Unleash the full potential of your data.
For decades, the language around data has barely changed. Every few years a new architecture or philosophy rises. We hear about data lakes, warehouses, meshes, fabrics, and observability platforms. Each is a promise to finally tame the chaos of data management. Billions have been invested across multiple generations of tooling
In 2014, my last startup was acquired. We joined a fast growing organization with a top-notch data team. They had invested heavily in data infrastructure. Data was strategic. They had "the hub," a Hadoop cluster built on HDFS. I thought: here's a company doing things right.
Live Webinar ᐧ January 8 ᐧ 11 AM PT / 2 PM ET You’re storing terabytes of data “just in case.” But when AI initiatives launch, that data is inaccessible, poorly formatted, or locked behind a six-month pipeline project. Sound familiar? You’re paying thousands in storage costs for data
The Challenge 70% of AI projects fail on data quality and integration, not models. But even teams with clean data struggle when they can’t reproduce their training runs. Your model’s accuracy dropped from 87% to 64%. But why? Was it bad training data? A schema change upstream? Preprocessing
I wrote a post about thinking past medallion architectures. That one went a little deeper about the architectural characteristics that make thinking in “medallions” unnecessary. But the truth is, you don’t need to internalize all that. I’m guessing you sense that data just doesn’t work, even with
Let’s talk about something nobody wants to admit. Your marketing team has their own copy of customer data. Sales has a different version. Product is maintaining yet another extract. Finance built their own dashboard using data they pulled last month. Each team has created their own shadow copy of
The Challenge Your team is testing OpenAI embeddings, Anthropic’s Claude, and a custom fine-tuned model. Each needs customer data in a slightly different format. The traditional approach: build three separate pipelines, each with its own failure modes and maintenance overhead. Every AI workload expects data its own way. Your
Picture this: You’re in an executive meeting. The company just acquired another business, and the CEO wants to change how you calculate monthly active users to include the new customer base. Simple request, right? “That’ll be six months,” comes the response from the data team. Six months?! To
The Challenge Your AI team has transformative ideas. Leadership approved the budget. Then reality: preparing data for AI means months of cleaning and formatting. Data scientists become data wranglers. Engineers build pipelines instead of AI features. By the time data is ready, your competitor already shipped. The problem isn’t
