What is Data Science as a Service?
Recently, I had the pleasure of writing a series of articles for Data Informed. The focus was data science as a service**—**what it is, how it can help your company, and how it can ultimately improve your bottom line. Although this is something we discuss here at Adobe frequently, the benefits of looking to the cloud for your data science capabilities aren’t as well-known as they should be.
In this Data Informed series, we discussed how data science as a service allows us to get to know and predict our customers’ needs in a way that businesses have not been able to do for a hundred years. We do it by using machine learning developed to understand how customers interact with your brand across a myriad of channels. The result is the ability to engage at scale in an informed way with your customer, creating a much higher level of service that customers appreciate.
It is one thing for a well-trained and experienced data scientist to be able to get insight from reams of customer data, but it’s another thing altogether to make data science accessible to hundreds of business users in an enterprise. To this end, I touched on how you can better utilize data science as a service in your internal workflows. This democratization of data begins by organically surfacing data in existing workflows. This can allow your employees to access insights in stride and in real time, thus making them more effective and productive, and allowing them to better service your customers.
Throughout the series, I focused on defining data science as a service. A quick online search indicated that although many companies offer this service, there’s no great definition of what it is online. To help users get a solid understanding of what data science as a service is, I worked to define the tools and techniques involved.
Data science as a service isn’t a brand new concept but it’s one that we’re starting to see enterprises really benefit from at scale_._ They’re brands you’d recognize. The experiences they create for their customers are unique and differentiated from their competitors, and their businesses benefit materially as a result. Is there a reason you haven’t begun implementing these tools in your enterprise?