As a former Salesforce CMO and now CEO of a Salesforce customer, partner and vendor, Dreamforce has, is and no doubt will continue to be a pivotal event in my year. And when the parties are over and the community has returned home, what is left is the agenda for what products will go from pilot to general availability (GA) over the next 12 months; Dreamforce is Salesforce’s great product planning showcase.
Data and analytics increasingly form the foundation
This year, like last, analytics featured prominently at Dreamforce. But rather than just being about Wave and business intelligence (BI), analytics is maturing in important new ways. The Internet of Things (IoT) Cloud brings a new level of big data into the Salesforce platform and ecosystem. This will cause major disruptions among the data providers in Salesforce’s ecosystem I expect, with both new data sources and more data sources becoming available in more easily configurable fashions. Not only will this influx create new opportunities for many, it will also drive quite a transformation in the ways data connects to the Salesforce platform before Dreamforce 2016.
In a platform-centric world, the ecosystem wins
The ecosystem of partners and vendors is more important than ever to Salesforce. As Alex Dayon, President of Product, put it, “You are always stronger with an ecosystem. It’s important that we keep our system open to partners.”
This year Dreamforce felt less like a branding event and more like a network collective. It has always been the place to meet around as much as participate in, and I felt this more than ever this year. The number of clouds, platforms and industries does make you wonder how much longer the power of a killer keynote can keep it all hanging together.
Predictive analytics becomes more critical in an “IOT Cloud” world
Seeing data science grow to the status of a trend along with cloud, social, mobile and so on is a huge transformation. But it’s only natural with the data explosion. Without predictive analytics (not just business intelligence), it will be hard to weave this data into business processes. If people are confused today by all the use cases loosely grouped under the term predictive analytics, it might only get more complex.
But as a corollary, we’re also starting to see (as noted in The Wall Street Journal piece on Dreamforce and “The Data-Driven Rebirth of the Salesman”) that the real business value comes from aligning predictive analytics to these specific business processes — for example, how InsideSales is enabling sales processes, Leadspace is transforming demand generation processes and Preact is transforming customer retention.
Predictive analytics has sometimes been seen as a cool piece of technology, but now people are realizing it is an enabler of better data usage and a source of insights to improve specific business processes. This shift will continue to accelerate.
For us at Leadspace it was great to see the discussion in predictive move beyond the conceptual and esoteric to the practical, rooted in real use cases and business results (some of which we outline in our ebook, Top 10 Use Cases for B2B Predictive Analytics). I’m sure this trend will continue as Salesforce implements its Dreamforce vision over the coming months.