Data Science as a Service – What it Means for Customer Intelligence

Recently, I discussed how using Data Science as a Service can change your business for the better. From improved internal workflows to increased ability to predict your customer’s needs, implementing data science as a service can be a huge benefit to your business.

To drill down a little further, as companies start to focus more attention on understanding and optimizing how their customers interact with their brand across channels, data science as a service can enable critical insights to be democratized to hundreds, if not thousands, of individuals within the enterprise.

How Democratization Happens

The first and most intuitive change is that analysis workflows are becoming more approachable to a wider audience. Employees get the ability to log into a service in the cloud that houses the data needed to provide insights instead of making a request to the IT shop whenever they need information. Additionally, machine learning reduces the intellectual horsepower needed to gain that insight.

Second, insights are democratized by surfacing insights to users in their existing business workflows, even though they don’t identify as data analysts. There’s power in “hitting the business user in stride” with insights in their daily workflows. This is the point of my recent blog post about Adobe Sneaks.

When you think about it, this idea aligns with Adobe’s corporate direction from the day of our founding. We have always tried to democratize expertise with our solutions. We will continue to invest in making the process of deriving insights from large data sets much easier and to put those insights into the hands of every employee who can benefit from better understanding the customer, their actions, and their preferences.

New Roles Benefitting From Data

We often think of marketers and analysts driving the need for analytics but that’s not the whole story; recently we’ve been seeing an increase in demand for insights from creative personas, sales personas, and (hitting close to home as a product guy myself) people who drive product innovation. Looking at what’s worked, what hasn’t, why you’re experiencing a particular anomaly with a particular customer persona, or the paths that customers are taking through your customer experience – all these things contribute to what the next version of your product actually looks like and how you grow to serve new personas.

Our goal is to use data science as a service to make customer intelligence as optimized and accessible at all levels of the organizations as possible. Over the next few posts, I’ll dive into how personas not traditionally associated with analytics can benefit from access to customer intelligence information.