Enterprises undergoing digital transformations move through three phases of maturity: Commodity Storage, Self-service Everything, and Machine-learning Ubiquity. At each stage, enterprise data technology innovations have served end users seeking to get the most value out of their data.
Many enterprises have reached that second stage—using self-service data technologies to empower end users to access and consume data on their own. But the convenience of self-service data technology is self-limiting: As enterprise data grows, end users’ ability to find it, figure out what to do with it, and gain insight from it gets more difficult. And that’s a complex challenge only exacerbated by static, technology-reinforced, self-service processes.
An emerging third phase responds to that challenge, and helps enterprises move into a dynamic data operations environment characterized by smart workflows, self-optimizing data workflow orchestration, and an enterprise commitment to maximizing data-derived value. In this new world, enterprises leverage machine-learning technologies to craft DataOps models that learn with iteration, and scale with continuous improvement. Coupling that approach with embedded analytics can deliver insight at the point of its greatest potential impact: where data meets decision.
In this report, Blue Hill Research examines how digital transformations have evolved, and looks at how innovative enterprises are using machine-learning-enabled technology like GoodData to accelerate data flow, shorten communication spans, empower line-of-business stakeholders, and deliver greater bottom-line value (while overturning a few old-school business models in the process).
To read the rest of this report, please fill out the download form.