“It’s just a quick ask.”
Anyone who has worked with data has heard this phrase—a seemingly simple request: “Can we get a slightly modified report?” “What’s the regional breakdown for this other region?” “Just pull the data.” It sounds like something that should take minutes, maybe hours at most. Then comes the reality: “That’ll take six months.” – if you're lucky and your request doesn't disappear in a backlog.
Cue the frustration. Leadership can’t fathom why it’s so hard. Data engineers groan under the weight of yet another pipeline. Analysts feel despair because they know the answer is locked behind endless transformations, bottlenecks, and backlog. And yet, everyone asks the same thing: “Why? Isn’t this just a quick ask?”
We talk about data like it’s a simple, tangible thing—a collection of facts, sitting neatly in a system, ready to be pulled out and used. But that’s not how it works. In reality, what we store isn’t "data" in the way most people imagine. What we’re actually storing is information—data that’s been, carefully processed, organized, structured, and shaped for specific purposes.
Data is raw and unstructured—a collection of phone numbers and names, for example. Information on the other hand, is data that’s been refined into something to suit a use—a phone directory organized alphabetically.
The structure and organization make it useful for one task (finding a specific person and their number), but terrible for another (counting how many numbers belong to a specific area code). The same is true for systems in organizations. We design them to optimize for specific questions or use cases, and that’s where the trouble starts.
At scale, systems don’t just hold data. They hold data in a specific shape, structure, and schema. That shape is chosen to answer certain kinds of questions quickly and efficiently. Want to know last quarter’s revenue? The data warehouse has that answer because it’s optimized for business intelligence queries. Want to trace the real-time path of a shipment? That’s in a different system designed for operational queries. Want to model behavioral similarities between your customers, that's still another system. In all these cases, often times it's the same underlying data.
When someone asks for "the data," what they’re frequently asking for is information that isn’t currently in the shape they need. It exists, but not in a system or form that lends itself to their question. And transforming it into the right shape—reorganizing, reprocessing, and integrating it—is painful. The pain can be so high that these requests are often abandoned.
If we’re going to fix this, we need to change how we think and talk about data. Data isn’t static. It’s not just a thing sitting on a shelf, waiting to be retrieved. It’s dynamic. It flows. It transforms. And the process of turning raw data into usable information hasn't traditionally been seen as a dynamic process, rather one that's done once up front for a known use.
If we keep treating data like a static resource, we’ll continue to frustrate ourselves and our teams. Instead, we need to embrace the reality: data isn’t just data. It needs to be transformed for use. It’s information that needs to be shaped and reshaped and loaded into systems that are the right tool for the job.
When we start thinking about data as dynamic, the conversation shifts. Instead of wondering why it takes so long to get the data, we can focus on building systems that prioritize agility, flexibility, and flow. Because at the end of the day, what you really want isn’t data—you want answers. And that requires a whole new way of thinking.
