Data Quality

Tell us about your background Throughout my career, I have dedicated myself to creating tools, products, and technologies that help people effectively utilize their data. My passion lies in developing products that enable users to efficiently and scalably gain maximum value from their data. My journey in understanding the intricacies of data and its potential began at Palantir Technologies, where I began working on search and indexing products. As data volumes grew, I focused my efforts on solving some of Palantir customer’s core problems across the financial and defense verticals before leading customer focused compute teams. After Palantir, I served as CTO at Veraset, a cloud-based data-as-a-service company. Veraset delivered high-quality, large scale data to a number of enterprises and grew to 15M ARR before being acquired. Following this, I joined Citadel Investment Group as the Head of Business Engineering of Ashler. In that role, I managed crucial data operations, including overseeing data pipelines, investment platforms, data lakes, and the software and data engineering teams responsible for them. ...

Databricks Sr. Staff Developer Advocate, Denny Lee, Citadel Head of Business Engineering, Vinoo Ganesh, and Databand.ai Co-Founder & CEO, Josh Benamram, discuss the complexities and business necessity of setting clear data service-level agreements (SLAs). They share their experiences around the importance of contractual expectations and why data delivery success criteria are prone to disguise failures as success in spite of our best intentions. Denny, Vinoo, and Josh challenge businesses of all industries to see themselves as data companies by driving home a costly reality – what do businesses have to lose when their data is wrong? A lot more than they’d like to believe. ...

As the data-as-a-service ecosystem continues to evolve, data brokers are faced with an unprecedented challenge – demonstrating the value of their data. Successfully crafting and selling a compelling data product relies on a broker’s ability to differentiate their product from the rest of the market. In smaller or static datasets, measures like row count and cardinality can speak volumes. However, when datasets are in the terabytes or petabytes though – differentiation becomes much difficult. On top of that “data quality” is a somewhat ill-defined term and the definition of a “high quality dataset” can change daily or even hourly. ...

We’ve all heard the phrase “data is the new oil.” But really imagine a world where this analogy is more real, where problems in the flow of data - delays, low quality, high volatility - could bring down whole economies? When data is the new oil with people and businesses similarly reliant on it, how do you avoid the fires, spills, and crises? As data products become central to companies’ bottom line, data engineering teams need to create higher standards for the availability, completeness, and fidelity of their data. ...

In this episode of The Data Standard, Catherine Tao and Vinoo Ganash talk about large-scale data and data processing challenges. Vinoo starts the conversation by explaining his current obligations and how his company uses data to find working solutions for a wide range of problems. Then he talks about OLTP and OLAP models and how large-scale data can help improve workflows and offer better results. Optimization is needed for every specific application, and Vinoo talks about the methods he uses to enhance existing platforms. Even when the newly developed systems show positive results, the work is never done, as optimization is a constant, dynamic process. ...