By Brynne Henn - December 18, 2020
As the price of data warehousing continues to fall, companies can afford to track almost every business metric at an increasingly fine granularity. And, along with these metrics, companies can afford to capture more useful context about each individual transaction, customer, and product use.
In theory, this data gives every organization the opportunity to develop a data-driven culture. The data should guide teams towards a better understanding of what actually drives loyal, satisfied customer behavior, what new products and services to offer, and how they should optimize every customer interaction.
However, this influx of data presents a new challenge: where to prioritize efforts? Without a clear direction for where to look first, what to investigate, or how to uncover insights, the opportunity is obscured by its complexity and dimensionality. Without a different approach, today’s enterprise data is challenging to operationalize.
Data width, not depth, is the new performance bottleneck for analytics. With more columns and features to analyze, this rising complexity creates three big blockers that prevent teams from getting the ROI they expect from their data: data prep and discovery, insight generation, and insight explanation.
However, a more augmented analytics stack can remove these barriers by shifting the burden of these tasks away from individual analysts. Not only does this accelerate time to insight, but it frees up data teams to do the more valuable work of synthesis, recommendation, and creative thinking.