It’s a challenge humans have been struggling to overcome since the dawn of time. Building a solution that is fast, but also reliable. In Formula 1 auto racing, teams strive to build the fastest car possible. However, if their car never makes it to the finish line due to technical failures, it doesn’t matter how
I recently had lunch with the founder of a business analytics company whom I mentor. Over some very tasty interior Mexican food, he vented about how his clients do not fully understand how hard it is to get data right every day. If I got a dollar every time I heard this, I’d be having
Agentic AI systems don’t just passively receive commands. They plan, reason, and act autonomously to accomplish goals. But even the most capable autonomous agent is only as smart as the data scaffolding around it. The difference between brittle execution and adaptive autonomy? Complete, accurate, and structured metadata. To operationalize agentic AI, particularly in commercial environments,
There are so many new (cool) things happening with data nowadays, it’s easy sometimes to overlook the basics. So I thought it would be helpful to look at some “best practices” for setting up your data workflows. Effective data workflows are essential for maintaining data quality, promoting collaboration, and driving reliable insights across an organization.
I recently spoke with the head of data and analytics at an investment management firm about how 2025 was going so far for him and his peers in the industry. Maybe not surprisingly, he said it’s been challenging. Setting aside geopolitics and market conditions, he said, throughout the industry, data engineers and data scientists are
nLite metadata generator now available to capital markets participants via DIH. Schaumburg, Illinois, Austin, Texas, 24 June 2025: XtremeData, a provider of next-generation advanced data profiling and quality solutions, today announced a collaboration with Data In Harmony (DIH), a global financial data provider, to bring its flagship product, nLite, to institutional market participants. nLite helps
Data Quality can sometimes feel like the Holy Grail, with those of us who generate, process, or use data doing our best impersonations of Sean Connery from “Indiana Jones and the Last Crusade.” We dedicate so much time, treasure, blood, sweat, tears… a lot of tears… trying to ensure the highest quality data. But what
Last week, I discussed data onboarding, and I received numerous questions about data profiling. It is a critical step in data processing. So today, let’s talk about data profiling – what it is, and some best practices for doing it right. Data profiling is the process of examining, analyzing, and summarizing data sets to understand their structure,
Data onboarding is a crucial process in any data-driven project. Doing it properly ensures data quality, consistency, and usability. Poor onboarding, on the other hand, can lead to flawed analyses and misguided decisions. Here are ten (10) dos and don’ts to guide you through the process… #1 – Do understand the data source. Before importing
A recurring concern I hear from heads of data science teams is that their data scientists spend too much time preparing their data for analysis rather doing actual analysis. As one hedge fund manager in London told me, “I don’t pay my quants to ‘fiddle’ with their data – I pay them to find alpha
