I want to take a quick breather from writing about corporate innovation and return to another topic of this blog: big data and insight as a service. Host Analytics, one of my portfolio companies, recently completed a $25M financing round. Host Analytics offers a cloud-based Enterprise Performance Management (EPM) Suite that streamlines a corporation’s planning, close, consolidation and reporting processes. But it is what they are enabling for the enterprise that is important to write about. Host Analytics has moved from being an EPM company, to being an insight generation company.
On July 15 IBM and Apple announced an exclusive partnership. There are several components to this partnership that have been addressed elsewhere (here and here) but of most interest was the commitment to develop 100 industry-specific mobile analytic applications for the enterprise. As I had written, the broad adoption of smartphones and tablets by employees, customers and partners, combined with a BYOD strategy, is driving corporations to rethink their enterprise application strategies. They are starting to mobilize existing applications and embrace a mobile-first approach for the new applications they are licensing or developing internally. Analytics-based insight-generation applications represent a major category of these new applications. Recognizing this trend, I and many other venture investors, have been aggressively funding startups that develop mobile enterprise applications.
A few days ago I presented a webinar on Insight as a Service. In the presentation I tried to provide further details on the concept which I first introduced here and later elaborated here. I am including the webinar presentation (click on the slide below) and the notes because they elaborate further on Insight as a Service and provide some examples.
In a previous post introduced the concept of Insight as a Service and described some of the issues that will need to be addressed for such services to be possible. Insight as a Service refers to action-oriented, analytic-driven solutions that operate on data generated by SaaS applications, proprietary corporate data, as well as syndicated and open source data and are delivered over the cloud. This definition is meant to differentiate Insight as a Service, which I associate with action, from Analytics as a Service, which I associate with data science, and Data as a Service which I associate with the cloud-based delivery of syndicated and open source data. For example, a cloud-based solution that analyzes data to create a model that predicts customer attrition and then uses it to score a company’s customer base in order to establish their propensity to churn is an Analytics as a Service solution. On the other hand, a cloud-based solution which, in addition to establishing each customer’s attrition score, automatically identifies the customers to focus on, recommends the attrition-prevention actions to apply on each target customer and determines the portion of the marketing budget that must be allocated to each set of related actions, is an Insight as a Service solution.
The survey data presented in Pacific Crest’s SaaS workshop pointed to the need for a variety of data analytic services. These services can be offered under the term Insight-as-a-Service. They can range from business benchmarking, e.g., compare one business to its peers’ that are also customers of the same SaaS vendor, to business process improvement recommendations based on a SaaS application’s usage, e.g., reduce the amount spent on search keywords by using the SEM application’s keyword optimization module, to improving business practices by integrating syndicated data with a client’s own data, e.g., reduce the response time to customer service requests by crowdsourcing responses. Today I wanted to explore Insight-as-a-Service as I think it can be the next layer in the cloud stack and can prove the real differentiator between the existing and next-generation SaaS applications (see also here, and Salesforce’s acquisition of Jigsaw).