Big Data Analytics

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Big Data Analytics

by Geoff Keston

Docid: 00021046

Publication Date: 2209

Report Type: TUTORIAL


Big Data analytics provides enterprises with a range of new insights into
how to operate their businesses. While taking full advantage of this
emerging practice is difficult, many organizations are using it
extensively, pressuring competitors to keep pace. Understanding the
technology available and how it is being used can help organizations use
Big Data analytics to meet their own business goals.

Report Contents:

Executive Summary

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Over the past few years, the scale, speed, and power of analytics have
been dramatically transformed.

Faulkner Reports
Big Data Marketplace
Big Data Technology

The amount of data available from the Internet, combined with advances in
software to make use of it, has created a practice called “Big Data
analytics.” It can provide types of information that were not available in
the recent past, and it has the potential to do so in real-time.

The advent of Big Data analytics has created new challenges for
executives. There are new types of data to be understood and incorporated
into strategic planning. Making Big Data analytics work is not simply a
technical task but also an executive strategy-setting one. Some core
business practices in certain industries could be transformed in the
coming years.

The use of Big Data analytics is still maturing, but it is already
common. As revealed by Fortune Business Insights, prominent market
participants include:

  • IBM (US)
  • SAP SE (Germany)
  • Microsoft (US)
  • SAS Institute (US)
  • FICO (US)
  • Oracle (US)
  • Salesforce (US)
  • Equifax (US)
  • TransUnion (US)
  • QlikTech International AB (US)1

Importantly, the many elements of the Big Data analytics process – from
gathering data to spotting patterns to translating raw findings into
actionable information – are rarely provided by a single solution.
Instead, enterprises must build their own systems, using an understanding
of their business goals as a guiding factor.


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Using software to analyze data is an old practice. Analytics have been
employed for purposes as diverse as predicting the weather to determining
what line of business a company should enter. Starting a few years
ago, the practice began undergoing what has been called a revolution. The
use of the Internet has greatly expanded the volume and breadth of data
available, and many diverse tools to crunch the data have been created.
The difference is not simply that analytics have become better, but that
they are fundamentally different. This new discipline is Big Data
analytics. Describing this change as it began to fully emerge, a 2012 Harvard
Business Review
assessment of the development offered the following

“Booksellers in physical stores could always
track which books sold and which did not. If they had a loyalty program,
they could tie some of those purchases to individual customers. And that
was about it. Once shopping moved online, though, the understanding of
customers increased dramatically. Online retailers could track not only
what customers bought, but also:

    • What else they looked at;
    • How they navigated through the site;
    • How much they were influenced by promotions, reviews, and page
      layouts; and
    • Similarities across individuals and groups.

Before long, they developed algorithms to
predict what books individual customers would like to read next –
algorithms that performed better every time the customer responded to or
ignored a recommendation.”2

Describing the key difference between Big Data analytics and traditional
versions of the practice, a report by the SAS Institute says that “The
primary purpose behind traditional, ‘small data’ analytics was to support
internal business decisions. What offers should be presented to a
customer? Which customers are most likely to stop being customers soon?
How much inventory should be held in the warehouse? How should we price
our products?”3 But, as the report points out, traditional
analytics requires structured data, although much of the information being
produced online today is unstructured. Today’s Big Data technology,
however, can make full use of both types of information. The ability to
make use of unstructured data is particularly important for the vast
amounts of “multi-channel” information now being produced – enterprises
today interact with customers in many more ways than in the past,
including Web site visits and Internet chats as well as legacy methods
like phone calls. And analytics can often be performed in real time, or a
“stream.” Real-time analytics continues to mature, but implementing it
remains somewhat difficult.4

Types of Big Data Analytics

As catalogued by analyst Catherine Cote, there are four basic types of
Big Data analytics:

Descriptive analytics, which
answers the question, “What happened?”

Diagnostic analytics, which
answers the question, “Why did this happen?”

Prescriptive analytics, which
answers the question, “What should we do next?”

Predictive analytics, which
answers the question, “What might happen in the future?”5

Of these, predictive analytics is perhaps the most intriguing. While
knowing “what happened” in the past is important, knowing “what might
happen” in the future can be even more valuable, enabling enterprise
workers to:

  • Avoid potential problems
  • Create new opportunities
  • Improve critical decision-making
  • Achieve competitive advantages

How Big Data Analytics Works

According to Simplilearn, a leading certification training provider, the
Big Data analytics process is divided into eight stages:

“Stage 1 – Business case evaluation
– The Big Data analytics lifecycle begins with a business case, which
defines the reason and goal behind the analysis.

“Stage 2 – Identification of data
– Here, a broad variety of data sources are identified.

“Stage 3 – Data filtering
All of the identified data from the previous stage is filtered here to
remove corrupt data.

“Stage 4 – Data extraction
Data that is not compatible with the tool is extracted and then
transformed into a compatible form.

“Stage 5 – Data aggregation
– In this stage, data with the same fields across different datasets are

“Stage 6 – Data analysis
Data is evaluated using analytical and statistical tools to discover
useful information.

“Stage 7 – Visualization of data
– With tools like Tableau, Power BI, and QlikView, Big Data analysts can
produce graphic visualizations of the analysis.

“Stage 8 – Final analysis result
– This is the last step of the Big Data analytics lifecycle, where the
final results of the analysis are made available to business stakeholders
who will take action.”6

Current View

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The “Big” in Big Data analytics not only reflects the volume and variety
of data being analyzed, but the complexity of analytic operations.

Figure 1 illustrates a highly-stylized Big Data analytics workflow, as
various data types from various data sources are analyzed by various
analytic engines to produce new or enhanced enterprise applications.

Figure 1. Sample Big Data Analytics Workflow

Figure 1. Sample Big Data Analytics Workflow

Source: Wikimedia Commons

Big Data Analytics Market

Fortune Business Insights reports that the global Big Data analytics
market was valued at $240.56 billion in 2021. The firm predicts that the
market will grow from $271.83 billion in 2022 to $655.53 billion by 2029,
a compound annual growth rate (CAGR) of 13.4 percent during the forecast

Prominent market participants include:

  • IBM (US)
  • SAP SE (Germany)
  • Microsoft (US)
  • SAS Institute (US)
  • FICO (US)
  • Oracle (US)
  • Salesforce (US)
  • Equifax (US)
  • TransUnion (US)
  • QlikTech International AB (US)8

The continuing – and, in some sectors, accelerating – adoption of BIg
Data analytics can be generally attributed to several conditions: 

The Rise of the Internet of Things,
in which the world’s devices – not just data – are being rendered
addressable and programmable. “According to …  International Data
Corporation (IDC) data, 152,200 IoT devices are expected to connect per
minute by 2025.”9

The Digital Transformation Movement,
which seeks to reinvent or revitalize enterprise operations through the
application of digital (i.e., data-based) technologies.

The Explosive Growth of Enterprise
, especially in the following sectors:

    • Banking, Financial Services and Insurance (BFSI)
    • Automotive
    • Telecom/Media
    • Healthcare
    • Life Sciences
    • Retail
    • Energy & Utility
    • Government10

Advancements in Data Storage
, enabling the creation of bigger-than-ever data
warehouses and other mass data stores.

Advancements in Data Communications,
particularly 5G, which permits more data (even the same data) to be stored
in more places, thus complicating data wrangling operations.

The Emergence of Machine Learning,
which requires mountains of data to train artificial intelligence

The Popularity of Social Media,
whose users contribute massive amounts of audio and video content to
Facebook, YouTube, and other SM platforms.


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Changes to Some Core Business Practices

The advent of Big Data analytics has the potential to dramatically
change core practices in many fields. One primary example is in a “smart
city,” which is the urban use of advanced technologies to perform a
variety of municipal services.

Describing how Big Data analytics are used in a smart city program in
Oulu, Finland, Susanna Pirttikangas discussed its applications for traffic
management: “In order to avoid traffic congestion, decrease emissions, and
increase safety on the roads, you need information about traffic speeds,
construction, weather, and even available parking spaces. You also need
information about other drivers, bus locations, vehicles, vehicles behind
corners, and so on.”11 The amount of data is critical. “With
enough data, you can make reliable assumptions about the situation, and in
traffic, where situations emerge fast, you need the information to be
updated and delivered quickly.”

Applications in Key Sectors

In recent years, Big Data analytics has been put to wider, more critical
use in several key market segments. These uses are just beginning to fully
emerge, but they are showing the direction that the marketplace and
technology may take in the coming years. Examples of emerging applications

  • To leverage data shared among treatment centers to battle opioid use12
  • To investigate crimes and make tactical policing decisions13
  • To improve the delivery and profitability of healthcare services14
  • To analyze, even modify, the behavior of insurance customers15

Security and Privacy

By improving the utility of Big Data, Big Data analytics promotes the
capture and creation of even more Big Data, call it “Bigger Data.”

One of the obvious – and ominous – consequences of generating Bigger Data
is exposing the enterprise to bigger security breaches and bigger privacy
violations. As Fortune Business Insights concludes, “remote storage, weak
identity governance, low investment in … system and network security,
human error, connected devices, and IoT applications are some of the major
areas that need to be addressed.”16

Ultimately, security and privacy concerns may “hamper” the growth of Big
Data and Big Data analytics, especially as enterprises contemplate their
duty to observe standards like the:

  • European Union (EU) General Data Protection Regulation (GDPR)
  • California Consumer Privacy Act (CCPA)
  • US Health Insurance Portability and Accountability Act (HIPAA)


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According to a study of the goals that Fortune 1000 companies have for
using Big Data, the following were most important:

  • “Decrease expenses
  • “Find new innovation avenues
  • “Launch new products/services
  • “Add revenue
  • “Increase the speed of current efforts
  • “Transform business for the future
  • “Establish a data driven future”17

Executives are a crucial part of planning to implement Big Data analytics
in order to achieve these goals. Before key decisions about technology can
be made, enterprises must first set their analytics strategy. In doing so,
the following questions will be crucial:

  • What decisions need to be made?
  • What data helps us to make decisions?
  • In what form is data best provided to us?
  • Where, when, and to whom does data need to be delivered?

Using Big Data analytics effectively will take a change in mindsets and
even processes and job roles, however. Enterprises cannot simply approach
the new analytic capabilities in the same way that they have traditionally
used information. Studying how enterprises in a variety of industries are
using the technology can provide ideas for adapting to this new way of
approaching analytics.18

Finally, QlikTech International AB, a business intelligence software
developer, urges enterprise management to create and cultivate an
atmosphere conducive to Big Data operations. Among their specific

  • “Give your entire organization access to Big Data.
  • “Make it easy for users to find the data they need.
  • “Use an agile analytics environment that can meet the needs of every
  • “Provide access to analytics solutions anywhere, on any device.
  • “Implement a scalable solution that grows with your changing needs.”19


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About the Author

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Geoff Keston is the author of over 250 articles that
help organizations find opportunities in business trends and technology.
He also works directly with clients to develop communications strategies
that improve processes and customer relationships. Mr. Keston has worked
as a project manager for a major technology consulting and services
company and is a Microsoft Certified Systems Engineer and a Certified
Novell Administrator.

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