Editor’s note: This piece is an excerpt from their upcoming book Winning with Data by Wiley.
When I think about the behavior of many business people today, I imagine a breadline. These employees are the data-poor, waiting around at the end of the day on the data breadline. The overtaxed data analyst team prioritizes work for the company executives, and everyone else must be served later. An employee might have a hundred different questions about his job. How satisfied are my customers? How efficient is our sales process? How is my marketing campaign faring?
These data breadlines cause three problems present in most teams and businesses today. First, employees must wait quite a while to receive the data they need to decide how to move forward, slowing the progress of the company. Second, these protracted wait times abrade the patience of teams and encourage teams to decide without data. Third, data breadlines inhibit the data team from achieving its full potential.
Once an employee has been patient enough to reach the front of the data breadline, he gets to ask the data analyst team to help him answer his question. Companies maintain thousands of databases, each with hundreds of tables and billions of individual data points. In addition to producing data, the already overloaded data teams must translate the panoply of figures into something more digestible for the rest of the company, because with data, nuances matter.
The conversation bears more than a passing resemblance to one between a third-grade student and a librarian. Even expert data analysts lose their bearings sometimes, which results in slow response times and inaccurate responses to queries. Both serve to erode the company’s confidence in their data.
Overly delayed by the strapped data team and unable to access the data they need from the data supply chain, enterprising individual teams create their own rogue databases. These shadow data analysts pull data from all over the company and surreptitiously stuff it into database servers under their desks. The problem with the segmented data assembly line is that errors can be introduced at any single step.
A file could be truncated when the operations team passes the data to the analyst team. The data analyst team might use an old definition of customer lifetime value. And an overly ambitious product manager might alter the data just slightly to make it look a bit more positive than it actually is. With this kind of siloed pipeline, there is no way to track how errors happen, when they happen or who committed them. In fact, the error may never be noticed.
Imagine a world where data is put into the hands of the people who need it, when they need it.
Data fragmentation has another insidious consequence. It incites data brawls, where people shout, yell and labor over figures that just don’t seem to align and that point to diametrically different conclusions.
Imagine two well-meaning teams, a sales team and a marketing team, both planning next year’s budget. They share an objective: to exceed the company’s bookings plan. Each team independently develops a plan, using metrics like customer lifetime value, cost of customer acquisition, payback period, sales cycle length and average contract value.
When there’s no consistency in the data among teams, no one can trust each other’s point of view. So meetings like this devolve into brawls, with people arguing about data accuracy, the definition of shared metrics and the underlying sources of their two conflicting conclusions.
Imagine a world where data is put into the hands of the people who need it, when they need it, not just for Uber drivers, but for every team in every company. This is data democratization, the beautiful vision of supplying employees with self-service access to the insights they need to maximize their effectiveness. This is the world of the most innovative companies today: technology companies like Uber, Google, Facebook and many others who have re-architected their data supply chains to empower their people to move quickly and intelligently.