Imagine being asked to collect and maintain vast quantities of data, even though your title couldn’t be any further from “engineer” or “data scientist.” Having the responsibility of overseeing both supply chain and activity and internal processes to understand why product isn’t going out the door fast enough. Trying to strategize on next quarter’s objectives Read the full article…

A database, especially one full of long strings of unstructured text, is only as valuable as how easily you can search it and extract meaningful interpretations. That’s harder than ever with recent changes in how many organizations ask their teams to manage and learn from the data their functional area produces. Instead of waiting for Read the full article…

Most people in an organization have access to far more relevant text documents than they do neatly-organized sets of data. Whether they want to ask questions about customer sentiment or analyze a market’s demand for a potential new product or service, it’s often easier to read pages and pages of text-based research instead of finding Read the full article…

My first job was to manage the website of a local furniture company. There were about 175 individual static HTML pages, and the first thing they needed me to do was update the company’s phone number on all 175 HTML pages. Immediately I thought, “There has to be a better way.” Soon after, I figured Read the full article…

Much like an iceberg, the vast majority of data management happens beneath the surface: Your developers and data scientists often work with data in ways that aren’t always easy to visualize, and at an even deeper layer, your applications and AI tools execute billions of database transactions per day without direct human oversight. This beneath-the-surface Read the full article…