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.
Insight as a Service
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).
In my last blog I tried to define the concept of insight. In this post I discuss insight generation. Insights are generated by systematically and exhaustively examining a) the output of various analytic models (including predictive, benchmarking, outlier-detection models, etc.) generated from a body of data, and b) the content and structure of the models themselves. Insight generation is a process that takes place together with model generation, but is separate from the decisioning process during which the generated models, as well as the insights and their associated action plans are applied on new data.
A little over two years ago I wrote a series of blogs introducing Insight-as-a-Service. My idea on how companies can provide insight as a service started by observing my SaaS portfolio companies. In addition to each customer’s operational data used by their SaaS applications, like all SaaS companies, these companies collect and store application usage data. As a result, they have the capacity to benchmark the performance of their customers and help them improve their corporate and application performance. I had then determined that insight delivered as a service can be applied not only for benchmarking but to other analytic- and data-driven systems. Over the intervening time I came across several companies that started developing products and services that were building upon the idea of insight generation and providing insight as a service. However, the more I thought about insight-as-a-service, the more I came to understand that we didn’t really have a good enough understanding of what constitutes insight. In today’s environment where corporate marketing overhypes everything associated with big data and analytics, the word “insight” is being used very loosely, most of the times in order to indicate any type of data analysis or prediction. For this reason, I felt it was important to attempt defining the concept of insight. Once we define it we can then determine if we can deliver it as a service. During the past several months I have been interacting with colleagues such as Nikos Anerousis of IBM, Bill Mark of SRI, Ashok Srivastava of Verizon and Ben Lorica of O’Reilly in an effort to try to define “insight.”