And why you need to think in terms of data-informed decisions.
Self-service analytics is a really broad phrase. It can cover both an aspiration that we've never quite reached, and something that we already do every single day.
The simplest definition I've seen is that business stakeholders aren't stuck waiting on the data team. If a marketing manager wants to understand sales this quarter they can look it up themselves without putting in a request to the data team to provide them with the info.
As an aspiration this is great — imagine a world where everyone could quickly find the data suitable for their needs. Even if we're not there yet, our (really data-driven) stakeholders will request data and wait patiently for us to put it together for them. And all we have to do is just keep making dashboards to cover each of their needs and eventually that queue of data requests will go away.
In reality that's generally not the case.
We end up in a continual state of partial self-service. To illustrate, let's look at our hypothetical marketing manager. The next time she's making a decision she could do one of these things
But there's a risk she could do one of these instead
Self-service clearly isn't about stakeholders getting data themselves. If we build out a dashboard covering every possible scenario, it's not clear that it will even get used. A dashboard might not even be the right approach in the first place. We need a better way to think about this.
As defined above, self-service covers every scenario except a user directly submitting a request to the data team. The goal shouldn't be to leave the data team alone. We also need to think about data-informed decision making.
A data-informed decision is also a super-simple definition — it's just a decision made using data.
Everyone at a company makes decisions. Those decisions are data-informed if (relevant) data influenced the decision in some way. The data could be in the form of dashboards, analyses, presentations, data products, raw data dumps, data support tickets, or anything else. The format doesn't matter so long as the data was made available in some way that could be properly understood.
Providing data to support decision making is the primary goal of the data team.
The concept of 'data-informed decisions' isn't exactly a real metric that can be tracked, but it's still a neat way of thinking about how a data team can have impact on the business.
As we saw in the section above, providing unstructured or unfocused access to data for everyone isn't data-informed and is therefore probably not very impactful. It's not a great definition for self-service.
Self-service is the intersection of data-informed decisions and minimal data team involvement
Why is this useful? There are two truths buried in this diagram
Let's say our marketing manager needs to decide whether to put more money in the current ad campaign she's running. She would strongly prefer to use data when making her choice. Because she has a great data team she can submit a ticket to ask for the customer acquisition cost of her campaign.
In this case we need to keep in mind that
We need to provide her with relevant, understandable data without a lot of friction or waiting. If she can get the data she needs in a few minutes, that's true self-service.
In practice, providing this kind of self service analytics is tough. We'll talk about how to actually do it successfully in a future post.
In the meantime we'd suggest stepping back and thinking about what you're trying to accomplish with self-service. Focusing exclusively on data democratization — on ensuring stakeholders have access to data — will only cause problems.
Instead, try to increase data-informed decisions. Ensure that stakeholders have access to relevant data at the right time, even if it's a manual process that requires the data team to be involved. Invest in tools to make the data team faster and coach people on the right question to ask.
