Shailvi Wakhlu
Data Science & Analytics Leader | "Self-Advocacy" Speaker, Author, and Consultant
San Francisco, California, United States
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Shailvi Wakhlu is a seasoned Data Leader and Self-Advocacy Expert with over seventeen years of experience building technology products. She has spoken at nearly 100 global conferences and Fortune 500 events, coached close to 500 individuals, and authored the best-selling book "Self-Advocacy." Her career includes notable roles at Salesforce, Fitbit, and as the Head of Data at Strava.
As a fractional executive, Wakhlu has consulted, advised, and invested with multiple high-growth startups. She also teaches online courses to a global audience to help technologists grow their technical and career skills.
Wakhlu grew up in India and studied Computer Engineering at Illinois Tech in Chicago. She loves to travel and has visited thirty-three countries. She lives in San Francisco with her husband and their seventy plants.
Learn more about her extensive speaking experience: http://www.shailvi.com/speaking-engagements.html
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Topics
Bad Data, Bad AI: How to Prevent, Diagnose, and Cure Data Quality Issues
Data insights, and now AI systems, are only as good as the quality of the data they’re built on. Inaccuracies and biases in your data can lead to costly mistakes, misinformed decisions, and unreliable models. In this talk, I highlight the typical lifecycle of data and the phases where bad data sneaks in. I also cover practical ways to prevent, diagnose, and fix issues before they spread across dashboards or machine learning pipelines.
Businesses rely on good data to make thoughtful decisions. When that underlying data is of poor quality, it can lead to unexpected and expensive outcomes. Preventing bad data starts with understanding how it gets created in the first place. In this talk, I’ll walk through the lifecycle of data, show where bad data gets introduced, and share examples of how to catch it early in the pipeline. You’ll learn how to diagnose common data problems, trace them to their root cause, and take actionable steps to fix them.
As AI becomes more deeply embedded in business processes, these lessons matter more than ever. The talk provides a practical blueprint for anyone who works with data to be proactive and intentional about ensuring data quality.
Key takeaways:
1. What is bad data and why should we care about fixing it - especially in the age of AI?
2. How can we prevent bad data from occurring?
3. How can we diagnose conditions that resemble poor data quality?
4. How can we cure and fix bad data before it impacts decisions or models?
I have seen data at scale and worked on products with 400M+ users. Thus, I’ve seen the impact of what bad data can do. Over the years, I’ve developed a robust framework to narrow down data issues quickly so that they can be fixed. I’ve also developed policies around checking for bias in data, that will help businesses be more thoughtful in their data solutions.
I have given plenty of variations of this talk, and just in the last year 2 of those sessions were billed as keynotes. I am able to adapt this talk to a more technical overview, a more business overview, or a mix of both depending on the audience.
Mastering Self-Advocacy: A Blueprint
In the modern workplace, self-advocacy is the cornerstone of success. This session unveils a pragmatic blueprint for honing your self-advocacy prowess, delivering benefits for your personal growth. Learn about what self-advocacy skills can unlock for you, the ways to reframe limiting beliefs, and tactical ways to frame wins in a way that leads to your desired outcomes. Break free from self-doubt, seize the reins of your potential, and sculpt a future brimming with accomplishments and empowerment.
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This topic is great if you want to enable high-ownership attendees equipped with self-advocacy skills.
The audience will leave with:
- An in-depth understanding of self-advocacy and its importance for career & business success
- Tools to reframe limiting beliefs around advocacy
- A framework to strategically unleash your authentic voice for your self-interest
- An actionable set of advocacy challenges to hold yourself accountable and practice your skills
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At technical conferences, this topic provides a nice balance from all the other talks - since everyone attending a conference is ultimately there to learn and progress in their career. I am able to customize this talk a little bit to include specific examples that are relevant to the niche audience group and can accommodate such requests.
At technical conferences, this topic provides a nice balance from all the other talks - since everyone attending a conference is ultimately there to learn and progress in their career. I am able to customize this talk a little bit to include specific examples that are relevant to the niche audience group and can accommodate such requests.
Ideal audience for this session:
- Groups of individuals who struggle with self-advocacy
- Members of minority groups eg. Women and underrepresented genders, BIPOC, LGBTQ+
- Employee Resource Groups (ERGs)
- Early Career Professionals
Bias In, Bias Out: Fixing the Root Cause of AI Inequity
AI systems are only as fair as the data they learn from. When bias creeps into data, it quietly shapes models, predictions, and decisions - often in ways that reinforce inequity. In this talk, I’ll explore how bias enters at every stage of the data lifecycle, from collection to modeling, and how it ultimately impacts AI outcomes. Using practical examples, I’ll share actionable ways to detect and reduce bias early, before it becomes embedded in analytics or algorithms.
You’ll walk away with a framework for building more equitable data and AI systems - starting at the source: the data itself.
Key takeaways:
1. Common types of bias in data and how they affect AI outcomes
2. Why fixing data bias is essential for responsible and fair AI
3. Core principles of bias detection and reduction
4. How to debias each stage of the data lifecycle to improve AI integrity
I have seen data at scale and worked on products with 400M+ users. Thus I’ve seen the impact of what bad data can do. Over the years, I’ve developed a robust framework to narrow down data issues quickly so that they can be fixed. I’ve also developed policies around checking for bias in data, that will help businesses be more thoughtful in their data solutions.
I have given variations of this talk - many of them focused on the larger concept of data quality. In just the last year 2 of those sessions were billed as keynotes. I am able to adapt this talk to a more technical overview, a more business overview, or a mix of both depending on the audience.
Empowering Teams: The Self-Advocacy Revolution
Discover the keys to business triumph by empowering and aligning your workforce. In this session, I unveil a straightforward framework for bolstering self-advocacy skills within your team, promising enhanced results for your business. Explore the transformative potential of normalizing self-advocacy, cultivate a resilient team dynamic, and proactively mitigate business risks while gaining profound insights into your team's motivations. Unleash your organization's full potential by fostering enhanced psychological safety, and witness the substantial benefits it bestows upon your business output.
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This topic is great if you want to build psychologically safe & high advocacy cultures.
The audience will leave with:
- An in-depth understanding of self-advocacy and why teams that hone that skill succeed
- Tools to understand and reframe the challenges your team faces in advocating for themselves
- A framework to create personalized blueprints that can help individuals unleash their authentic voice
- An actionable execution plan for you as well as your team
- Accountability and success measurement tactics
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At technical conferences, this topic provides a nice balance from all the other talks - since everyone attending a conference is ultimately there to learn and progress in their career. I am able to customize this talk a little bit to include specific examples that are relevant to the niche audience group and can accommodate such requests.
At technical conferences, this topic provides a nice balance from all the other talks - since everyone attending a conference is ultimately there to learn and progress in their career. I am able to customize this talk a little bit to include specific examples that are relevant to the niche audience group and can accommodate such requests.
Ideal audience for this session:
- Members of the C-suite
- Senior leaders, department heads, and managers
- Business leaders, team leaders, and technology leaders
- Employee Resource Group (ERG) leads or leads of other interest groups
RailsConf 2023 Sessionize Event
DeveloperWeek 2023 Sessionize Event
Women in Data Science - Puget Sound
Workplace Self-Advocacy for Women in Data Science
droidcon London 2021 Sessionize Event
VTTA Tech Conference Sessionize Event
Shailvi Wakhlu
Data Science & Analytics Leader | "Self-Advocacy" Speaker, Author, and Consultant
San Francisco, California, United States
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