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Now, the question is, who is the key benefactor within an organization from this approach. Itis
primarily the Chief Technology Ofcer (CTO) or the Chief Information Ofcer (CIO) who is
tasked with running the Information Technology (IT) organization within a specied budget, but
at the same time, the chief must ensure that right technology decisions are being made for the
production, storage and processing of data to support analytics and business decision-making.
As you now understand, the outcome of this approach is an extension of the traditional data
warehouse or the development of a modern data platform.
In this scenario, obviously, the primary justification is cost reduction as we discussed.
Thesecondary justification is value addition through capturing and processing newer types of
data, thereby making analytics and decision-making more effective.
Let us now consider the second use case for a Big Data implementation.
11.2.2 Big Data Primarily for Enhanced Value
In this scenario, the application of Big Data technologies is more about adding more value to
reporting, analysis and decision-making in an enterprise.
In the earlier sections of this book, we have seen that the information available in traditional
sources of data in an enterprise is limited. Today, there are tremendous opportunities of capturing
information and intelligence about customers, partners and about business ecosystem in general
from a variety of data sources that exist outside an organization.
For example, it is possible to understand a customer better by gaining an insight into his
profile, his network and friends on social media. It is possible to understand the sentiment of
the customer towards certain product or service by reading his online reviews, his posts in social
media or his comments in discussion forums. It is possible to understand that the customer has
plans to purchase certain product or subscribe to certain service anytime soon. It is possible to
determine whether a customer is an influencer in a group of potential buyers and hence, needs
to be taken care of in a special manner, so that the customer forms a favourable perception about
the product or service.
Now, all these data we study is large, rapidly changing and originates from a wide variety, and
hence, as established in earlier sections of this book, it is collectively called ‘Big Data’.
Now, an organization with traditional data storage in Relational Database Management System
(RDBMS) or a well-structured and well-managed enterprise data warehouse will neither have the
infrastructural capability nor the required processes for capturing, storing and processing such
kind of data that will provide the organization with a competitive edge at low cost, because of its
ability to better understand customers, partners and the ecosystem in general, to make better and
timely decisions, and to respond better to changes.
Here comes the second use case for Big Data implementation, where an organization wants to
embrace Big Data technologies not for cost optimization as the key driver, but to get increased
value out of data.
Let us now look at some of the applications of this use case (Figure 11.2):
a. Better Understanding of Business Entities: This allows the enterprise to capture a wide
variety of rapidly changing data in large volumes to create a complete, rounded, holistic
view of business entities like customers, assets, etc. Elaborating more on the example of
assets, in a manufacturing plant, the downtime of a machine would mean loss of produc-
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FIGURE 11.2 Big Data implementation use case 2: value additions
Scenario 2
Big Data for value
addition
Better analytics through
more data
Wider varieties of data
Better understanding of
business entities
tion and associated financial losses. Now, in addition to basic information about machines,
the enterprise also has the right technology and process in place to continuously capture
machine data from sensors attached to machines, it will be possible to predict when a
machine might go down, and accordingly, plan and execute proactive maintenance activ-
ities, so that the precious downtime hours are not encountered.
b. Better Analytics Through More Data: With a Big Data implementation in place, the
effectiveness of analytics and decision-making is no longer constrained by the volume
of data an enterprise can capture, store and process. This provides an organization with
competitive advantage in the marketplace at reasonable costs.
c. Wider Varieties of Data: Enabled with Big Data implementation, an organization can
now handle different varieties of data. We have seen how Big Data implementation can
benefit an organization through unstructured data, like pictures, movies, textual content
and semi-structured data, like XML, JSON or metadata information for images and videos.
It is important to note here that the benefactors of this use are not just the CIOs or the CTOs,
because this scenario is not just about saving expenses. The benefactors of this use case are all the
senior stakeholders of the organization, like the Chief Executive Ofcer (CEO), the Chief Finance
Ofcer (CFO) and others.
The outcome of this use case is enhanced insight and business effectiveness.
As for priorities, unlike the first scenario, in this case, the primary objective is value addition
and the secondary objective is cost reduction.
Did You Know?
We discussed the two key scenarios of Big Data implementation. The rst scenario is
primarily about cost reduction and the second is about value addition. It is interesting to
note that from a strategy perspective, a number of organizations embark on the Big Data
journey from the rst use case, and move on to the second, as they gain more maturity and
technology expertise.
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