Social media algorithms silently shape what billions of people see and how they interact online. While most data scientists work to optimize business value within platform rules, there's valuable knowledge to be gained from understanding how these systems can be exploited - knowledge that can make ethical data scientists better at their jobs.

In this episode, Tim O'Hearn joins Dr. Genevieve Hayes to share insights from his experience manipulating social media platforms, revealing what ethical data scientists can learn from understanding the dark side of algorithmic systems.

This conversation reveals:

1. How social media platforms are essentially just sophisticated recommendation engines [08:16]
2. The "canary" technique for detecting when underlying systems have changed [11:36]
3. Why customer accounts often provide better testing data than artificial test accounts [13:56]
4. The importance of time series data collection for identifying suspicious patterns, effectiveness of campaigns, and understanding platform dynamics [18:04]

Guest Bio

Tim O’Hearn is a software engineer who spent years gaining millions of followers for clients by circumventing anti-botting measures on social networks. He is also the author of the new book, Framed: A Villain’s Perspective on Social Media.

Links

• Tim's Website
• Connect with Tim on LinkedIn
• Subscribe to Tim's newsletter
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Most dashboards and reports get ignored despite all the technical expertise that goes into creating them. The reason isn't technical limitations or poor data quality - it's that they fail to deliver value to the people who are supposed to use them.

In this Value Boost episode, Nicholas Kelly joins Dr. Genevieve Hayes to reveal proven strategies for increasing dashboard adoption and showcasing your value as a data professional.

In this episode, you'll discover:

1. The number one reason why dashboards fail [01:15]
2. The three-bucket framework that transforms dashboard development [04:06]
3. How to salvage an already-built dashboard [07:12]
4. The simple wireframing technique that opens doors to meaningful user conversations [10:08]

Guest Bio

Nicholas Kelly is the founder of Delivering Data Analytics, a consultancy focused on helping organisations enable their teams to make smarter, faster, and more confident decisions through data and AI. He is also the author of Delivering Data Analytics and the recently released How to Interpret Data.

Links

• Nicholas's Website
• Connect with Nicholas on LinkedIn
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Despite unprecedented data abundance and widespread data science education, even experienced data professionals still struggle to interpret data effectively. They draw wrong conclusions, miss critical insights, or fail to communicate findings in actionable ways.

In this episode, Nicholas Kelly joins Dr. Genevieve Hayes to tackle the critical challenge of data interpretation - revealing why technical expertise alone isn't enough and sharing practical frameworks for transforming raw data into actionable business insights that drive real organisational change.

This conversation reveals:

1. The four primary challenges that make data interpretation so difficult [02:24]
2. Why ChatGPT and AI tools are changing the data interpretation landscape [06:23]
3. The "Five Whys" technique that ensures you're asking the right questions instead of wasting time on problems everyone already understands [17:32]
4. Why successful data projects don't end with presenting insights and what to do next [20:01]

Guest Bio

Nicholas Kelly is the founder of Delivering Data Analytics, a consultancy focused on helping organisations enable their teams to make smarter, faster, and more confident decisions through data and AI. He is also the author of Delivering Data Analytics and the recently released How to Interpret Data.

Links

• Nicholas's Website
• Connect with Nicholas on LinkedIn
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Can you clearly articulate what makes your data science work valuable - both to yourself and to your key stakeholders? Without this clarity, you'll struggle to stay focused and convince others of your worth.

In this Value Boost episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how creating a compelling value proposition transformed his data team from report writers to strategic partners by providing both external credibility and internal direction.

This episode reveals:

1. Why a clear purpose statement serves as both an external marketing tool and an internal compass for daily decision-making [02:09]
2. A framework for identifying your stakeholders' true pain points and how your data skills can address them [04:48]
3. A practical first step to develop your own value statement that aligns with organizational strategy while focusing your daily work [06:53]

Guest Bio

Dr Peter Prevos is a water engineer and manages the data science function at a water utility in regional Victoria. He runs leading courses in data science for water professionals, holds an MBA and a PhD in business, and is the author of numerous books about data science and magic.

Links

• Connect with Peter on LinkedIn
• A Brief Guide to Providing Insights as a Service (IaaS)
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Internal data science teams face a unique challenge - they're providing an invisible service that only gets noticed when something goes wrong. This puts data scientists in the awkward position of having to market themselves within their own organization, without any marketing training.

In this episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how he applied his PhD research in services marketing to transform his water utility's data team from "report writers" to strategic partners by positioning data science as "Insights-as-a-Service."

This episode explains:

1. Why treating data science as "Customer Satisfaction Engineering" rather than technical implementation shifts everything about team effectiveness [08:19]
2. How understanding both the financial and psychological "price" users pay for insights leads to dramatically better adoption [14:36]
3. The treasure hunt technique that transformed how stakeholders discover and engage with available data resources [18:17]
4. Why the mantra "99% of business problems don't need machine learning" can paradoxically increase your data science impact [22:29]

Guest Bio

Dr Peter Prevos is a water engineer and manages the data science function at a water utility in regional Victoria. He runs leading courses in data science for water professionals, holds an MBA and a PhD in business, and is the author of numerous books about data science and magic.

Links

• Connect with Peter on LinkedIn
• A Brief Guide to Providing Insights as a Service (IaaS)
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

When deadlines loom, it's easy for data scientists to fall into the trap of cutting corners and bending analyses to deliver what stakeholders want. But what if a simple framework could help you maintain quality under pressure while preserving your professional integrity?

In this Value Boost episode, Dr. Brian Godsey joins Dr. Genevieve Hayes to reveal his powerful "Knowledge first, Technology second, Opinions third" hierarchy - a framework that will transform how you handle stakeholder pressure without compromising your standards.

In this episode, you'll discover:

1. Why this critical hierarchy gets dangerously inverted when deadlines loom and how to prevent it from undermining your credibility [01:05]
2. How to resist the career-limiting trap of cherry-picking facts that merely support executive opinions [04:09]
3. A practical note-taking technique that keeps you anchored to reality when stakeholders push for convenient answers [06:04]
4. The one transformative habit that separates truly valuable data scientists from those who merely validate existing assumptions [07:17]

Guest Bio

Dr Brian Godsey is a Data Science Lead at AI platform as a service company DataStax. He is also the author of Think Like a Data Scientist and holds a PhD in Mathematical Statistics and Probability.

Links

• Brian's website
• Connect with Brian on LinkedIn
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The data science world has always been obsessed with tools and techniques - a fixation that's only intensified in the era of generative AI. Yet even as ChatGPT and similar technologies transform the landscape, the fundamental challenge remains the same - turning technical capabilities into business results requires a process most data scientists never learned.

In this episode, Dr. Brian Godsey joins Dr. Genevieve Hayes to discuss why the scientific process behind data science remains more critical than ever, sharing how his original "Think Like a Data Scientist" framework has evolved to harness today's powerful AI capabilities while maintaining the principles that drive real business values.

This conversation reveals:

1. Why the seemingly basic question "Where do I start?" continues to derail data scientists' effectiveness and how mastering the right process can transform your impact [01:15]
2. The three stages of the data science process that remain essential for career success even as AI dramatically changes how quickly you can execute them [11:07]
3. How the accessibility revolution of generative AI creates new career opportunities for data scientists in organizations that previously couldn't leverage advanced analytics [18:34]
4. The underrated troubleshooting skill that will make you invaluable as organizations increasingly rely on "black box" AI models for business-critical decisions [20:21]

Guest Bio

Dr Brian Godsey is a Data Science Lead at AI platform as a service company DataStax. He is also the author of Think Like a Data Scientist and holds a PhD in Mathematical Statistics and Probability.

Links

• Brian's website
• Connect with Brian on LinkedIn
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Even the most brilliant data analysis can fall flat when presented with poor visualisations. Many data scientists simply use default charts from their analysis software, missing the opportunity to create compelling visuals that drive understanding and decision-making.

In this Value Boost episode, Bill Shander joins Dr. Genevieve Hayes to share the design principles that can transform technical charts into powerful communication tools - even for those without formal design training.

This quick-hit episode reveals:

1. Why default visualisation settings in most software undermine effective communication [02:03]
2. The research-backed "preattentive response" principle that determines whether your visualisation succeeds or fails [05:17]
3. How the counterintuitive "do less" approach creates more impactful data stories [06:18]
4. A simple glance test to immediately evaluate and improve any visualisation you create [11:21]

Guest Bio

Bill Shander is the founder of Beehive Media, a data visualisation and information design consultancy. He is also a keynote speaker; teaches workshops on data storytelling, information design, data visualisation and data analytics; and is the author of Stakeholder Whispering.

Links

• Bill's Website
• Connect with Bill on LinkedIn
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE