The end of data bread lines

  • 28 March 2022
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This content, written by Lloyd Tabb, was initially posted in Looker Blog on Feb 5, 2015. The content is subject to limited support.

There is a lot of talk about “Data-Driven” cultures and it’s the right kind of talk. When people have access to the right data they really do make better decisions. When groups of people are presented with decent evidence, in the form of data, they usually start talking about the right things. Information wins wars.

In conventional data warfare, companies have data teams: people whose job it is to answer questions. But like the bread lines of the Great Depression, the results aren’t great--there is still way too much demand for analyst time and not enough of it as a resource. When companies want to be more data-driven, they invest to make the data teams bigger so they can answer more questions. Still, there always seems to be too many questions and not enough bread.

The result is inequality. Not everyone's questions are going to get answered, so the queue gets prioritized. If you are in the C-Suite, you can ask questions and become data-driven. But, if you're in operations, you might not to get to ask all that much, and maybe, that’s a problem.

In the conventional data economy, there are the data-rich and the data-poor. The data-poor make uninformed decisions. The data-poor have to guess.

Let’s take something simple: a retail buyer for an ecommerce business. The buyer’s job is to pick the styles and clothes to sell on the website; their expertise is pretty important for the success of the ecommerce site. We can start with some simple questions: “What’s selling? What colors and sizes is it selling in?” Maybe some harder questions: “What’s getting returned? Is there a particular size/color getting returned?”. Then even harder: “Is there something one-time buyers are buying in more frequency than other items (are my customers have a bad experience with a particular product)? Do customers we get from Facebook buy different things than the customers we get from Google?” Is there a product people buy first that increases their likelihood of becoming a repeat customer?”

If you are the buyer, eventually, you will run out of currency to ask all these questions. Your data team is simply too overloaded, so you don’t get answers.

Even when you do get an answer, often, it just begs another question. So what do you do? You get back in line and wait.

There exists a different world though—the world of companies that use Looker. In the world of Looker, the data team evolves from being a service organization, working from a work queue, to a design organization, designing and building a data model. It's a model that describes the relationships between data, what to measure, and the relationships between your customers and your products (repeat purchasers, first purchases, etc). And through this model data analysts create a single source of truth from which everyone works.

It becomes a world where data is ubiquitous, it’s everywhere. Where smart people can pull the data themselves and figure it out, a world of data literate people, a world where the buyer can ask and answer their own questions. It’s a world where everything can be freely questioned, a world where you can ask the next question seconds after it comes to you. With Looker, it doesn’t cost anything to ask a question anymore.

Inside a Looker company, an amazing thing starts to happen: people start backing up their opinions with facts. Paragraphs are punctuated with data (or links to data). People learn by observing the data. The company gets collectively smarter.

People start making better decisions.

We’ve helped some two-hundred companies through this transition. It works. We’ve heard from data people--now some of the most important people in their companies--tell us how much better their world is. We’ve watched our clients win. We love it and they love us, too.

Let them eat cake!

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