Global catastrophe has brought with it rapid change. Over the last 18 months, we’ve seen an acceleration in the number of companies adopting digital strategies, driven in large part by the most recent global pandemic. As Alex Dean, Snowplow’s CEO and Co-founder put it in 2020,
“COVID-19 is accelerating a drive towards digital in many businesses. Although every major company has been trying to implement digital transformation over the last 20 years, many of these projects have failed. What we can see today is that digital transformation is no longer a nice to have – it’s now essential for
survival.” – Alex Dean, CEO and Co-founder at Snowplow
At the heart of this transformation is a need to deeply understand how users interact with an organization’s digital platforms and products. As people lead increasingly digitized lives, it’s up to modern companies to invest in optimizing digital experiences, and the only way to do that effectively is to build an understanding of how users engage with their brand across web, mobile, and many other platforms.
Fundamentally, we’re talking about the task of observing and analyzing human behavior at scale. In the past, there have been attempts to understand users through data (be that through surveys or focus groups), but only recently have organizations had real access to behavioral data.
Behavioral data is perfectly suited to this challenge for a few key reasons.
- It’s granular – interactions are put under a microscope, bringing minute actions and events into focus;
- It’s rich – it describes happening in depth, so you can see context behind a given action or interaction;
- It can be easily aggregated and transformed – allowing you to build a picture of certain users or actions;
- It can be highly-structured – making it apt for analysts and data consumers to organize and understand the data.
The advantages of working with behavioral data are clear. Yet even today, capturing and managing behavioral data in order to derive value is a real challenge. To meet this challenge, more and more companies are adopting the modern data stack – a landscape of specialized tools designed that fit together to enable data teams to work effectively with behavioral data.
The data stack is evolving
One glance at our blueprint of the modern data stack (or Indicative’s ecosystem of modern data infrastructure) is enough to get a taste for its growing complexity. At a top level, first-party data goes on a journey from the products and platforms where the action happens, to the data warehouse (which we can consider, in many ways the brain of the business) for storage, processing and transformation. From there we have a plethora of visualization tools available to bring the data to life, which analysts can use to serve analytics to front-line teams such as product and marketing.
The right visualization tools are crucial to effective data storytelling. They can be the difference between cross functional data consumers deriving value from the data versus dashboards sitting abandoned and individual teams resorting to their own tools, resulting in data silos and a disparate data culture. It is why tool selection is such an important part of building out the data stack, to ensure the organization is equipped to derive value from their data.
Self-serving data discovery with Indicative
Arguably the best product analytics tools allow data consumers to ‘fish for themselves’, handing them the reins to data discovery without having to rely on data engineers. Indicative is a great example of this.
The Indicative team have launched a unique product, designed to make behavioral data more accessible in the organization through an intuitive UI. With its own storage engine optimized for analysis of the user journey, Indicative makes it easy to efficiently query the sequence of events in a given user’s journey, or the time between those events.
In the Indicative platform, data consumers can work with data to ask their own questions and develop their understanding of the user journey, without the need to write SQL. This alone has the potential to ease the data workflow in many organizations where data practitioners are in high demand and data breadlines begin to form.
What’s more, when data consumers are free to make their own discoveries, they’ll typically ask more questions of the data and dive deeper than traditional analytics platforms would allow. Rather than being presented with a dashboard over which they have little control, product and marketing teams can use Indicative themselves to build an understanding of their users. They can perform complex analyses like audience segmentation, funnel analysis, churn and retention propensity analysis and more. And they can do all this within a user-friendly interface that’s designed to deliver insights quickly.
Indicative grants access to behavioral data to the data consumers who need it most. Best of all, the data surfaced in Indicative comes directly from the data warehouse without leaving the company’s cloud environment. The data warehouse remains the ‘brain’, and the data team retains total control and ownership of their data within their data stack.
Sending the best behavioral data to the best tools
Tools like Indicative are game changers when it comes to uncovering insights at speed, yet high-quality data is a prerequisite to a reliable data function. The conversation has evolved beyond the crude concept “garbage in, garbage out” to a more nuanced “richer data in, richer insights out”.
The better – and by better, we mean richer, highly-structured and more granular – data you can get into your data warehouse, the stronger the integrity of your analytics further downstream. For this reason, it’s worth investing in the best possible ingestion technology, i.e. a robust platform for collecting behavioral data at source.
Tools like Snowplow are ideal for this purpose. Snowplow is a behavioral data platform, architected to capture rich, behavioral data at scale from all your platforms and products, before sending your data to a central data warehouse or data lake. Much like tools such as Indicative, Snowplow puts you in control of your data, giving you the flexibility to define the structure of your data and decide how it’s processed, so your data arrives in an expected format your analysts and business teams can easily work with.
Taken together, Indicative and Snowplow form a complete system at opposing ends of the central data stack, upstream and downstream of the data warehouse. While one gives you the ability to capture the best behavioral data in a way that makes sense to your business, the other presents the data in an accessible, intuitive platform.
What’s more, Indicative comes with a dedicated integration for Snowplow built into its architecture, allowing you to plug Snowplow events straight into the Indicative platform with no need for transformation beforehand.
