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In the 1980s, traditional Business Intelligence (BI) systems
focused on the delivery of reports that describe the state of
business activities in the past, such as for questions like "How
did our sales perform during the last quarter?" A decade later,
there was a shift to more interactive content that presented how
the business was performing at the present time, answering
questions like "How are we doing right now?" Today the focus of BI
users are looking into the future. "Given what I did before and how
I am currently doing this quarter, how will I do next quarter?"
Furthermore, fuelled by the demands of Big Data, BI systems are
going through a time of incredible change. Predictive analytics,
high volume data, unstructured data, social data, mobile,
consumable analytics, and data visualization are all examples of
demands and capabilities that have become critical within just the
past few years, and are growing at an unprecedented pace. This book
introduces research problems and solutions on various aspects
central to next-generation BI systems. It begins with a chapter on
an industry perspective on how BI has evolved, and discusses how
game-changing trends have drastically reshaped the landscape of BI.
One of the game changers is the shift toward the consumerization of
BI tools. As a result, for BI tools to be successfully used by
business users (rather than IT departments), the tools need a
business model, rather than a data model. One chapter of the book
surveys four different types of business modeling. However, even
with the existence of a business model for users to express
queries, the data that can meet the needs are still captured within
a data model. The next chapter on vivification addresses the
problem of closing the gap, which is often significant, between the
business and the data models. Moreover, Big Data forces BI systems
to integrate and consolidate multiple, and often wildly different,
data sources. One chapter gives an overview of several integration
architectures for dealing with the challenges that need to be
overcome. While the book so far focuses on the usual structured
relational data, the remaining chapters turn to unstructured data,
an ever-increasing and important component of Big Data. One chapter
on information extraction describes methods for dealing with the
extraction of relations from free text and the web. Finally, BI
users need tools to visualize and interpret new and complex types
of information in a way that is compelling, intuitive, but
accurate. The last chapter gives an overview of information
visualization for decision support and text.
Business intelligence and analytics software enable businesses to
analyze performance data in order to make better decisions through
the use of cloud computing--an Internet-based model for convenient,
on-demand network access to a shared pool of configurable computing
resources. This book is a practitioner's guide for successful
evaluation and design for implementation of Cognos Business
Intelligence cloud solution, for either Cognos 8 BI or Cognos
Business Intelligence Version 10. With pragmatic and practical
information about the best practices and guidelines, as well as
specific software and configuration steps, this guide for solutions
and IT architects includes detailed screen shots, code samples, and
input instructions.
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