Page 121 - Building Big Data Applications

118 Building Big Data Applications

includes text, image, audio, video, and machine data. To deliver these powerful appli-
cations and their insights, we need a foundational structure for analytics and visuali-
zation of the data. Apart from the data, we need to have subject matter experts who can
understand the different layers of data being integrated and what granularity levels of
integration can be completed to create the holistic picture. This is the storyboard re-
quirements to paint the application.
Big data applications programs provide a platform and the opportunity to measure
everything across the enterprise. The effect of this process is the creation of transparency
across the different layers of data, its associated processes and methods, exposing
insights into potential opportunities, threats, risks, and issues. All users from different
business teams and their executive leaders, when they get access to this application and
its associated insights and data layers, gain better understanding of the decisions and are
able to provide more effective guidance to the enterprise.
Big data application can be defined as the combination of visualization and data
mining techniques along with large volumes of data to create a foundational platform to
analyze, model, and predict the behavior of customers, markets, products or services,
and the competition, thereby enabling an outcome-based strategy precisely tailored
to meet the needs of the enterprise for that market and customer segment. Big data
applications provides a real opportunity for enterprises to transform themselves into an
innovative organization that can plan, predict, and grow markets and services, driving
toward higher revenue.
Big data application storyboarding is a process of creating a narrative. The narrative
will include all the specific steps of what we need to discover in the storyboard, the
actual data and logs that will be used in the storyboard, the outcomes, analysis, and all
identified steps of corrective actions to rerun the storyboard. The difference in this
storyboard is the use of technology to rerun the process with different tweaks to check
the outcomes. The experiments in research cannot be reexecuted for 100% validation,
but the outcomes can be analyzed and visualized in the storyboard multiple times. Think
of the experiments for cancer research or genetic chromosome sequencing studies or
particle physics experiments or chemistry experiments.
The sequences of this storyboard can be altered as needed, the reason being
infrastructure is very cheap and scalability is foundational, the application-specific
requirements will be deliverable based on user-specific access and security. The end
result of how the user reacts to the application and what they will want to explore and
discover are all recordable as interactive logs and can be harnessed and replayed
multiple times for visualization. This is where the new skills and delivery mechanisms
are all used in the new world of data and analytics. The complexity of every visualization
is documented as user interactions and each step can be broken down and managed.
The formulas and calculations in the sequences are very much managed as operational
data analysis and discovery to data lakes to data hubs and analytical layers are all
captured and each layer will provide its own hits and misses, which means the actual
analytical outcomes will be 99.9999% accurate.