively that the recommendations we made helped, as oncologists say, increase the progression free survival or the overall survival… That speaks volumes to be able to show that we can detect the cancer, we can detect the heart disease, and of course you get them on medication earlier, that reduces the risk of a cardiac event, a heart attack.” “AI's not gonna replace the doctor, but the doctor using AI will replace the doctor that is not.” “No two snowflakes ever look the same. No two cancers molecularly are ever the same. So we define cancer by the part of the body, the organ it's found in. But really it should be defined by the molecular makeup. And no two will ever be the same. So if no two are ever the same, how can you train a machine learning algorithm? You can't. And so that's where machine learning will never be the AI tool that'll get the best treatment recommendation for that specific patient.” “I'll say that lab work of that cancer, it's like the 23 And Me of that cancer. And so we'll say, based on this person's specific cancer, there's no other kind in the world. And all the drugs available, the algorithm will match. Hence why we named it CureMatch. And we’ll say, here's a recommended combination. It's for that person. It’s true precision medicine.” “But I've heard of examples of a doctor having ChatGPT write a prescription or send a letter to insurance in 90 seconds instead of 30 minutes, right? So if you can give that time back to the doctor at the primary care level to then take care of their patients, which is their true passion, not filling out paperwork in clicks and clicks and clicks. That's huge. That time is priceless.” “Doctors see this and they see the results. And the fact is that an AI algorithm doesn't need a coffee break. It doesn't get distracted. It doesn't have a food coma after lunch. It doesn't get tired. And so that's where you marry the HI, the human intelligence, and the AI, the artificial intelligence, and that's gonna help deliver better care. We have a shortage of doctors, by the way. I wouldn't be concerned about doctors losing their jobs. They're gonna use this technology to do their job better and more efficiently.” Time Stamps [00:16] Navid’s background [02:14] Data’s role today [03:13] Why it’s important to train data sets [05:08] How can we trust AI technologies? [06:34] Using AI for precision medicine [08:54] Data frameworks [11:12] How Navid uses augmentative AI [15:17] How Navid uses generative AI [18:07] Measuring health and quality of data [19:26] Regulatory confines affecting healthcare AI [21:44] Main advantages of AI [22:59]  Building tech literacy [25:29] Navid’s challenges as CEO [26:51] How to build a great team [28:26] The best advice Navid’s received [30:21] Navid’s advice for people in his field Links Connect with Navid on LinkedIn Check out CureMatch Connect with Faisal on LinkedIn Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Mayank Goel, VP of Financial Crimes Compliance, Data Management and Governance at MUFG. MUFG is one of the world’s leading financial groups, with over 360 years of history and a global network. As a data governance risk manager, Mayank works in a second line of defense to mitigate risks and solve data problems. With over 13 years in the industry, he can make data tell a story that helps clients make decisions.  In this episode, Mayank describes the three-line defense model for bank risk. He also emphasizes the importance of trusting data, defining policies and procedures, and knowing your business objectives. Quotes “Most banks operate under the three line of defense model. The first line is, generally, what people know as the business or the revenue producing side. The people who make sales and services and interact with our customers on a day-to-day basis. They produce the data the rest of the bank generally uses for their work. The second line is the independent risk management function, consists of various risk disciplines, such as credit risk, market risk, financial crime risk, and so on. Both first and second line generally roll up to the chief executive. The second line provides the policies and procedures framework under which the business is expected to operate under. The third line, which is internal audit, is independent. It provides independent oversight to the first and second line, and generally reports directly to the board. As you can imagine, it's a human and regulated industry. All this is a very formally documented and understood governance structure with each line playing their own role and having their own set of responsibilities." “Trusting data is probably the most important thing that we have worked towards. If you don't trust the data that you are using, you're not gonna be able to trust the analysis, defend our decisions, and satisfy our mandates that we have. Trust in data is, in fact, the most important.” “I'd say in my experience, essential components would be having documented and agreed upon policies and procedures. That includes frameworks for establishing accountability, consequence management, pros and responsibilities, having ways to escalate matters as needed. Second would be well-defined business objectives, with the right messaging from the key stakeholders as to why they need governance or why they feel governance is important. The third would be having ways and means to measure and monitor your data quality, and then putting the right set of controls, then going back to the previous question around establishing that trust in data and then making it fit for purpose. And last, I'd say is the change management aspect. How you keep up with the changes in your infrastructure, in your piping of the data and then making sure it's always trustworthy. I'd say those could be the four most important things.” “One way to think about the BCBS framework of aggregation and reporting in banking is: you have a data production layer, right? People who originate a loan for a customer. Then you have your aggregation systems such as your warehouses, or maybe a data lake, where you bring everything together. Then you pick what you need and transform it in various ways. Then you make it relevant for any sort of reporting and consumption based decision that happened in that consumption layer. So, in a risk-based approach, each of these layers has controls that they're responsible for implementing and that are relevant for them.” “The subjective measure of success, in my opinion, is things such as, ‘Were you able to improve your data culture? How successful were you in bringing along the key stakeholders? To close off the project or to get a win.’” “Who do I need to bring along for this journey and how do I bring them along? Knowing who you need, and then secondly, knowing what they need. Knowing their problems and then tying them to your objectives.” “Overcommunicate. Don’t make assumptions of what you said, and what the other side understood. Be very transparent and open. Just lots of deep breathing when things are going wrong. And then try to keep yourself calm and if you do yoga, maybe do that, or whatever keeps you calm… You try and control what you can control.” “In my early career, I was quite hesitant to learn about the audit world. I fought a lot to not be part of the world. But everything I do today has some foundations in what I learned in my early career. And I think it's made me quite successful because of that. So don't be afraid of doing something that you have very little idea of.” Time Stamps [01:17] Mayank’s start in data [03:06] Mayank’s role at MUFG [03:33] The three line defense model [05:01] Breaking down financial crimes [06:03] The importance of trusting data [06:31] Essential components of governance [08:10] Where self-service is heading [09:31] What is the BCBS framework? [11:45] Obstacles to becoming more data-driven [14:08] How to prioritize data projects [15:12] How Mayank measures success [19:26] Handling pressure when stakes are high [21:22] Mayank’s career advice [23:12] Staying on top of your game Links Connect with Mayank on LinkedIn Check out MUFG Connect with Faisal on LinkedIn Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Daniel Marovitz, Senior Vice President of Booking.com’s recently established Fintech unit. You probably know Booking.com as one of the world’s leading digital travel companies, with over 28 million accommodation listings, including homes, apartments and other unique places to stay.  There, Daniel is responsible for facilitating easy and seamless access to Booking.com’s marketplace across borders and currencies. Over his more-than 20-year career, Daniel has established himself as a global leader in data. He served as co-founder and CEO of software startup Buzzumi, which has since been acquired by F1000.com. He has also held leadership positions at Deutsche Bank, Gateway, and iVillage, which he helped take public in 1999.  On this episode, Daniel discusses how he used data visualization to better understand client relationships and improve conversion rates, Booking.com’s approach to data experiments, and how they’re facilitating their cloud migration. Quotes *”Investment banking is like consulting a balance sheet. And a lot of what makes investment banking work are relationships. And relationships always feel like quite a qualitative topic. But you always want to find a way to make those kinds of problems quantitative, not qualitative. So we started to think about the heuristics of relationships and how that might be represented, and what was the source of data we could use.” *”There has long been this quite opaque internal marketing culture that exists at banks all over the world. I think there's kind of this veneer of evanescence, something magical, about the client relationships and how well we know each other and how much we can depend on those relationships. And I think what was great about it is that we were able to actually get really quantitative about performance and also try to figure out how many relationships does it take to be successful? You know, is there a correlation between very close relationships and more deals or higher value deals, or faster time to actually execute deals.” *”I think it's something that maybe people don't talk about enough, which is that with a data-driven, analytics-driven culture from an engineering and product perspective, which is not married to a culture of kind of human openness, you lose a lot of ground. I think you lose a lot of value. So I think it starts with the kind of human engineering of making people feel comfortable and safe to share information and failure Is also important, right? Most experiments fail dramatically. Not a little bit. Like most experiments are a colossal failure. And that's okay. What's not okay is not to test. And what's even less okay Is to test, fail, and hide.” *”This culture of onboarding, like building an onboarding plan for somebody, think through what do you want them to learn? Who do you want them to work with? What do you want them to understand?” *”[Data] is a space where you will never know what you're talking about. It's impossible, even, just in payments. If you ignored regulation or insurance, or other aspects, you can never fully know what you're talking about. There's so much depth and so much detail. And so I think one is just to be respectful of that, right? Just be respectful of the fact that you're gonna have to keep studying. You're gonna have to ask questions and be very comfortable saying, ‘You know what? I don't know. Can you explain it?’ I think it's a good lesson for life, but certainly it's good for a FinTech career.” Time Stamps [2:11] Get to know Daniel [2:57] Daniel’s path into the field of data [5:25] How do you use data visualization for better decisioning? [7:13] How do you identify the highest value customer relationships? [10:56] The Booking.com approach to running data experiments [16:42] Building a truly data- and analytics-driven culture [18:14] How Booking.com ensures data literacy with new hires  [19:37] The complexities of working across a two-sided market [24:26] Understanding the growing importance of data in business [28:50] Why choosing a CDO with an unconventional profile can lend itself to business growth [35:36] Daniel’s advice to anyone entering Fintech for the first time Links Connect with Daniel on LinkedIn Follow Daniel on Twitter Check out Booking.com Connect with Faisal on LinkedIn Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Anita Gandhi, Vice President of Analytics at SmileDirectClub, leaders in oral care and creators of the first telehealth platform for orthodontia. Anita has been with SmileDirectClub since 2019. Prior to SDC, she served as Head of Analytics at FreshDirect. On this episode, Anita talks about how she would rework the data structure at SmileDirect Club if she could start fresh, enabling data self-service among the SDC analysts, and how she helps strengthen her analytics team. Quotes *”My number one thing about building trust in the data is admitting when it's wrong. I have seen people go down in flames[… ]by defending the data till the end and then it turning out to be wrong. Data is imperfect. It is built by human beings, and there are lots of reasons why it can go wrong. And when there's a question about the data, if you approach it as, ‘Well, it's possible. Let's look into it,’ and when there is a problem, being very communicative and accepting that there was something wrong with the data, then people start to trust you when you say, ‘This data is right.’” *”Anyone on the analytics team who got a request from the CEO would drop everything that they're doing and work on that. And we had a lot of trouble finishing projects. So it's been building the confidence in the team, and the leaders on my team to ask that question when something comes in from leadership, ‘Hey, we wanna know this,’ or ‘Hey, we wanna do this,’ and say, ‘We can do it right now. Is it more important than finishing this project? Or is it something that can wait for a couple of days for us to get to it?’ And I think really debunking the idea that everything is urgent and putting a pin in false urgency is what really helps you make that happen.” Time Stamps [2:20] What was Anita’s path to VP of Analytics at SmileDirectClub?  [5:05] How is SmileDirectClub data-driven? [7:12] How does Anita build trust in the data? [11:15] What kind of challenges is Anita navigating as VP of Analytics at SDC? [13:54] How is Anita powering smart decisions at SDC through data analytics? [17:29] How does the analytics team navigate prioritizing data projects, especially with requests from leadership? [19:04] What are the issues with catering to self-service among a data team? [26:29] Why is it important to let the people on your team fail? [29:02] How does Anita stay on top of new information in the data field? [30:15] What advice would Anita give someone going into her role for the first time? Links Connect with Anita on LinkedIn Check out SmileDirectClub Connect with Faisal on LinkedIn Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This is a special episode in which Rob passes the torch to our new host, Faisal Khan. Faisal is VP of Business Applications, Data and Analytics at Talend. Prior to his current role, he was leading Talent's Global Customer Success team and managing the digital and customer experience. He has 20+ years of experience in the Data Management & Analytics domain. He has held leadership roles at Informatica and IBM prior to joining Talend. He is an experienced technology evangelist focused on delivering successful digital transformation initiatives with emphasis on Cloud Data Management & Analytics.

A new season of Truth Be Known is coming your way. Talend is bringing you all-new insights from top data leaders as they crack the code of what it means to be truly data-driven.  Philip O’Donnell: You need to understand the data. You need to understand what it can tell you, what it can't tell you, and then you need to figure out what you can do [with it], because that's how you demonstrate value. Kelly Hereid: You can do amazing research and come up with really interesting conclusions. But if you don't have any way to tie it to the needs of your audience, the needs of your end user, it may as well have not have happened. KJ Gupte: When it comes to data, things are needed as of yesterday. And the moment you get data, it is stale. You have data right now, but it has gone light years ahead. So you have to be extremely fast and evolve with the data. And this season we’re introducing our new host, Faisal Khan. Faisal is VP of Data and Analytics at Talend. He’ll be turning to the biggest names in financial services, retail, healthcare, and more, to derive actionable insights that you can take and use in your business. So you can have the most important, trusted and healthy data at your fingertips. Make your data work for you. Welcome to Truth Be Known.

This episode features an interview with Scott Zoldi. He is the Chief Analytics Officer at FICO where he is responsible for the analytic development of FICO's product and technology solutions. Scott is involved in developing new analytic products and applications, and has authored more than 100 patents. His current focus is on self-learning analytics to detect cyber security attacks. On this episode, Scott talks about how to attract and retain world-class data scientists, the importance of following a model governance process, and responsible AI. Quotes *”Responsible AI is really ensuring that you have a level of competence of what it's learned. You can explain it, you can justify it. And when things start to go off the rail, you know how to react.’” *”Machine learning and AI can become callous. Particularly if it's starting to provide value. Very often people don't inspect where it could go wrong. Because when AI is wrong, it's very wrong. Whereas when the human is wrong, it's typically not tremendously wrong.” *”Standard algorithms and capabilities are very highly valued versus the one-off solution for our customer. Because we see that we can get multiples of usage out of these algorithms and it differentiates our entire company, not just one client at a time.” *”There's this constant noise and churn in our lives as CAOs. And it's very easy for that to consume every moment of our workday. Our value is not being a general manager. We can be important people managers and general managers. Our value is actually carving out a bit of time and thinking about how we could make our products, our solutions, the world, the industry, better through collaboration, through science.” *”That's where you're going to drive the most value, is what makes you different than the way you see the world and how you want to approach the problems that you're exposed to.” Time Stamps *[3:22] Scott’s path to CAO *[5:53] The distinction between analytics science and data science *[8:05] The importance of exploration within analytics *[10:04] How Scott applies research findings at FICO *[13:24] About responsible AI *[17:58] Promoting regulation of responsible AI *[21:54] Who owns the operationalization process? *[25:48] How to prioritize responsible AI at your company *[39:28] The best career advice Scott has ever received Links Connect with Scott on LinkedIn Follow Scott on Twitter Check out FICO Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Robert Brown, the Senior Director of Research for the Venture Forward Initiative at GoDaddy. This is his 13th year at GoDaddy, having started as Director of Database Marketing. Prior to GoDaddy, Robert served as Director of Pulte Homes for 9 years. On this episode, Robert talks about tiering data for smarter decisioning, developing intrinsic motivation in employees, and being a successful steward of data and insights. Quotes *”It's very important as a manager to be invested in the career of the people I'm managing. To have these extended one-on-one conversations with people that are on my teams. And not just during review cycles but along the way. What's working well for them? What are their aspirations? How can I be different in terms of the way that I'm engaging with them? What do they need more of, or less of, from me? And I found that that first builds a lot of loyalty, but it's also just made me a better manager.” *”You have to give people an opportunity to fail or succeed in a safe environment. Start with a smaller group. Don't put them in front of the CEO the first time. Don't put them on the big stage. Give them those moments in smaller increments, smaller doses with a more comfortable audience for them to practice and learn and give them that feedback.’” *”I personally had a lot of managers who haven't given me a lot of feedback along the way. And it feels comfortable, but it doesn't make you better. And so how you frame that critique of course matters. But that it's even delivered is a big part of growing people and making them more expert in what they're trying to do. And telling them, ‘That's one way to do it. Here's a different way to potentially do it where I've found some success,’ without bashing somebody over the head and saying, ‘Here's the way I want you to follow this template.’ To me, that doesn't teach people. That just turns them into automatons or robots, of following somebody else's dictate or even personal style.” *”Step back and try not to control the individual. Just give them a broad target. Say, ‘Here's the goal,' and let them have some creativity. Let them do some experimentation within that broad framework of the outcome you're trying to get to.” Time Stamps *[6:26] How GoDaddy uses data to shift the global economy *[6:51] What is Venture Forward? *[12:34] How does Venture Forward work? *[15:31] Stitching together data to influence policy makers *[19:21] Branching into the UK *[21:59] GoDaddy’s journey to becoming data-driven *[30:57] How Robert Brown leads high performance teams *[48:25] The importance of experimentation to progress Links Connect with Robert on LinkedIn Check out GoDaddy.com Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Philip O’Donnell, Group SVP of Global Data Platforms at the Adecco Group, the world’s leading talent advisory and solutions company. Philip has 13 years of experience in data analytics leadership and strategy consulting across a variety of industries. Prior to the Adecco Group, Philip served as Director of Data Science at Lee Hecht Harrison. On this episode, Philip discusses managing data at a big enterprise, how to prevent business decisions based on bad data, and turning data into dollars. Quotes *”Just showing data to people leaves it up to their interpretation. And that's not usually the value you're providing. You're there to communicate something to them. And we talk about things like data storytelling or crafting narratives with the data, because data by itself is just too unhelpful. You really have to turn that into something that people can understand, and that's a different skill set than it is to just analyze and reproduce the same information.” *”Most large firms are struggling with knowing that it's better to have all this data in the same place. But that's really hard, and it takes investment, and it takes time, and it takes executive commitment and buy-in, and I'm blessed to be able to have that Adecco. They have really put the focus on, ‘Let's figure this out.’ Like, ‘We know it's hard. We know it's not easy, but let's do it. Let's make sure that we're dedicating the resources to do it.’” *”It was always the question of how much do you trust the data that you're getting? As data professionals will tell you, we don't create the data. We're getting it, we're interpreting it, we're reading it, we're organizing it, we're structuring it. But we don't create it. Something else creates it, some sort of business process. And I'm not in charge of that. So in some cases, the data quality is, did the report refresh on time? Now that's the kind of data quality that we, as data, professionals should be able to own. But the other kind of data quality is, did someone enter it in the system correctly? And we can't really control that. But what we can do is give visibility to whether or not that's happening correctly.” *”It's one thing for a report to be wrong. It's a different thing for you to tell the person that report is wrong so that they don't use it to make a decision. And then there's some sort of a process that's correcting it. And I think that's where we have to try to focus, is it’s a very realistic assessment of what our scope can be as data professionals. And we mostly focused on informing and visibility. If there are data quality issues, the worst case scenario is that someone makes a decision on bad data.” *”If there's a piece of data that we're asking people for as a part of the process and it's not actually required for the process, [but just] because we want to know it, it's going to be very difficult to have that be high quality [data]. Because there's not an incentive from the person entering it, other than the threat of being yelled at because you didn't do it right… So if you can give ways of providing value to those users with the data that they're putting in, then you create a sort of incentive feedback loop… You have to provide ways of giving people incentive to enter the right information that is actually then helping them do their job better instead of it just being something that management dictates you have to put in.”  *”You need to understand the data. You need to understand what it can tell you, what it can't tell you, and then you need to figure out what you can do [with it], because that's how you demonstrate value.”  Time Stamps [7:09] The role of the data professional [10:09] Consolidating mass amounts of data at a large company [11:07] Risk management and controlling exfiltration [15:18] Proving value and ROI in data [20:50] Understanding incidental data and how to monetize it [24:20] To centralize or to decentralize the data? [29:09] Learning to trust the data [31:40] Incentivizing accurate data input Links Connect with Philip on LinkedIn Check out the Adecco Group Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Dr. Kelly Hereid, Director of Catastrophe R&D at Liberty Mutual Insurance in the Corporate Enterprise Risk Management Group. Prior to Liberty Mutual, Kelly was a research scientist at Chubb in their primary side natural catastrophe unit. She has a Ph.D. in geological sciences from the University of Texas – Austin, focusing on climate science. On this episode, Kelly talks about using historical data to create catastrophe models, taking a strategic standpoint to invest in resiliency, and reducing vulnerability to changing hazards. Quotes *”There is a huge need in the financial sector to try and capture [climate] risk. Which means there's a lot of information that's available, but you also have to be able to sift through what's there and interpret it appropriately to make sure that you're not just ending up with perhaps nonsense just because the data is available does not necessarily mean that it's data that you can use.” *”If I have someone who's trying to say that they have a model that will tell me down to the cent what your hurricane losses will be in 2050, I would want to have some questions about that because I know that the scientific confidence in changes in hurricane frequency is comparatively low. We operate in a field that includes a lot of data, but also includes a lot of uncertainty. So we need to be really comfortable operating in a space of uncertain and developing science.” *The Tubbs Fire in Santa Rosa, at the time, the most expensive and destructive California wildfire in history in the past has almost exactly the same footprint as another fire called the Hanly Fire that happened in the 1960s. Literally exactly almost identical. The only difference is nobody knows about the fire in the sixties, because at that time there was no one living in the area. So it's that it's a change in hazard, but some of these are areas that have always burned, but now there are people there who are at risk.” *”Catastrophe models allow us to look at historical events in the context of where people live today. So you might run, say, a historical wildfire footprint, but run it with where people are actually living today. So you can be able to see those changes that are driven by exposure, by people moving to different areas. On my team, we also do things like stress tests, where we take the model as it stands. And we can do ‘what if’ experiments, because each one of these events in the model has some characteristics of the event; how big it is, severity, spread, but it also has an assumed frequency. So we can say what would happen if you doubled the frequency of this particular characteristic of event, or tripled it? What kind of impact could that potentially have to sort of our overall book of business, our overall loss potential and damage potential from wildfires? So as a tool, we don't just use it at face value. We can use it as a place to drop, to run and drive experiments that can allow us to explore future climates when we don't necessarily know what that percent change is going to be.”  *”You can do amazing research and come up with really interesting conclusions. But if you don't have any way to tie it to the needs of your audience, the needs of your end user, it may as well have not have happened.” *”We can't prevent disasters from perhaps we can, we can't prevent hazards from happening, but we do have the opportunity as a society to prevent more disasters. Because disaster is an intersection between hazard and a community.” Time Stamps [2:03] The path to becoming Director of Catastrophe Research and Development [3:44] What are catastrophe models? [12:44] Studying wildfires and why they have become more hazardous [19:59] How to change your risk profile [20:57] How updated building codes can affect your business resiliency [28:12] The role of the modeling team [32:51] Data integrity and challenges in modeling hazards Links Connect with Kelly on LinkedIn Follow Kelly on Twitter Check out Liberty Mutual Insurance Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Dr. Catherine Williams, Global Head of IQ at Qualtrics, an experience management software platform. Catherine has extensive background in data science and quantitative analytics. Prior to Qualtrics, Catherine served as Chief Data Scientist and Chief Data and Marketplace Officer at AppNexus and Xandr, which is part of AT&T. Catherine holds a Ph.D. in Mathematics from the University of Washington. She has also held postdoctoral fellowships at Stanford and Columbia Universities. On this episode, Catherine discusses growing a machine learning approach to unstructured data, using data to get to the “why” of customer behavior, and following your local happiness gradient. Quotes *”When you think about companies trying to operate, they derive lots of data about what humans are doing, their transactions and touch points with those humans. And that's really valuable information. There's great infrastructure for understanding that and operating on that. I think that's what people often mean when they say data-driven decisions. What are the objective facts of those interactions with the humans in your business? But that's missing a huge component, right? A lot of what drives human behavior is their subjective experience and that doesn't enter into the equation very often.” *”Companies have measured customer satisfaction or the Net Promoter Score, which is how likely are you to recommend this business or product to a friend or neighbor. And so they try to get some sense of how people are feeling, but that's just the tip of the iceberg. And so what we're doing is just providing tools to derive a lot more information and really understand the why's of your behavior. Not just, your customers did these things, but why did they do those things? How are they feeling about it? How might that affect their loyalty or their future purchasing behavior?” *“[My] graduate advisor told me to follow my local happiness gradient, which is a very nerdy math way of just saying ‘Follow your passion,’ but somehow framing it in math terms made it feel more authoritative to me. Later in my career, a wise CIO reminded me to always find those things in your day to day that nourish you. I’ll put those two together and make one piece of career advice that has been meaningful for me, is just to find those things that light me up and energize me and, and find that thing, that passion. I do think that finding and following that passion, following that joy actually leads to better career results.” Time Stamps *[5:20] Using NLP to derive insights from unstructured data *[10:37] The importance of testing your own CX *[11:51] Qualtrics’ journey to becoming data-driven *[14:18] Promoting data literacy among employees *[22:54] Understanding the “why” of customer behavior through data *[27:39] Advice for people entering a career in AI Links Connect with Catherine on LinkedIn Check out Qualtrics Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Tom Edwards, Chief Digital and Data Officer at Omnicom Health Group, the largest healthcare marketing and communications network in the world. Prior to Omnicom, Tom served as Chief Digital and Innovation Officer at Epsilon. Tom has been named one of the Top 50 Most Influential Business Leaders in Technology and a Top 10 Global Marketer Award winner by OnCon this year. On this episode, Tom talks about transparency in decision making, how to organize massive amounts of data in order to derive insights, and how to determine the ideal communication strategy for a target audience. Quotes *“You want to connect the vision coming from the top to how that's practically going to be rolled out across those different entities, and connecting the expediter to the vision and pulling that completely through. And then going and driving towards quick wins. Like that ultimately is the core driver of adoption. People have to see it in action. What is it actually doing for the business? How can this help me be faster to market, you know, ahead of my competition.” *“Communication is key. Making it easy and consumable is probably the other. There tends to be this stigma with data, digital and technology that it's complicated. And in a lot of ways, it is. But one of the core things that you have to do to be able to truly drive adoption across a large organization and transform it as you have to make it consumable. You have your subject matter experts who can go incredibly deep, understand the vernacular and the terminology, understand everything about taxonomy and data hygiene and all the things you need to do. But at the end of the day, if it's not understandable across the various functions within the organization from account to creative disservice or strategy, then it's not going to drive any comprehensive value.” *“Especially when we're going into new areas and new areas of expertise, I’ve learned not to just trust at face value when someone says they can do something. I need to verify the level of competency upfront before we engage on a major initiative and make sure there is a proven track record providing the type of solution we’re looking for.” Time Stamps *[3:38] Merging Digital and Data *[5:10] Making the most out of data assets *[6:38] How to strategically enable agencies *[8:03] Predictive decisioning and the cookieless future *[9:30] Making massive data sets actionable *[11:47] Omnicom’s data architecture and tech stack *[15:27] Creating common language across the organization *[20:13] Looking to partner with leaders in data-driven organizations *[23:14] Preparing for a shifting data landscape *[26:24] The importance of transparency as a leader *[29:23] Finding a mentor and sponsor Links Connect with Tom on LinkedIn Follow Tom on Twitter Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Asha Saxena, Founder and CEO of Women Leaders in Data and AI. sha has 25 years of experience building successful tech businesses. She also serves as a CEO Coach working with women in tech, and she’s an Adjunct Professor at Columbia University, teaching enterprise strategy and data strategy. In this episode, Asha talks about how empowering women in tech means including male allies, investing in a simple user experience for improved adoption rates, and how to earn trust in data. Quotes *”My son asked, ‘Mom, you seem to be doing these multiple things. Where do you really truly get the satisfaction? Why don't you do something or build something for people like you?’ And I said, ‘What do you mean?’ He said, ‘Women in tech or women in data. Have you done anything for that?’ And [at] first I got offended and I was like, ‘What do you mean women? Are you putting me in a box? I'm a professional.’ All my life, I worked really hard for my worth. And I don't think my worth should be just boxed by my gender. [But] it's not about boxing, it's more [about] owning it. You can own that you're a woman in tech and there’re not too many women. And women have challenges because they don't have enough role models on the top."  *”You must have heard about the financial services companies who did this experiment. An algorithm for the high positions - the C-level positions - rejected women’s resumes because the data didn't show that there were enough women [who] could succeed in that position. Because there [were] no women hired in the past. So we don't have enough data, we don't have diverse data, and we don't have women on the top who can help create that change. So what do you do about that?” *“What happens to the women who are on the top and don't have role models? What happens to women who are VP level or C level who don't have [a] peer group? And what happens to the environment? Is the environment ready? I tell you within our organization, I see so many women who are leaving their organization because they get on the top and they realize that the top is not ready for them.” Time Stamps *[4:59] How algorithms become sexist *[7:04] How to support women in positions of power *[12:41] Why support from men is important *[16:28] Facilitating change through simple steps *[19:32] Building trust in data *[25:50] Tips to develop a data strategy *[28:49] Supporting engineers in developing a data strategy *[35:12] How Asha Saxena uses data to make difficult decisions *[38:46] Advice for data leaders Links Connect with Asha on LinkedIn Follow Asha on Twitter Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with KJ Gupte, Data Science Lead at Tradeshift, a cloud-based platform for supply chain payments. KJ has had nearly 15 years of experience in the industry. Before Tradeshift, KJ served as Data Analytics Manager at PwC where she led a team of engineers and developers in making data-centric products for customers like Apple, Google, and HP. She is also a graduate of the Harvard Business Analytics Program, and says her experience in the Data Science Pipeline and Critical Thinking course felt like rewiring her brain to think about data in new ways. In this episode, KJ discusses translating technical data for her stakeholders, building credibility in data among the C-suite, and being a trailblazer in the supply chain economy. Quotes *“I think what has helped me drive success is being extremely collaborative with the team and extremely transparent. Because many times when you are transparent and you exchange your thoughts, that's where you get the most gains. So I might be thinking about data from an angle and the users might be thinking about it from a very different angle. And a lot of exchange needs to happen. Because not everybody looks at the data in the same way. Everybody comes with a very different skill set, whether you are an engineer, whether you are a data scientist, whether you are a CEO, CFO, everybody has a core skillset that they bring to the table. It's not always the same or uniform. So collaboration and transparency is always the key.” *“We are absolutely data-driven right now. I'm very glad I was hired at the point that I was, because that's when we were scaling up. And that's when we were sitting on like chunks of data that was so potent that it was just a matter of using it. And in fact, we are sitting on buyer and seller supply chain data, which is the core of the supply chain disruptions that are going on. So we are in so many ways trail blazers to the whole supply chain economy.” *“The leadership knew that there was a lot of work that needed to be done in data. So my core job was to actually convey that message. Initially I spent a lot of time giving presentations on just what we had, not going into complexities. But, um, presentations around, okay, this is what we have. This is the story that data is telling. And just making people curious, you know, making people interested about data. Because if, if your data is not telling a story, it is just numbers.” *”When it comes to data, things are needed as of yesterday. And the moment you get data, it is stale. You have data right now, but it has gone light years ahead. So you have to be extremely fast and evolve with the data. So that was definitely a challenge, but what helped me is I had a lot of visibility. Right from the time I was hired, I was directly working with leadership. I was working with engineering to see the lay of the land, I was working with my CEO, my CFO, I was working with the head of engineering to see what they were talking about, to understand the language.”  Time Stamps [8:34] Driving success through collaboration and transparency [14:28] Tradeshift's journey to becoming data-driven [19:05] Getting visibility on the freshest data [24:08] How to build credibility and trust in the data from company executives [29:24] How to deriving more value for customers [34:47] Under Pressure: How KJ Gupte makes difficult decisions [38:39] Advice to budding data scientists Links Connect with KJ on LinkedIn Check out Tradeshift Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Tom Edwards, Chief Digital and Data Officer at Omnicom Health Group, the largest healthcare marketing and communications network in the world. Prior to Omnicom, Tom served as Chief Digital and Innovation Officer at Epsilon. Tom has been named one of the Top 50 Most Influential Business Leaders in Technology and a Top 10 Global Marketer Award winner by OnCon this year. On this episode, Tom talks about transparency in decision making, how to organize massive amounts of data in order to derive insights, and how to determine the ideal communication strategy for a target audience. Quotes *“You want to connect the vision coming from the top to how that's practically going to be rolled out across those different entities, and connecting the expediter to the vision and pulling that completely through. And then going and driving towards quick wins. Like that ultimately is the core driver of adoption. People have to see it in action. What is it actually doing for the business? How can this help me be faster to market, you know, ahead of my competition.” *“Communication is key. Making it easy and consumable is probably the other. There tends to be this stigma with data, digital and technology that it's complicated. And in a lot of ways, it is. But one of the core things that you have to do to be able to truly drive adoption across a large organization and transform it as you have to make it consumable. You have your subject matter experts who can go incredibly deep, understand the vernacular and the terminology, understand everything about taxonomy and data hygiene and all the things you need to do. But at the end of the day, if it's not understandable across the various functions within the organization from account to creative disservice or strategy, then it's not going to drive any comprehensive value.” *“Especially when we're going into new areas and new areas of expertise, I’ve learned not to just trust at face value when someone says they can do something. I need to verify the level of competency upfront before we engage on a major initiative and make sure there is a proven track record providing the type of solution we’re looking for.” Time Stamps *[3:38] Merging Digital and Data *[5:10] Making the most out of data assets *[6:38] How to strategically enable agencies *[8:03] Predictive decisioning and the cookieless future *[9:30] Making massive data sets actionable *[11:47] Omnicom’s data architecture and tech stack *[15:27] Creating common language across the organization *[20:13] Looking to partner with leaders in data-driven organizations *[23:14] Preparing for a shifting data landscape *[26:24] The importance of transparency as a leader *[29:23] Finding a mentor and sponsor Links Connect with Tom on LinkedIn Follow Tom on Twitter Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

This episode features an interview with Dr. Catherine Williams, Global Head of IQ at Qualtrics, an experience management software platform. Catherine has extensive background in data science and quantitative analytics. Prior to Qualtrics, Catherine served as Chief Data Scientist and Chief Data and Marketplace Officer at AppNexus and Xandr, which is part of AT&T. Catherine holds a Ph.D. in Mathematics from the University of Washington. She has also held postdoctoral fellowships at Stanford and Columbia Universities. On this episode, Catherine discusses growing a machine learning approach to unstructured data, using data to get to the “why” of customer behavior, and following your local happiness gradient. Quotes *”When you think about companies trying to operate, they derive lots of data about what humans are doing, their transactions and touch points with those humans. And that's really valuable information. There's great infrastructure for understanding that and operating on that. I think that's what people often mean when they say data-driven decisions. What are the objective facts of those interactions with the humans in your business? But that's missing a huge component, right? A lot of what drives human behavior is their subjective experience and that doesn't enter into the equation very often.” *”Companies have measured customer satisfaction or the Net Promoter Score, which is how likely are you to recommend this business or product to a friend or neighbor. And so they try to get some sense of how people are feeling, but that's just the tip of the iceberg. And so what we're doing is just providing tools to derive a lot more information and really understand the why's of your behavior. Not just, your customers did these things, but why did they do those things? How are they feeling about it? How might that affect their loyalty or their future purchasing behavior?” *“[My] graduate advisor told me to follow my local happiness gradient, which is a very nerdy math way of just saying ‘Follow your passion,’ but somehow framing it in math terms made it feel more authoritative to me. Later in my career, a wise CIO reminded me to always find those things in your day to day that nourish you. I’ll put those two together and make one piece of career advice that has been meaningful for me, is just to find those things that light me up and energize me and, and find that thing, that passion. I do think that finding and following that passion, following that joy actually leads to better career results.” Time Stamps *[5:20] Using NLP to derive insights from unstructured data *[10:37] The importance of testing your own CX *[11:51] Qualtrics’ journey to becoming data-driven *[14:18] Promoting data literacy among employees *[22:54] Understanding the “why” of customer behavior through data *[27:39] Advice for people entering a career in AI Links Connect with Catherine on LinkedIn Check out Qualtrics Connect with Rob on LinkedIn Follow Rob on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com

In this short episode,  Lauren is passing the torch to our new host, Rob Norman.  Rob is the Head of EMEA Marketing at Talend. Links Check out highlights from the last season: Taking Charge of Your Health Through Data with Unmesh Srivastava, CTO at P3 Health Partners How Technology Plays a Part in our "New Normal" with Wendy Pfeiffer, CIO at Nutanix Creating a Culture of Excellence with John Foley, Former Lead Pilot of the Blue Angels Empathy and Data go Hand in Hand with Karl Hightower, VP of Enterprise Information Management and Chief Data Officer at Novant Health

A brand new season of Truth Be Known is coming. Talend is excited to bring you insights from top data leaders about how they make hard decisions on tight deadlines and with imperfect information. Lyssa Myska Allen: “No longer are we looking for like, where's our data or what's our data. We're trying to figure out what in the world to do with this data.” Randy Bean: “We're really at the beginnings in many respects of the, of the data revolution.”  Alana Winter: “We'll never have all the data and we'll never have all the information. Make your best guess based on what you have.”  We’re also welcoming our new host, Rob Norman, Head of EMEA Marketing at Talend. He’ll be talking with some of the brightest minds in data, including CDOs at some of the top technology companies in the world. They’ll explore the pivotal decisions they’ve made in leading data-first organizations, the lessons they’ve learned, and how they seek truth in an uncertain world. So join us as we dive deep into data and how it’s shaping the modern world. Welcome to Truth Be Known.

This episode features an interview with Ben Kepes, a professional board member, consultant, business owner and angel investor.  In this episode, Ben talks about how he chooses startups to fund, the overall importance of simple human connection, and finding patterns in data that lead to success. Quotes *“I remember talking to a friend once about what makes an angel investor successful or unsuccessful. And his thing was that it simply comes down to pattern matching. You see enough of these companies and you can identify the patterns that predict success. And to me that is all about data. It's taking insights from the myriad of different data types that we have available to us.  And what's really exciting is that today, that is able to be done by anyone because of the proliferation of data platforms, and because storage and processing of data is so much cheaper and more accessible. ”  *“I think the thing that I have observed as an investor, as an observer and analyst of the tech space, is that there are so many entrepreneurs who deeply, deeply understand tech, but deeply, deeply don't understand people. And that just doesn't work. You have to understand the people you're selling to, the people that are going to be using the product and connect with individuals.” *“Leveraging data is very, very important as part of a toolkit that can then augment what you can do as an individual. It doesn't replace what we can do as individuals.” *“Data has been used for different reasons, but fundamentally the great thing about data platforms and technology, computer based data mining is that the infrastructure is the same. It's about getting lots and lots of different pieces of structured and unstructured data into a great big database and deriving some insights from that. So I really liked this notion of a great, big data lake and the ability to look at that data with different lenses. And I think that's the really exciting thing that we're seeing in our world. The ability for anyone within the business, within an organization to query data in a way that is meaningful and will derive insights that are meaningful for them.” Time Stamps *[5:23] Looking for patterns for success *[6:26] The right patterns to look for *[9:00] Data does not replace people *[10:51] Deriving insights across the tech industry *[15:19] Combining data with human knowledge for smarter results *[18:31] Looking at both qualitative and quantitative data *[23:53] The next bastion of data analytics *[30:27] How to make better decisions Links Connect with Ben on LinkedIn Follow Ben on Twitter Connect with Lauren on LinkedIn Follow Lauren on Twitter Thanks to our friends Truth Be Known is brought to you by Talend, a leader in data integration and data integrity, enabling every company to find clarity amidst the chaos. Talend Data Fabric brings together in a single platform all the necessary capabilities that ensure enterprise data is complete, clean, compliant, and readily available to everyone who needs it throughout the organization. Learn more at Talend.com