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How ‘Insights-as-a-service’ is growing based on big data

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Insights-as-service should deliver not only actionable insights, but also a concrete plan to use them. We review different types of insights as a service, how they are used with big data, deployment challenges, and future trends.




Big data represents a goldmine of opportunities for companies. To sell their products and services, companies need to get customer insights continuously and big data offers that. However, it is not easy to extract insights, given the investments needed.  Insights-as-a-service solves this problem specifically, not to mention the values it adds. Insights-as-service is a software service or solution that delivers actionable insights. The services include not only insights but also a concrete plan to utilize the insights. The service is delivered just like any other SaaS product. It is hosted in the cloud and you need to buy subscriptions. Of course, Insights-as-a-service depends on other SaaS solutions and other sources for data and analytics. Large, medium-sized and small companies have been relying on this service because of various reasons.

What is Insights-as-a-service?

As already defined earlier in this article, Insights-as-a-service refers to cloud-based services that not only provide insights to business corporations but also concrete steps to leverage the insights to achieve business goals. Insights-as-a-service is fundamentally different from other SaaS offerings in the sense that other SaaS offerings offer insights and analytics while Insights-as-a-service offer action plans. However, Insights-as-a-service depends on other SaaS solutions for data and insights.

Insights-as-a-service can be offered in multiple ways or forms. The following examples show some typical ways of offering Insights-as-a-service:

  • Business benchmarking services which help corporations compare their businesses with that of their rivals or peers who have hired the services of the same SaaS vendor.
  • Business process improvement services based on the usage of SaaS applications. For example, reduce the budget on search keywords by using the SEM application’s keyword search module.
  • Improving business productivity by integrating syndicated data with client data and reducing client service request response time.

Types of big data Insights-as-a-service uses

Insights-as-a-service cannot deliver independently. It depends on a lot of data sources for delivering actionable insights. Mainly, Insights-as-a-service uses the following data types:

  • Company data: This is the data stored by the company in the SaaS applications. Company data could encompass a wide range of data such as financial data and results, stocks, employee details, keyword performance, and business process data.  Basically, the company decides the type of data it stores.
  • Usage data: Usage data refers to the data captured during the usage of the SaaS applications. Whenever a module is accessed, used, configured or removed, the data is captured in a web-compatible format. For example, if a few fields in an application are frequently accessed, that constitutes an interesting insight.
  • Syndicated data: This is the data collected from third party sources such as LinkedIn, Bloomberg, other open source applications and Facebook. When syndicated data is combined with the two types of data described above, it can result in some deep, relevant insights.

What should not be missed is the correlation between the SaaS growth and that of the Insights as Service solutions. Both are directly proportional. So, given the fact that SaaS is on the rise, expect an upsurge in the Insights as Service sector. The image below shows the growth statistics of SaaS.

saas-growth-2008-2011


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