Databases Reference
In-Depth Information
statistical insights. These insights provide visible patterns that can be useful in improving quality of
care for a given set of diseases.
Data warehousing evolved to support the decision-making process of being able to collect, store,
and manage data, applying traditional and statistical methods of measurement to create a reporting
and analysis platform. The data collected within a data warehouse was highly structured in nature,
with minimal flexibility to change with the needs of data evolution. The underlying premise for this
comes from the transactional databases that were the sources of data for a data warehouse. This con-
cept applies very well when we talk of transactional models based on activity generated by consum-
ers in retail, financial, or other industries. For example, movie ticket sales is a simple transaction,
and the success of a movie is based on revenues it can generate in the opening and following weeks,
and in a later stage followed by sales from audio (vinyl to cassette tapes, CDs', and various digital
formats), video ('DVDs and other digital formats), and merchandise across multiple channels. When
reporting sales revenue, population demographics, sentiments, reviews, and feedback were not often
reported or at least were not considered as a visible part of decision making in a traditional computing
environment. The reasons for this included rigidity of traditional computing architectures and associ-
ated models to integrate unstructured, semi-structured, or other forms of data, while these artifacts
were used in analysis and internal organizational reporting for revenue activities from a movie.
Looking at these examples in medicine and entertainment business management, we realize that
decision support has always been an aid to the decision-making process and not the end state itself, as
is often confused.
If one were to consider all the data, the associated processes, and the metrics used in any decision-
making situation within any organization, we realize that we have used information (volumes of data) in a
variety of formats and varying degrees of complexity and derived decisions with the data in nontraditional
software processes. Before we get to Big Data, let us look at a few important events in computing history.
In the late 1980s, we were introduced to the concept of decision support and data warehousing.
This wave of being able to create trends, perform historical analysis, and provide predictive analytics
and highly scalable metrics created a series of solutions, companies, and an industry in itself.
In 1995, with the clearance to create a commercial Internet, we saw the advent of the “dot-com”
world and got the first taste of being able to communicate peer to peer in a consumer world. With the
advent of this capability, we also saw a significant increase in the volume and variety of data.
In the following five to seven years, we saw a number of advancements driven by web commerce
or e-commerce, which rapidly changed the business landscape for an organization. New models
emerged and became rapidly adopted standards, including the business-to-consumer direct buying/
selling (website), consumer-to-consumer marketplace trading (eBay and Amazon), and business-to-
business-to-consumer selling (Amazon). This entire flurry of activity drove up data volumes more than
ever before. Along with the volume, we began to see the emergence of additional data, such as con-
sumer review, feedback on experience, peer surveys, and the emergence of word-of-mouth marketing.
This newer and additional data brings in subtle layers of complexity in data processing and integration.
Along the way between 1997 and 2002, we saw the definition and redefinition of mobility solu-
tions. Cellular phones became ubiquitous and the use of voice and text to share sentiments, opinions,
and trends among people became a vibrant trend. This increased the ability to communicate and cre-
ate a crowd-based affinity to products and services, which has significantly driven the last decade of
technology innovation, leading to even more disruptions in business landscape and data management
in terms of data volumes, velocity, variety, complexity, and usage.
