Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
Have you ever stood in the slide projector’s glow and pitched furrowed-brow executives a change that could swing millions in revenue? Or flip the mission of hundreds of employees?
Shane and his data team navigated journalistic, product, and business interests to fine-tune the New York Times pay model. This highwire act helped transition The Gray Lady from an ad revenue to a subscriber-driven publication.
If you don’t like change, data engineering is not for you. Little in this space has escaped reinvention.
The most prominent, recent examples are Snowflake and Databricks, disrupting the concept of the database and ushering in the modern data stack era.
Will Larson (writer of "An Elegant Puzzle" – recommended read) may have said it best when he wrote that one of the best kinds of reorganization is the one you don’t do.
However, data leaders inevitably reach a point where, due to team growth or evolving business demands, things just don’t work. Faced with these challenges, data organizations may swing back-and-forth between centralized vs. decentralized organizational structures until they achieve the right balance.
For many data teams, the past five years have witnessed an evolution of technology, teams, and processes that calls to mind another significant period in time: the Industrial Revolution.
From the late 18th century to the mid-19th century, the Industrial Revolution transformed economies with new tools, cheaper power sources, and more streamlined ways of organizing work in factories. And even now, visit a modern printing plant, and you’ll find a state-of-the-art operation with advanced tech and robotics.