I related to this since it’s very often been the case for me, and something I strive towards in my teams — I’ve occasionally gotten feedback to the effect of “you guys started out pretty slow, but the end result was spectacular.”
You can ‘plan’ a machine tightly from the beginning, and implicit in that design is bounded results. Or you can allow for emergence and see how much more team-mates can offer by adapting and learning.
Empowered Teams Get a Slow Start, But Soon Zoom Ahead – Andrew O’Connell – The Daily Stat – Harvard Business Review.
Big Data is our generation’s civil rights issue, and we don’t know it – Solve for Interesting.
In the old, data-is-scarce model, companies had to decide what to collect first, and then collect it. A traditional enterprise data warehouse might have tracked sales of widgets by color, region, and size. This act of deciding what to store and how to store it is called designing the schema, and in many ways, it’s the moment where someone decides what the data is about. It’s the instant of context.
That needs repeating:
You decide what data is about the moment you define its schema.
With the new, data-is-abundant model, we collect first and ask questions later. The schema comes after the collection. Indeed, Big Data success stories like Splunk, Palantir, and others are prized because of their ability to make sense of content well after it’s been collected—sometimes called a schema-less query. This means we collect information long before we decide what it’s for.
And this is a dangerous thing.