DataFramed

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 [https://www.linkedin.com/company/ribbonhealth/] 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 [https://www.linkedin.com/in/natfox/], the CTO, Co-Founder, and President of Ribbon Health, and Sunna Jo [https://www.linkedin.com/in/sunnajo/], a former pediatrician who is now a data scientist at Ribbon Health.\ Throughout the episode, we talk about why data quality in healthcare is messy, why having context around data is necessary to interpret and utilize it properly, how healthcare providers are improving their services because of platforms like Ribbon Health, how to tackle common data cleaning problems, and much more

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 [https://www.linkedin.com/in/philipdavidharvey], an Industrial Metaverse Architect in the Industrial Metaverse Core group at Microsoft. He is an expert in Data & AI Technical and Business Strategy & Philosophy. Harvey is also co-author of the book Data: A Guide to Humans [https://unbound.com/books/data/], which explores the concept of Data Empathy, and how it can power better use of data through better communication and understanding of stakeholders in the value chain of data.

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 [https://www.linkedin.com/in/selenafisk/] comes in. Fisk is a data storyteller [http://www.selenafisk.com], 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, we discuss the difference between being data-informed and data-driven, the different levels of data literacy, why change management is crucial to the success of any data literacy program, how to democratize data skills, how to approach data upskilling as a leader, and much more.

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 [https://www.linkedin.com/in/scottdownes]. Scott is the CTO of Invisible Technologies [https://www.linkedin.com/company/invisible-technologies-inc-/], 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 organization, whether their roles are more creative or more technical. Throughout the conversation, we talk about what Invisible Technologies uses GPT-3 to optimize workflows, a brief overview of GPT-3 and its use cases for working with text, how GPT-3 helps companies scale their operations, the promises of tools ChatGPT, how AI analysis and human review can work together to save lives, and much more.

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 [https://www.linkedin.com/in/jonathan-cornelissen] and COO Martijn Theuwissen [https://be.linkedin.com/in/martijn-theuwissen-b569b933] 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 [https://www.linkedin.com/in/jonathan-cornelissen] is the CEO and co-founder of DataCamp. As the CEO of DataCamp, he helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education and entrepreneurship. He holds a PhD in financial econometrics, and was the original author of an R package for quantitative finance. Martijn Theuwissen [https://be.linkedin.com/in/martijn-theuwissen-b569b933] is the COO and co-founder of DataCamp. As the COO of DataCamp, he helps DataCamp’s enterprise clients on their data and digital transformation strategies, enabling them to make the most of DataCamp for Business’s offering, and helping them transform how their workforce uses data.