Enterprise analyzed or to be transferred more efficiently

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Enterprise analyzed or to be transferred more efficiently

Enterprise data warehouses (EDW) have been around for 3
decades now, but still number of companies which implemented are lesser than it
seems to be, further there are even fewer companies which implemented it
correctly in terms of timing and architecture that suits business needs. It is
well established by now that data warehouses offer advantages which have the
potential of cost reduction by more than $5 million annually, which is an
estimated data-related problems cost in majority of companies. The concept
under the hood of data warehouses goes hand in hand with the concept and
strategy of business. Thus, it is of utmost importance that an early establishment
of data warehouse solution, either customized in-house built or more general
market purchased, be consented and agreed upon by top management. According to
a survey by Salesforce, about 37% of businesses publicized that data analytics
processes, which work cohesively with data warehousing capabilities,
facilitated business growth. The
functions of a data warehouse are primarily to bring data from multiple sources
together under one roof either to be analyzed or to be transferred more
efficiently between systems.

The task of building a data warehouse is complex,
time-consuming, and expensive, thus requires justification and assessment based
on measures like return on investment (ROI), net present value, internal rate
of return, and payback period. There have been many case studies and surveys
based on data warehouse development projects. Some companies plainly justified
the requirement of data warehouse to survive in rigorous competition, while
some companies got involved in deep assessment of ROI of such projects, then
there are some companies which realized the more than expected value brought by
data warehouses that there was no post-implementation assessment done. Most of
such projects were deployed in phases, e.g. creating datamarts for marketing
department; this method has its benefit of convincing top management for
upscaling data warehouse infrastructure to the enterprise-wide level.

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Various industries like retail, finance, healthcare,
distribution, manufacturing, publishing, and even casino businesses have
realized the obvious values which data warehouses bring and completely changed
the way business is done. Data warehouses provided the big picture perspective
allowing firms to make short- and long-term strategies and business problem
solutions to capture more market segment ahead of competition by implementing
customer relationship management strategy. Significantly reducing customer
attrition rate is the primary goal of corporations to be in business, and that
is the penultimate benefit for which corporations changed the way of doing
business with the help of data warehouse architecture. Product and business
processes improvements are also considered to be widely accepted benefits which
modified the business strategies of several companies.

Although the growth of data warehousing market has been
amplified and realized by almost every industry sector, there are few big
companies which helped evolve data warehouse technology with their own growth
while deriving value from this technology. To name a few companies: Google,
Teradata, Twitter, iPhone, Microsoft, Oracle, IBM, Amazon, Facebook, HireRight
etc.

The Gartner 2017 Magic Quadrant for Data
Warehouse and

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