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 and later elaborated on my blog: http://blog.tridentcap.com I am including the presentation and the notes because they provide further details on the concept and some examples

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2. 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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3. 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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4. 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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5. 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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6. Which means that the insights that are derived from the analysis of this data can drive actions.
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7. Today we face 4 problems with the analysis of big data including the analysis of SaaS
application data
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9. 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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10. 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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12. 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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13. 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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14. 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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15. 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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16. 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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