On the Trail at Dreamforce – Data Science Goes Mainstream

It’s Dreamforce week, which is always a great time to connect with customers, meet new prospects and hear what’s on their minds. This week everyone is talking about how to become a data driven enterprise.

Every company knows that data is a competitive advantage for their organization, but how they apply it is not as easily understood. For years companies have hired teams of data analysts and data scientists. Early thinking was to wall them off in a separate group to provide “data as a service” to other teams. Today, more companies are embedding data scientists directly into the lines of business and functional groups such as sales and marketing. Now, with the introduction of Salesforce Einstein and predictive apps like Lighthouse for Sales that work directly within systems of record, it’s not just data scientists who are pulling and analyzing data. It’s the doers.

As Marc Benioff said in his keynote yesterday “Einstein is everyone’s data scientist.” No longer do we need to solely rely on individuals with PhDs to crunch the data for us. Capabilities in the platform itself can surface insights that can shape behavior and even predict outcomes, such as the likelihood of a lead converting to an opportunity. In some cases, it could prescribe a course of action, such as setting up a customer meeting to address a problem identified on a service ticket. Companies like Salesforce, Google, Microsoft and the partner ecosystems surrounding these platforms are making it easier than ever for the doers to understand the data, to see patterns and to take action.

But at the same time, it’s not easy for customers to know where to begin. What they need now, what’s overkill for their organization, and what to make of all the marketing hype around machine learning, artificial learning, and predictive analytics.
A lot of customers I spoke to this week are just getting started on their analytics journey. Many of them have only recently implemented Wave, and they are still trying to figure out how to wring the most value from it.

My advice is to start small. Identify the problem that is causing the most fits across the organization, fix that and move on to the next problem. I call this execution-oriented transformation, and it’s what enables organizations to deliver value right away and concretely show how data can change behavior and improve performance. You can talk about analytics all day long, but until you see it in action, it’s just words.

Take one of our large health services customers. They called us on Christmas Eve and were in need of  sales dashboard for a Board meeting that was less than three weeks away. We worked with them to create a standard definition for the data they needed to present to the Board and delivered the initial dashboard in time for the meeting. Over time, we fine-tuned the dashboard to deliver even more business value and now they are using dashboards from Wave to run their board meetings.

If you’d like to hear more about this story, please join one of the many Optum sessions at Dreamforce, including SpringML’s lunch & learn today at 12pm with Maria Perkins, VP of Growth Technologies at Optum held at the Wave Analytics Lounge at 111 Minna.