Page 125 - Building Big Data Applications

122 Building Big Data Applications

spend for the consumer. This type of analytics and personalized services are being
implemented on a global basis by many countries.
Big data applications can be used to create an effective strategy for healthcare
providers. Today whole-body scanners and X-ray machines store data electronically and
this data is hosted by datacenters of large equipment manufacturers and accessed by
private cloud services. Harnessing the infrastructure capabilities, different providers can
study the patient data from multiple geographies and their reviews can be stored
electronically along with recommended treatment options. This data over a period of
time can be used as an electronic library of treatments, disease state management, and
demographic data. We can create heatmaps on the most effective treatments and predict
with confidence what therapies are effective for regions of the world, and even extend
this to include gene therapies and genetic traits based on regions of the world and
ethnicity. Doctors can derive propensity analytics to predict outcomes and insurance
companies can even harvest this data to manage the cost of insurance.
As we see from these examples, big data applications create an explosion of
possibilities when the data is analyzed, modeled, and integrated with corporate data in
an integrated environment with traditional analytics and metrics.
The next segment of big data application is to understand the data that we will
leverage for the applications. The data platform is a myriad of types of data and it
includes the following: (Fig. 6.5)
This data platform is discovered in the data discovery exercise and all the operational
data can be consumed as needed by every user. In developing big data applications, the
process of data discovery can be defined as follows:
Data acquisitiondis the process of collecting the data for discovery. This includes
gathering the files and preparing the data for import using a tool. This can be
accomplished using popular data visualization tools like Datameer, Tableau, and R.

FIGURE 6.5 Data types used in big data applications.