Catch all of the Talks from MDS Fest 2.0 HERE
If data is informing your most important decisions, that data must be reliable, high-quality, and trustworthy.
Empower your decisions with data you can trust and dive into proactive data quality management strategies at MDS Fest 2.0, with our Data Quality track.
Join us and be immersed in a series of expert-led talks that delve into innovative strategies for preemptively identifying and addressing data quality issues. Our speakers will not only highlight the challenges faced by data teams in maintaining data integrity but also showcase real-world examples of how leading companies have successfully optimized data quality for business-critical pipelines.
What is data quality?
Data quality refers to the degree to which a dataset meets the expectations of accuracy, completeness, validity, and consistency, ensuring that the data is reliable and trustworthy for analysis, reporting, and decision-making. High-quality data is essential for organizations to make informed decisions and drive business growth, while poor data quality can result in incorrect conclusions and inefficiencies.
The cost of poor data quality can be substantial, affecting everything from strategic decision-making to customer satisfaction. Gain access to cutting-edge methodologies and tools that can significantly enhance your organization’s ability to manage data quality effectively.
How can you implement a data quality strategy?
The insights gained from these sessions will empower you to implement proactive measures that can drastically reduce the incidence of data quality issues, saving valuable resources and mitigating risk for your organization. The knowledge shared during these talks will equip data teams to build stronger trust with stakeholders by ensuring the delivery of consistent, high-quality data.
Why data quality is a top priority for leaders
Data quality is the foundation upon which successful, data-driven organizations are built. High-quality data leads to better analytical outcomes, more accurate predictions, and informed decision-making processes.
Making data quality a top priority is essential not only for mitigating the risks associated with data inaccuracies but also for enabling businesses to move swiftly and confidently in a competitive landscape. By focusing on data quality, organizations can unlock the full potential of their data assets, drive innovation, and achieve sustainable growth.
Not sure which sessions to attend? We’ve got a rundown of all of the speaker sessions on data quality to make things a little bit easier for you.
To register for these talks and more, visit mdsfest.com.
Data contracts: Federated data governance
A talk by: Chad Sanderson, CEO, Gable
In this talk, Chad Sanderson, CEO of Gable.ai and author of the upcoming O'Reilly book "Data Contracts" will help data developers and data leaders understand what data contracts are, how they allow for federated data governance and data quality, how to get started implementing contracts, and why they are so critical in the upcoming race for Artificial Intelligence.
Delivering high-integrity reporting
A talk by: Archie Sarre Wood, Head of Community, Evidence - Business intelligence as code
This talk will explore how to use Evidence, a literate, open source data tool, to: - generate clear, polished reporting automatically - add explanatory context, documentation and explanation inline - maintain your reports as production standard assets by authoring them in code and testing them before publication This talk will be valuable for practitioners who have to communicate data with partners outside their team, or outside their organisation.
How Vanta implemented data contracts at scale
A talk by: Jake Peterson, Head of Data, Vanta & Etai Mizrahi, CEO, Secoda
Dive into the journey and strategies Vanta employed to implement data contracts across its large-scale, data-intensive operations. The discussion covers the initial challenges faced, including managing data consistency, ensuring data integrity, and facilitating efficient data exchange between different parts of the organization. Learn more about data contracts with real-life implementation examples.
Data: The keystone of AI safety
A talk by: Sumi Singh, Ph.D., Founder, Generative Artificial Intelligence Labs
AI technology carries risks that we need to address. This presentation focuses on how data is critical in spotting and handling these risks, covering everything from enterprise security to personal privacy. We'll look at real examples to show how to monitor AI systems, set up safety measures, and keep improving them. We'll discuss how data involves AI risks’ problems and solutions. Everyone is welcome to this talk, whether you're interested in AI or working with it directly. It's all about learning how data impacts AI safety.
Building great data teams - how culture leads to better data quality?
A talk by: Augusto Rosa, VP Engineering, Infostrux Solutions & Shravan Deolalikar, Director of Engineering, Data Management, Infostrux Solutions
This talk brings together Augusto and Shravan's experiences as leaders and consultants building great teams and how team culture drives data quality. Both with diverse backgrounds as architects and team builders, we believe in transparent and blameless cultures to improve the quality of modern data implementations. Today's modern data-driven culture is more than just technology; it is a more balanced approach to people, technology and processes. We will give you insights into how to hire, how and use to pick technology while doing good data management through the process and finally maintaining a happy and functioning team.
Event tracking is the new superpower of product analysts
A talk by: Eva Schreyer, Analytics Manager, Neugelb Studios GmbH
A sufficient event tracking framework is the most powerful data asset for product analytics. It will make or break your ability to analyse data and to influence your business. Quite often event tracking is a task nobody really wants to own and it is left for the data engineers to figure it out. And then analysts have to deal with data that are hard to use to answer business questions. I would argue that event tracking - as it is a data collection technique as well as a data source for analytics - should be the center of digital product analytics and analytics team should focus much more on building appropriate tracking frameworks from the start. I want to talk about my experience with two very different event tracking approaches (both for mobile apps) and what pros and cons are of various approaches.
Join us at MDS Fest 2.0
Join us at MDS Fest from April 8 - 12 to explore the cutting-edge of data-driven strategies, and take home actionable insights that could redefine the future of your organization. Get your tickets at mdsfest.com today.
See more about our other tracks and speakers in articles on our blog:
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