SSENSE Data Mesh: Going From Vision to Value — Part 1 of 2

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” ― Jim Barksdale

At SSENSE, we want to further unleash the value of data in order to increase agility, drive innovation, and improve efficiency. For us, data is the gateway to new opportunities and improving our clients’ experience. It’s limitless in use and the amount generated is increasing at an exponential rate, largely due to the growing number of business models and the percentage of customers subscribing to our platform. While data is abundant and increasing at a rapid rate, we face the challenge of getting value from data at scale. Simply producing or storing it doesn’t automatically generate value. Value is realized by creating a culture and operating model that uses the data to invent on behalf of our customers and partners using actioned insights, analytics, and AI/ML. Getting the optimal value out of data occurs when you transition from data-aware to data-informed to data-driven.

In this article I will touch on the motivation and pressure points to move towards data mesh, the divide between the operational and analytical data world, the inflection point for SSENSE, and the fundamental principles of data mesh.

Data-Aware to Data-Informed to Data-Driven

Conversations about data-driven enterprises often focus on big data tools, and the breakthroughs that have made storing, processing, and analyzing data faster and cheaper. While these are all important, creating a data-driven culture across the organization is essential to go beyond just a few successful data initiatives and islands of excellence limited to certain business areas.

The SSENSE Data Platform is responsible for establishing innovative methods for accessing, governing, and interacting with our organization’s data. We make data/information more discoverable, functionally usable, understandable, trustworthy, interoperable and valuable through advancements in data analytics adoption, data governance and quality. The intent is simple: to collect data and use it intelligently, turning data into actionable insights quickly and cost-effectively.