Dataframed

The most common application for data science is to solve problems within your own organization, and as professionals become more data literate, they rely less and less on others to solve their problems and unlock professional growth and career advancement.But in the world of consulting, data science is used to solve other people’s problems, which adds an additional layer of complexity since consultants aren’t always given all of the tools they need to do the job right.Enter Pratik Agrawal, a Partner at Kearney Analytics leading the automotive and industrial transportation sector. In this episode, we are taking a look at how data science is applied in the consulting industry and what skills are critical to be a successful data science consultant. As a software engineer and data scientist with over a decade of experience in the consulting world at companies like Boston Consulting Group and IRI, Pratik has a deep understanding of how to navigate the industry and how data science can be leveraged in it, as w

One of the toughest parts of any data project is experimentation, not just because you need to choose the right testing method to confirm the project’s effectiveness, but because you also need to make sure you are testing the right hypothesis and measuring the right KPIs to ensure you receive accurate results.One of the most effective methods for data experimentation is A/B testing, and Anjali Mehra, Senior Director of Product Analytics, Data Science, Experimentation, and Instrumentation at DocuSign, is no stranger to how A/B testing can impact multiple parts of any organization. Throughout her career, she has also worked in marketing analytics and customer analytics at companies like Shutterfly, Wayfair, and Constant Contact.Throughout the episode, we discuss DocuSign’s analytics goals, how A/B testing works, how to gamify data experimentation, how A/B testing helps with new initiative validation, examples of A/B testing with data projects, how organizations can get started with data experimentation, and muc

Perhaps the biggest obstacle to establishing a data culture is building trust in the data itself, making it vital for organizations to have a robust approach to data governance to ensure data quality is as high as possible.Enter Laurent Dresse, Data Governance Evangelist and Director of Professional Services at DataGalaxy. Throughout his career, Laurent has served as a bridge between IT and the rest of the business as an expert in data governance, quality, data management, and more. Throughout the episode, we discuss the state of data governance today, how data leaders and organizations can start their data governance journey, how to evangelize for data governance and gain buy-in across your organization, data governance tooling, and much more.

A special announcement from the DataFramed team. Join us for RADAR, a free two-day digital event curated to equip businesses and individuals with the insights to thrive in the era data, coming to you March 22-23, 2023! Register here to secure your spot!

Data quality can make or break any data initiative or product. If you aren’t able to collect data that is accurate, or you have data sets that have varying structures, or are filled with typos and other issues caused by human error, then the chances drop drastically that your data models will be accurate, or even helpful.When it comes to healthcare, data quality can be an absolute nightmare. With so many different data sources, high data churn rates, and a lack of standardization in many different healthcare categories, it can seem impossible to make quality healthcare more easily accessible to people when they need it.Ribbon Health seeks to change that by using AI to improve the quality of healthcare data and create a data platform with actionable provider information including insurance coverage, prices, and performance.Today’s guests are Nate Fox, the CTO, Co-Founder, and President of Ribbon Health, and Sunna Jo, a former pediatrician who is now a data scientist at Ribbon Health.Throughout the episode, we

When working with data, it’s easy for us to think about it as a mechanistic process, where data comes in and products come out. But as we’ve explored throughout the show, succeeding in data, whether you’re a data leader looking to build a data culture, a data scientist ascending the ranks, or even a policy maker looking to have an impact with data, the human side is crucial.At the heart of the “human side” is empathy— whether it’s for your stakeholders if you’re a data scientist developing a dashboard for them, empathy for your workforce if you’re a data or learning leader, or empathy for the planet and your citizens if you’re a policy maker. So how can we all practice better empathy? Specifically, can we all practice better data empathy? Luckily, empathy is a muscle that can be built. It’s not a “you have it, or you don’t” type of skill. So how can individuals and organizations utilize data empathy to improve how they work with data and the success rate of their projects?  Enter Phil Harvey, a

Something we talk about alot on DataFramed is the importance of data literacy and data skills — and how they help both individuals and organizations succeed with data. Oftentimes, when organizations engage in upskilling programs on data literacy, one of the common pushbacks people have is, “I am not a numbers person”. So how do you move past that? How can leaders help their people bridge the data literacy gap, and in turn create a data culture?That’s where Dr. Selena Fisk comes in. Fisk is a data storyteller, coach, and thought leader in the data industry. She works in both the corporate sector and in education to develop data-led strategies that can help organizations grow. Fisk mainly specializes in the areas of data literacy, data visualization, and data storytelling, and is the author of three books, “Using and Analysing Data in Australian Schools,” “Leading Data-Informed Change in Schools,” and “I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World.”Throughout our conversation, w

Throughout 2022, there was an explosion in generative AI for images and text. GPT-3, DALLE-2, pointed us towards an AI-driven future. Recently, ChatGPT has taken the (data) world by storm — prompting many questions over how generative AI can be used in day to day activities. With the incredible amount of hype surrounding these new tools, we wanted to have a discussion grounded in how these tools are being operationalized today.Enter Scott Downes. Scott is the CTO of Invisible Technologies, a process automation platform that uses GPT-3 and other generative text technologies. Scott joins the show to talk about how organizations and data professionals can maximize the potential of these tools and how AI and humans can work together in a complementary fashion to optimize workflows, reduce time-intensive, tedious tasks, and do higher quality work. Scott has a decade of experience in technology, product engineering, and technical leadership, making a veteran in training and mentoring employees across the organizati

In 2022, we saw significant developments in the field of data. From the emergence of generative AI to the growth of low-code data tools and AI assistants—these advancements signal an upcoming paradigm shift, where data-powered tools and machine learning systems will radically transform workflows across various professions.2022 also saw digital transformation remain a major theme for organizations across industries as they sought to embrace new ways of working, reaching customers, and providing value. As 2023’s looming economic uncertainty puts pressure on organizations to maximize ROI from their investments, digital and data transformation will continue to be one of the key levers by which organizations can cut costs and scale value for their stakeholders.So we’ve invited DataCamp’s co-founders, CEO Jonathan Cornelissen and COO Martijn Theuwissen to break down the top data trends they are seeing in the data space today, as well as their predictions for the future of the data industry.Jonathan Cornelissen is t

Just as data is used to help businesses determine new directions, set new goals, and measure progress, data can be used in everyday life to help people do the same as they seek to improve themselves.As the new year arrives, many people are thinking about new goals and new ways to improve their lives, so we have invited Gary Wolf to the show to explore how you can use data-driven thinking to drive meaningful changes in yourself.Gary Wolf is the Co-Founder of The Quantified Self, an international community of makers and users of self-tracking tools. Prior to co-founding The Quantified Self, Wolf was a contributing editor for Wired Magazine, where he spent two decades covering the intersection of technology and culture, and his cover story in the New York Times is what introduced the general public to self-tracking as an emerging trend.In this episode, we talk about what The Quantified Self is, why self-tracking projects can be life-changing, how to get started with self-tracking, how to connect with others in t

In programming, collaboration and experimentation can be very stressful, since sharing code and making it visible to others can be tedious, time-consuming, and nerve-wracking.Tools like Power BI are changing that entirely, by opening up new ways to collaborate between team members, add layers of customized and complex security to the data teams are working with, and making data much more accessible across organizations. Ginger Grant joins the show to talk about how organizations can utilize Power BI, Dax, and M to their fullest potential and create new opportunities for experimentation, innovation, and collaboration. Ginger is the Principal Consultant at the Desert Isle Group, working as an expert in advanced analytic solutions, including machine learning, data warehousing, ETL, reporting and cube development, Power BI, Excel Automation, Data Visualization and training. In addition to her consultant work, she is also a blogger at and global keynote speaker on developments and trends in data. Microsoft has als

The insurance industry thrives on data from utilizing data and analytics to determine policy rates for customers to working with relevant partners in the industry to improve their products and services, data is embedded in everything that insurance companies do. But insurance companies also have a number of hurdles to overcome, whether it’s transitioning legacy data into new processes and technology, balancing new projects and models with ever-changing regulatory standards, and balancing the ethical considerations of how to best utilize data without resulting in unintended consequences for the end user. That’s why we’ve brought Rob Reynolds onto the show. Rob is the VP and Chief Data & Analytics Officer at W. R. Berkley, a multinational insurance holding company specializing in property and casualty insurance. Rob brings over two decades of experience in Data Science, IT, and technology leadership, with a particular expertise in building departments and establishing highly functioning teams, especially in

With the increasing rate at which new data tools and platforms are being created, the modern data stack risks becoming just another buzzword data leaders use when talking about how they solve problems. Alongside the arrival of new data tools is the need for leaders to see beyond just the modern data stack and think deeply about how their data work can align with business outcomes, otherwise, they risk falling behind trying to create value from innovative, but irrelevant technology. In this episode, Yali Sassoon joins the show to explore what the modern data stack really means, how to rethink the modern data stack in terms of value creation, data collection versus data creation, and the right way businesses should approach data ingestion, and much more. Yali is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. Yali is an expert in data with a background in both strategy and operations consulting teaching compan

2022 was an incredible year for Generative AI. From text generation models like GPT-3 to the rising popularity of AI image generation tools, generative AI has rapidly evolved over the last few years in both its popularity and its use cases. Martin Musiol joins the show this week to explore the business use cases of generative AI, and how it will continue to impact the way the society interacts with data. Martin is a Data Science Manager at IBM, as well as Co-Founder and an instructor at Generative AI, teaching people to develop their own AI that generates images, videos, music, text and other data. Martin has also been a keynote speaker at various events, such as Codemotion Milan. Having discovered his passion for AI in 2012, Martin has turned that passion into his expertise, becoming a thought leader in AI and machine learning space. In this episode, we talk about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, what the future holds, an

Data Analytics has played a major role in Chelsea’s journey to becoming the seventh most valuable football club in the world, Chelsea has won six league titles, eight FA Cups, five League Cups, and two Champions League titles. Today, we are going behind the scenes at Chelsea FC to see how they use data analytics to analyze matches, inform tactical decision-making, and drive matchday success in one of the world’s top football leagues, just in time for the 2022 FIFA World Cup in Qatar! Federico Bettuzzi is a Data Scientist at Chelsea FC. As a specialist in match analytics, Federico works with Chelsea’s first team to inform tactical decision making during matches. Federico joins the show to break down how he gathers and synthesizes data, how they develop match analyses for tactical reviews, how managers prioritize data analytics differently, how to balance long-term and short-term projects, and much more.

To become a data-driven organization, it takes a major shift in mindset and culture, investments in technology and infrastructure, skills transformation, and clearly evangelizing the usefulness of using data to drive better decision-making. With all of these levers to scale, many organizations get stuck early in their data transformation journey, not knowing what to prioritize and how. In this episode, Ganes Kesari joins the show to share the frameworks and processes that organizations can follow to become data-driven, measure their data maturity, and win stakeholder support across the organization. Ganes is Co-Founder and Chief Decision Scientist at Gramener, which helps companies make data-driven decisions through powerful data stories and analytics. He is an expert in data, analytics, organizational strategy, and hands-on execution. Throughout his 20-year career, Ganes has become an internationally-renowned speaker and has been published in Forbes, Entrepreneur, and has become a thought leader in Data Scie

During Data Literacy Month, we shared how data journalists curate and distill data stories to the wider public. Since 2020, Data Journalism has risen both in significance and visibility. Throughout the COVID-19 pandemic, data journalists have been instrumental in keeping the public informed by investigating, challenging, interpreting, and explaining complex datasets. In this episode, Betsy Ladyzhets joins the show to talk about the state of Data Journalism today, and shares from her experience as a data journalist Betsy is an independent science, health, and data journalist focused on COVID-19 and Founder of the COVID-19 Data Dispatch, an independent publication providing updates and resources on public COVID-19 data. She is also currently working as a Senior Journalism Fellow with the Documenting COVID-19 project at the Brown Institute for Media Innovation and MuckRock. Her work has been featured in Science News, FiveThirtyEight, MIT Tech Review, and the Covid Tracking Project. Throughout the show, we discu

Python has dominated data science programming for the last few years, but there’s another rising star programming language seeing increased adoption and popularity—Julia. As the fourth most popular programming language, many data teams and practitioners are turning their attention toward understanding Julia and seeing how it could benefit individual careers, business operations, and drive increased value across organizations. Zacharias Voulgaris, PhD joins the show to talk about his experience with the Julia programming language and his perspective on the future of Julia’s widespread adoption. Zacharias is the author of Julia for Data Science. As a Data Science consultant and mentor with 10 years of international experience that includes the role of Chief Science Officer at three startups, Zacharias is an expert in data science, analytics, artificial intelligence, and information systems. In this episode, we discuss the strengths of Julia, how data scientists can get started using Julia, how team members and

While securing the support of senior executives is a major hurdle of implementing a data transformation program, it’s often one of the earliest and easiest hurdles to overcome in comparison to the overall program itself. Leading a data transformation program requires thorough planning, organization-wide collaboration, careful execution, robust testing, and so much more. Vanessa Gonzalez is the Senior Director of Data and Analytics for ML & AI at Transamerica. Vanessa has experience in data transformation, leadership, and strategic direction for Data Science and Data Governance teams, and is an experienced senior data manager. Vanessa joins the show to share how she is helping to lead Transamerica’s Data Transformation program. In this episode, we discuss the biggest challenges Transamerica has faced throughout the process, the most important factors to making any large-scale transformation successful, how to collaborate with other departments, how Vanessa structures her team, the key skills data scientist

As data leaders continue to fill their talent gap, how should they approach sourcing, retaining, and upskilling their talent? What strategies should data leaders adopt in order to accomplish their talent goals and become data-driven? Kyle Winterbottom joins the show to talk about the key differentiators between data teams that build talent-dense teams and those that do not. Kyle is the host of Driven by Data: The Podcast, the Founder & CEO of Orbition, a talent solutions provider, for scaling Data, Analytics, & Artificial Intelligence teams across the UK, Europe and the USA. As an accomplished expert and thought leader in talent acquisition, attraction, and retention, as well as scaling data teams, Kyle was named one of Data IQ’s 100 Most Influential People in Data for 2022. In this episode, we talk about how data teams can position themselves to attract top talent, how to properly articulate how data team members are adding value to the business, how organizations can accidentally set data leaders up