Want to be a data-informed company? Here’s why self-serve analytics is key.

Data-driven has become a buzzword. Every company says they’re data-driven, or data-informed, or data-something. But saying it doesn’t make it true. 

Too many companies that claim to be data-driven are working in siloes, with their data analysis locked away where only data teams can get at it, inaccessible to the rest of the company. When other teams want to access data to make decisions or test a theory, they need to put in a request for information—which can take days or even weeks to fulfill. When you’re moving at startup speeds, who has that kind of time? 

When companies do get it right, having useful data at your disposal can have a strong impact on performance, and ultimately revenue. According to McKinsey, “Intensive users of customer analytics are 23 times more likely to clearly outperform their competitors in terms of new  customer acquisition than non-intensive users, and nine times more likely to surpass them in customer loyalty.”

So, what’s their secret? Companies that actually use data to make business decisions have an ace up their sleeves: Self-serve analytics.

Self-serve analytics describes the tools, systems, and processes that companies use to empower non-technical team members to collect and analyze data for themselves. 

Self-serve analytics is the key to letting anyone on your team use product, marketing, revenue, or other company operations data to investigate outliers, test hypotheses, and make a case for data-driven business decisions—all without relying on data analysts to pull and compile that data. 

Monitoring your company’s key metrics is important. Many companies that want to make these metrics visible across teams have their data team preset dashboards on something like a business intelligence (BI) platform. With viewable dashboards like this, more people in the company can stay informed on performance dips or spikes.

But even if everyone on the team has access to these dashboards, this type of self-serve data monitoring is not the same as self-serve analytics. Watching metrics go up and down is useful, but it can only get you so far.

As the founder of Skydata, Timo Dechau, puts it, “Most BI datasets for dashboards are built on top of an existing data model reduced to contain the essential metrics and dimensions needed for the dashboard. This means plenty of follow-up questions from self-servers require extensions to the dataset and the dashboard itself.”

With true self-serve analytics, non-technical users can not only view dashboards and reports but also easily modify them, dice up and dive further into the details, and have enough to create informed hypotheses of why dips in spike in performance are happening and then process the data to find real answers.

To make analytics accessible to everyone, it’s self-evident that you need tools that anyone can use. For example, unlike most BI tools, Mixpanel allows users to run data queries and build reports and dashboards with mouse clicks, not SQL coding.

Under the hood, there’s one key technical difference that makes a simple-yet-powerful analytics UI possible: an event-based data model. This type of model can be easily extended with new context and dimensional or enrichment data at the self-serve level, meaning data teams don’t need to be involved in creating every ad hoc report or new dashboard.

As Timo explains, defining an event scheme with preexisting star schema-organized data in your warehouse is a relatively simple task. After that, connecting your warehouse data to a self-serve analytics tool is the last step to unlocking the full potential of self-serve analytics. 

Now that we’ve covered what self-serve analytics is and the tools needed to make it possible, let’s take a look at how a few different companies are using self-serve analytics to empower their teams and gather data-driven insights. 

With over 18 million customers, GoDaddy needs to gather insights quickly at scale. With self-serve analytics, the team can iterate on new feature launches and course-correct quickly, often within the same day.

“GoDaddy has built a culture of experimentation where we put our customers first.”

Before implementing self-serve analytics, Ancestry was using Adobe Analytics and creating data visualizations in Tableau to share data insights with the entire team. They adopted Mixpanel to give everyone access to strategic data, quickly. Now, data analysts have more time to focus on strategic initiatives and product managers have the information they need to better influence KPIs, like retention and driving users to purchase a family history subscription.

“Anyone can pick it up and learn about our customer behavior, digging deep into metrics that matter to them, and know that the data is accurate and trustworthy.”

As a fast-growing company in the highly competitive gaming industry, SuperPlay needed to quickly identify issues and validate data to make product improvements. With self-serve analytics, they can gather those insights and get everyone up to speed quickly. For example, when a new employee joins, they can access a few main dashboards on daily metrics and very easily understand what is going on there, and they can use these learnings to do the same things in future projects.

“It’s a solution that’s used heavily across different functions, even for those that aren’t data-literate.”

The local businesses market has changed a lot since 2020. Yelp, on a mission to connect people with the businesses that make up that market, invested in self-serve product analytics to accelerate iteration cycles and feedback loops. Now, ideation and launching critical features and product improvements take days instead of weeks.

“There is significant time-saving in our team’s daily workflow. In addition, smaller teams are able to answer questions about their product areas without requiring the support of data analysts.”

Before adopting a product analytics tool, TaskRabbit was already using its own version of self-serve analytics: manually copying and pasting data into spreadsheets. The process was understandably slow and frustrating. Campaigns had to run for a month before data could even be collected, and product insights were inaccessible. Now, with real self-serve analytics, insights are accessible instantly.

“I used to have to wait an hour to get really basic insights—just to see if someone clicked a thing or not. Now I can do it myself.”

We talked about companies that use “data-driven” as an empty promise. They don’t democratize access to data, and they can’t use analysis to gather valuable insights or make business decisions. Even businesses that have given their team access to data often fall into the trap of “data monitoring” instead of analysing, watching numbers go up or down without understanding why. 

Self-serve analytics solves both of these problems. With self-serve analytics, “data-driven” becomes more than a buzzword or a crutch: Team members can find the data they need when they need it. Most importantly, they can use their self-serve tools to zero in on interesting finds, dig deeper to investigate what they mean, and use those insights to draw powerful conclusions.