Content
Print date: 11/3/2025
eTextbook: Principles of Information Systems - Ebooks
Summary
Principle:
We have entered an era where organizations are grappling with a tremendous growth in
the amount of data available and struggling how to manage and make use of it.
“Big data” is the term used to describe data collections that are so enormous and complex that
traditional data management software, hardware, and analysis processes are incapable of
dealing with them. Big data has five distinguishing characteristics: volume, velocity, value,
variety, and veracity.
Organizations use big data to improve their day-to-day operations, planning, and decision
making.
There are many challenges associated with big data, including how to choose what subset of
data to store, where and how to store the data, how to find those nuggets of data that are
relevant to the decision making at hand, how to derive value from the relevant data, and how
to identify which data needs to be protected from unauthorized access.
Principle:
A number of available tools and technologies allow organizations to take advantage of
the opportunities offered by big data.
Traditional online transaction processing (OLTP) systems put data into databases very quickly,
reliably, and efficiently, but they do not support the types of data analysis that today’s
businesses and organizations require. To address this need, organizations are building data
warehouses specifically designed to support management decision making.
A data warehouse is a large database that holds business information from many sources in
the enterprise, covering all aspects of the company’s processes, products, and customers.
An extract, transform, load process takes data from a variety of sources, edits and transforms
it into the format to be used in the data warehouse, and then loads the data into the
warehouse.
Data marts are subdivisions of data warehouses and are commonly devoted to specific
purposes or functional business areas.
A data lake takes a “store everything” approach to big data, saving all the data in its raw and
unaltered form.
A NoSQL database provides a means to store and retrieve data that is modelled using some
means other than the simple two-dimensional tabular relations used in relational databases.
Such a database has the capability to spread data over multiple servers so that each server
contains only a subset of the total data. The NoSQL does not require a predefined schema,
data entities can have attributes edited or assigned to them at any time. NoSQL databases do
not conform to true ACID properties when processing transactions.
There are categories of NoSQL databases—key-value, document, graph, and column.
Hadoop is an open-source software framework that includes several software modules that
provide a means for storing and processing extremely large data sets. Hadoop has two
primary components—a data processing component (MapReduce) and a distributed file
system (Hadoop Distributed File System or HDFS) for data storage. Hadoop divides data into
subsets and distributes the subsets onto different servers for processing. A Hadoop cluster
may consist of thousands of servers. A subset of the data within the HDFS and the MapReduce
system are housed on every server in the cluster.
An in-memory database (IMDB) is a database management system that stores the entire
database in random access memory to improve storage and retrieval speed.
There are many business intelligence (BI) and analytics techniques that can be used to
support improved decision making.
Business intelligence includes a wide range of applications, practices, and technologies for the
extraction, transformation, integration, visualization, analysis, interpretation, and
presentation of data to support improved decision making. It is used to tell what happened
and what is happening right now in the organization.
Analytics is the extensive use of data and quantitative analysis to support fact-based decision
making within the organization. It is often used to develop predictions of what will happen in
the future.
BI and analytics help achieve the following kinds of benefits: detect fraud, improve
forecasting, increase sales, optimize operations, and reduce costs.
A data scientist is an individual who combines strong business acumen, a deep understanding
of analytics, and a healthy appreciation of the limitations of data, tools, and techniques to
deliver real improvements in decision making.
BI/analytics techniques can be divided into five categories: descriptive analysis, predictive
analytics, optimization, simulation, and text and video analysis.
Descriptive analysis techniques include visual analytics and regression analysis used to
perform preliminary analysis to identify patterns in the data and answer questions about
who, what, where, when, and to what extent.
Predictive analytics techniques include time series analysis and data mining used to analyze
current data to identify future probabilities and trends, as well as make predictions about the
future.
Optimization techniques include genetic algorithms and linear programming used to allocate
scarce resources in such a manner as to minimize costs or maximize profits.
Simulation techniques include scenario analysis and Monte Carlo simulation used to emulate
the dynamic responses of a real-world system to various inputs.
Text and video analysis techniques include text analysis and video analysis used to glean
insights and data relevant to decision making.
Self-service analytics includes training, techniques, and processes that empower end users to
work independently to access data from approved sources to perform their own analyses
using an endorsed set of tools.
The advantages of self-service BI and analytics are that it gets valuable data into the hands of
end users, it encourages fact-based decision making based on analysis, it accelerates decision
making, and it provides a solution to the shortage of data scientists.
The disadvantages are that it raises the potential for erroneous analysis, can lead to analyses
with inconsistent conclusions, can cause over spending on unapproved data sources and
analytics tools, and may remove the necessary checks and balances on data preparation and
use.
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