Insight as a Service


There are three broad types of data that can be used for the creation of insights:

  1. Company data.  This is the data a company stores in a SaaS application’s database.  As SaaS applications add social computing components, e.g., Salesforce’s Chatter, or Yammer’s application, company data will become an even richer set.
  2. Usage data.  This is the Web data captured in the process of using a SaaS application, e.g., the modules accessed, the fields used, the reports created, even the amount of time spent on each report.
  3. Syndicated data.  This is third-party data, e.g., Bloomberg, LinkedIn, or open source, which can be integrated (mashed) with company data and/or usage data to create information-rich data sets.

Some of the issues that will need to be addressed for such services to be possible include:

  1. Permission to use the data.  For this to be possible, corporations must give permission for their company data to be used by the SaaS vendor for benchmarking.  For example, if Salesforce customers are willing to make their data available then their sales forces’ effectiveness can be benchmarked against that of peer companies.  It may be more likely for companies to give their permission if the data is abstracted or even aggregated in some way.
  2. Data ownership.  The ownership of usage data has not been addressed thus far.  Before creating and offering insights, ownership will have to be addressed by the SaaS vendors and their customers.  Once ownership is established, as I had written before, this data can, at the very least, be used by the SaaS vendor to provide better customer service or even to identify upsell opportunities and customer churn situations.  While some vendors, e.g., Netsuite, are starting to utilize parts of usage data, utilization remains low and scarce.
  3. Data privacy.  Company and usage data will most definitely include details that may need to be protected and excluded from any analysis.  The SaaS vendors will have to understand the data privacy issues and provide corporate clients with the necessary guarantees.  Thus far SaaS vendors have only had to make data security guarantees.  Privacy concerns around this data will be similar to those that currently surround the internet data that is being used to improve online advertising.
  4. Potential need for pure-play Insight-as-a-Service vendors.  The SaaS application companies may not prove capable of providing such insight services.  It may be necessary to create specialized vendors to offer such services.  Such pure-play vendors may have more appropriate and specialized know-how which will be reflected in their software applications (essentially analytic applications that can organize, manipulate and present insights).  In addition they will be able to offer a broader range benchmarking since they will be able to evaluate data across SaaS vendors.  However, having such vendors will also necessitate the move of company and usage data to yet another location/cloud thus increasing the security and privacy risks.
  5. Eligibility for accessing these insights and business models under which they can be offered.  One approach would be to only offer such insights to as a separate product by the SaaS application’s vendor to its customers.  Another approach, particularly if the insights are to be created by a pure-play insights vendor, would be for such vendors to create data coops.  Under this scheme corporations contribute company and usage data to the coop, the Insight-as-a-Service vendor analyzes all contributed data, and only offers the results to the companies that belong to the coop.  For this service the vendor can use an annual subscription fee not unlike what industry analysts like Forrester and Gartner charge.  Internet data companies such as Datalogix, that has created a coop with retail purchase data, can serve as good models to consider.  Another business model may be for the vendor, either the SaaS application vendor or the Insight-as-a-Service vendor, to share revenue with the companies providing the company and usage data.  Internet data exchanges like Blue Kai and eXelate would provide good business model examples to imitate.
  6. Geography.  As we’ve learned with consumer internet data, each country approaches data differently.  For example, European countries are more restrictive with the use of collected data.  SaaS companies must try to learn from the relevant experiences of internet data companies as they determine how to best offer such insight services.
  7. Data normalization.  Usage data will need to be normalized and then aggregated since each customer, and maybe even each individual user, uses a SaaS application differently.  This could be tricky.

Hosted applications need not apply.  Not all vendors will be able to offer such services.  For these services to be successful, data from the entire customer base needs to be aggregated and organized.  This implies that vendors claiming to offer SaaS solutions when they are only offering single-tenant hosted solutions deployed in, what amounts to, private clouds will not be able to provide such insight services.  In fact, multi-tenant architectures will be even more important for insight-generation because they make data aggregation easier.

Insight-as-a-service can become the next layer of the cloud stack (following Infrastructure-as-a-Service, Platform-as-a-Service and Software-as-a-Service).  In addition to SaaS application vendors that can start offering such services, there exists an opportunity to create a new class of pure-play Insight-as-a-Service vendors.  Regardless, vendors will need to start addressing the issues and many more that I can’t anticipate at present.  But since surveyed customers are already starting to ask for such services, it is time to start creating them.  It means that the time for Insight-as-a-Service has arrived.

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