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We hear and read a lot about big data these days. This is because we are generating big data at increasing rates from a variety of sources such as the social web, mobile and other types of apps and sensors. So, what is big data? Big data is characterized by its shape (structured or semi-structured), the speed with which it is generated, the variety of data in its data set, and its size (petabytes, exabytes, etc.). It is estimated that this year we will generate 2.7 thousand exabytes of data. In a recent speech Eric Schmidt, Google’s executive chairman, reported that we are generating 5 exabytes of data and information every 2 days, and the pace is increasing. Different industries are generating and capturing different quantities of data. Moreover, the data from each industry has different value. In other words, just because we can capture a lot of data, doesn’t mean that all the data will be useful. As Vinod Khosla recently wrote, reducing, filtering and processing data streams to deliver the information or action that is relevant to you, is extremely crucial to our ability to deal with the data we generate. For this reason, the data we generate data must be analyzed so that we can realize its potential. SaaS applications are quickly becoming part of the big data ecosystem as producers, managers, and analyzers of big data. In this talk we explore the relationship of SaaS applications and big data through a concept called Insight as a Service

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Over the past 10+ years we have seen 3 waves of SaaS applications We have transitioned from on-premise apps that we moved to the cloud (the first wave), to applications that can only exist in the cloud because they take advantage of the cloud’s unique features, e.g., collaboration enabler, (the second wave), to data-driven applications that capitalize on the social web, mobility and crowdsourcing to automate business processes. Insight as a Service applications, which are themselves SaaS applications, are part of this third wave. Companies like 8thbridge, Jbara, Totango, and Visier have developed Insight as a Service applications. With each wave of SaaS application we have seen 1. Increasing adoption of SaaS applications by both SMBs and large enterprises. 2. Increasing number users per application. This means that we now have better statistics

about the overall usage of SaaS applications in general and specific applications in particular.

3. Applications operating on more data and generating more data.

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SaaS applications are processing and managing big data Structured corporate data semi-structured social data Syndicated data The data stored in these applications fulfills all the characteristics of big data. It is: 1. Structured or semi-structured 2. Large 3. Historical and real time. In fact the ability to combine and analyze both of these types of

data can allow corporations to “react in the now.” 4. Comes from all the different users accessing a particular app 5. Comes from all the different customers licensing a particular app 6. Reflects data from many integrated SaaS apps as well as behind-the-firewall data

Therefore, its analysis can provide several benefits.

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SaaS applications are also capturing/generating big data that includes: 1. Application usage data. For example, how many times an application was accessed, by

whom, what activities were performed during the session, how long the session lasted, etc. 2. Application performance data: Examples of this data include, how well the application is

performing, what is the response time, what is the uptime, how well are the APIs performing.

3. Customer performance data (with regards to the type of SaaS application): For example, a SaaS application for corporate performance management such as that offered by Host Analytics can capture data measuring how well a particular company’s business is doing. Data that can define a company’s performance includes DSOs, outstanding AR, delivery times, etc.

4. Customer feedback data: The feedback a company’s customers (corporate or consumers) provide.

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Which means that the insights that are derived from the analysis of this data can drive actions.

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Today we face 4 problems with the analysis of big data including the analysis of SaaS application data

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For example, how will a change in the lending rate by the Fed and a weather change in Europe can impact the supply chain and production schedule for all-weather boots by Nike? What actions should be taken by suppliers and logistics partners? How are actions by partners be considered?

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By 2018 the US alone will face a shortage of 140-190K of people with deep analytical skills as well as 1.5M managers and know-how to use the analysis of big data to make effective decisions

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Whatever few insights we can derive from the data we collect, we can only do so manually and only with the help of specialized personnel, the connectors, who can translate a business problem to a data problem and the resulting patterns into insights and actions For example, to determine how to allocate marketing budget to reduce customer churn a cable company must use data scientists to develop predictive models that enable it to score its customers in terms of their probability to abandon their service, and maybe even their probability to upgrade to a higher level of service. To do that, the Translator first working with the Business User must understand the nature of the churn problem and the data that is available to address this problem, and then turn around and present the appropriate definition of the problem and the available data to the data scientist who will proceed to develop the model. Once the model is developed and scores have been created, the Translator must again work with the Business User to determine which segments of the at-risk customers are worth paying attention to (insight), what percent of the marketing budget to allocate to each such segment (insight), and what actions to take with this budget. For example, maybe offer a particular package of premium channels for free for a period of time, or maybe upgrade the broadband internet service for another segment.

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Insight as a Service is provided by a new set of analytic applications. These applications are cloud-based. They are not only able to analyze data but they are also able to a) derive insights from these analyses and b) propose specific actions to be taken by the user based on each insights.

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Insight as a service applications may be vertical and horizontal. They come in two types. The first type is built to operate on data that is produced and managed by SaaS, or behind the firewall, transactional applications, such as ERP systems and CRM systems, and may be supplemented with syndicated or open source data. A good example of this type of Insight as a Service application is offered by Host Analytics. This analytic application operates on financial data typically stored in ERP and HCM systems to provide to a CFO and the members of a finance department with insights and actions regarding the corporate or departmental budgeting and planning data. The application also enables business users to collaborate in order to generate such insights and the associated performance-enhancing actions. The Host Analytics application can also benchmark the budgeting and planning data of two or more corporate customers from the same industry, for example Baptist Health Hospital and Mission Hospital in order to provide insights about best practices and actions on how to improve a customer’s performance. Another example is Visier’s Insight as a Service application that analyzes data stored in HCM systems to provide insights and actions regarding employee

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The second type is built to operate on data captured by SaaS applications including usage data, performance data, etc. A good example of such Insight as a Service application is Jbara’s application that analyzes customer performance and customer feedback data of other SaaS applications such as Marketo’s marketing automation SaaS application to provide insights around customer satisfaction, a customer’s probability to churn, etc. and the actions that the SaaS vendor can take based on these insights. As with the first type of Insight as a Service applications, this second type is also used for benchmarking. For example, Is the company best in class in responding to customer complaints? Is its Customer Acquisition Cost best in class? Is its churn rate best in class?

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More specifically I see each Insight as a Service application incorporating five components 1. Task specific analytics. For example, Jbara’s Insight as a Service application analyzes a

SaaS vendor’s customer acquisition costs and attrition rates, benchmarks them against best in class costs and rates and determines where they fall.

2. An explicit description of the business process automated by a specific class of SaaS applications. For example, Jbara’s application has an explicit description of the customer acquisition business process, as this is automated by SaaS applications such as Marketo’s.

3. Vertical industry knowledge. For example, customer acquisition in B2C ecommerce. 4. An action generator. This component generates candidate actions the user can take based

on the insights that have been generated by the analytics. For example, Jbara’s application offers actions on how to reduce customer acquisition costs based on the results of its analytics, e.g., how much a company’s customer acquisition costs vary from best in class costs.

5. User interface

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