It is a commonplace to say that companies are capturing exponentially increasing amounts of data. But this does not seem to make them exponentially smarter. Why?

Because it takes more than accumulating data to get actionable insights. Data is only as helpful as our ability to analyze it. As most teams cannot afford a dedicated data analyst, data analysis automation tools are emerging as the solution to this bottleneck.

The path to being data-driven

Tech teams have become smarter over the past decade with BI products (Tableau, Looker, Superset) and product analytics (Mixpanel, Amplitude, Heap). These products have planted the foundations of today's data-driven culture.

But there is still a long way to go before unlocking the full potential of how data can enhance decision-making. In a 2020 survey*, the vast majority of companies considered product analytics as a strong priority while only a minority thought they were levering them effectively.

So what makes it hard for teams to establish a culture where each decision is data-informed?

The answer is that the above-mentioned products are limited to querying and visualization, leaving it to you to ask the right questions and extract insights. You still need to manually slice and dice the data ourselves, which is a lost cause given the increasing size of datasets. In the end, "the people driving the business stop asking questions of the data altogether, and despite the fact that new information arrives everyday, nobody knows nuthin’" (Ben Horowitz).

Hence the promise of data analysis automated tools, removing the tedious manual work and letting you get to the insights directly.

Examples of Data Analysis Automated Tools

For machine data

With well-known players such as Datadog and New Relic, these products have managed to bring visibility over increasingly-complex engineering infrastructures while remaining accessible to the average developer. Most popular use cases in this space are application performance monitoring, infrastructure monitoring and log management.

Example of a Datadog pro-active insight:

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For operational data

Automated analysis for business intelligence seams to be gaining momentum with players such as Sisu, Outlier and Anodot. These players are addressing enterprises that don't have the firepower to analyze massive datasets. After detecting and diagnosing patterns in operational datasets, these tool generate insights that are understandable to the average employee.

Example of an Outlier pro-active insight:

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For product data

Product data is arguably a subset of operational data limited to user behavior while interacting with digital products: clicks, page views, transactions, ad impressions, etc. The potential of product data analysis is still largely untapped as 90% of retail companies think that the next big thing for their industries is user behavior analytics while only 34% of them think they are leveraging it effectively.

Players like Lazy Lantern generate automated insights about product usage, feature popularity, user paths and funnels, churn and engagement, user segments, bugs and outages, etc. They help product and marketing teams build better product experiences by effortlessly leveraging their data.

Example of a Lazy Lantern pro-active insight:


The promise of data analysis automation tools is to further democratize data-awareness with insights that are proactive, timely and understandable. It makes analytics consumption as easy as reading a personalized newspaper. By allowing each employee to leverage the full value of massive datasets, it can bring  companies to a new stage of data-awareness.

We hope that we conveyed the possibilities offered by this new wave of automation and that you will give these products a try!

*2020 Retail & CPG Customer Behavior Survey (SMS Research & Outlier)