The Analyti-fication of Applications

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In this special guest feature, Kyle Wild, CEO and founder of Keen IO, outlines ways for small companies with few resources hoping to compete in markets where analytics is a competitive weapon, must find a way to engage analytics capabilities. This is driving the push for adding analytics into applications. Kyle has been on the front lines of the data analytics revolution for the past 10 years as a developer and CTO. He is currently CEO of Keen IO, which helps businesses add customized, large-scale analytics and data science features directly into their web, mobile, or Internet of Things applications.

Virtually every leading company founded in the digital age has built its business on top of analytics, including Facebook, Google, Amazon, Netflix and many others. They’ve invested hundreds of millions in IT infrastructure, data scientists and engineers to get there. Using data science they’re able to understand the actions of their users or customers and make decisions, often in real-time, based on changing indicators, conditions and insights.

Meanwhile, companies with far fewer resources hoping to compete in markets where analytics is a competitive weapon, must find a way to mirror these capabilities. This is driving the push for adding analytics into applications.

Event vs. Entity Data

To understand this trend, let’s start at the beginning. With Event Data.

Unlike its more traditional counterpart, entity data, which describes objects and is stored in tables (i.e. databases), event data describes actions. Its structure allows many rich attributes to be recorded about the state of something at a particular point in time. For example, every time someone loads a webpage, clicks an ad, pauses a song, updates a profile, or even takes a step into a retail location, these actions can create an event, which can be tracked and analyzed. Event data spans so many channels and so many types of interactions that it paints an extremely detailed picture of what captivates buyers, readers, listeners, etc.

Simply put, event data is the fuel that drives the analytics engine. However, it is sufficiently unique from entity data that it demands a specialized infrastructure, architecture and access patterns. In the early days of data analysis, teams of data scientists and engineers were required to build the capabilities needed to process event data for large companies. This is changing. New building block technologies now enable even a single developer to capture billions of detailed interactions and begin running queries in seconds, accessing data programmatically and in real time. This makes it possible to build intelligent applications and services that use insights from event data, to personalize the user experience, and display information dynamically.

Weaving Event Data Analytics into Software

With event data analytics, companies can accelerate their businesses in ways that weren’t possible before. They can anticipate what users will need and take their products and services in the right direction. For example, they can show users extremely relevant content and demand higher ad revenue from top advertisers because of the engagement metrics they derive from analytics.

Just as organizations migrated from on-premise servers to cloud hosting and storage in the mid-2000s, many companies are starting to adopt data analytics enabled SaaS applications or embed pre-built analytics technologies (that they couldn’t build in-house) into their own applications. These applications can perform far more advanced, programmatic analysis, and make real-time decisions on how to engage the user — suggesting the right product, showcasing the right content, and asking for the right actions.

Event data analytics has fostered a revolution that is reshaping economies, industries and how companies compete. It used to be the exclusive purview of a few industry giants with the resources to build their own home-grown technology stacks. Innovative new technologies are leveling the playing field by making it possible to add analytics to custom or off the shelf applications.


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