Page 21 - Building Big Data Applications

Chapter 1 Big Data introduction 15

Risks and pitfalls

No success is possible without identifying associated risks and pitfalls. In the world
driven by the Internet of Things, the risks and pitfalls are all similar to those we need to
handle on a daily basis in the world of data. The key here is that, data volume can cause
problems created by excessive growth and formats.

Lack of data: A vital area to avoid within the risks and pitfalls is a lack of data,
which is not identifying the data required in this world driven by the Internet of
Things architecture. This pitfall can lead to disaster right from the start. Be sure to
define and identify the data to collect and analyze, its governance and stewardship,
its outcomes and processingdit is a big pitfall to avoid.
Lack of governance: Data lacking governance can kill a program. No governance
means no implementation, no required rigor to succeed, and no goals to be
measured and monitored. Governance is a must for the program to succeed in the
world of the Internet of Things.
Lack of business goals: No key business actions or outcomes can happen when
there are no business goals established. Defining business goals can provide clear
direction on which data and analytics need to be derived with Internet of Things
data and platforms. Two important requirements for these goals helps avoid this
important pitfall: one is executive sponsorship and involvement, and the other is
governance. Do not enter into this realm of innovative thinking and analytics
without business goals.
Lack of analytics: No analytics can lead to total failure and facilitates nonadoption
and a loss of interest in the Internet of Things program. Business users need to be
involved in the program and asked to define all the key analytics and business ap-
plications. This set of analytics and applications can be documented in a roadmap
and delivered in an implementation plan. A lack of analytics needs to be avoided
in all programs related to the Internet of Things.
Lack of algorithms: No algorithms can create no results and translates to non-
adoption of the program. A few hundred algorithms can be implemented across
Internet of Things platforms and data. These algorithms need to be understood
and defined for implementation, which requires some focus and talent in the orga-
nization both from a leadership and team perspective. Algorithms are expected to
evolve over time and need to be defined in the roadmap.
Incorrect applications: The use of incorrect applications tends to occur from busi-
ness users with a lack of understanding of the data on the Internet of Things plat-
form, and it is a pitfall to avoid early on. The correct applications can be defined
as proof-of-value exercises and executed to provide clarity of the applications.
Proof of value is a cost-effective solution architecture build out and scalability for
the Internet of Things platform.