Business analytics is a powerful tool in today’s
marketplace that can be used to make
decisions and craft business strategies. Across
industries, organizations generate vast amounts
of data which, in turn, has heightened the need
for professionals who are data literate and
know how to interpret and analyze that
According to a study by MicroStrategy,
companies worldwide are using data to:
•Improve efficiency and productivity (64
•Achieve more effective decision-making (56
•Drive better financial performance (51
The research also shows that 65 percent of global enterprises plan to
increase analytics spending.
In light of these market trends, gaining an in-depth understanding of
business analytics can be a way to advance your career and make
better decisions in the workplace.
“Using data analytics is a very effective way to have influence in an
organization,” said Harvard Business School Professor Jan
Hammond, who teaches the online course Business Analytics, in
a previous interview. “If you’re able to go into a meeting and other
people have opinions, but you have data to support your arguments
and your recommendations, you’re going to be influential.”
Before diving into the benefits of data analysis, it’s important to
understand what the term “business analytics” means.
Business analytics is the process of using quantitative methods to derive
meaning from data to make informed business decisions.
Business Analytics may be defined as refining past or present business
data using modern technologies. They are used to build sophisticated
models for driving future growth. A general Business Analytics process may
include Data Collection, Data Mining, Sequence Identification, Text Mining,
Forecasting, Predictive Analytics, Optimization, and Data Visualization.
Every business today produces a considerable amount of data in a specific
way. Business Analytics now are leveraging the benefits of statistical
methods and technologies to analyze their past data. This is used to
uncover new insights to help them make a strategic decision for the future.
Business Intelligence, a subset of the Business Analytics field, plays
an essential role in utilizing various tools and techniques such
as machine learning and artificial intelligence technologies to predict
and implement insights into daily operations.
Thus, Business Analytics brings together fields of business
management, and computing to get actionable insights. These
values and inputs are then used to remodel business procedures to
generate more efficiency and build a productive system.
After going through What is Business Analytics, let us understand
more about its evolution.
Evolution of Business Analytics
Technologies have been used as a measure to improve business
efficiency since the beginning. Automation has played a considerable
role in managing and performing multiple tasks for large
organizations. The unprecedented rise of the internet and information
technology has further boosted the performance of businesses.
With advancement today, we have Business Analytics tools that
utilize past and present data to give businesses the right direction for
As we now have a stronghold on What is Business Analytics, let us
next look into the types of business analytics techniques.
Types of Business Analytics Techniques
Business analytics techniques can be segmented in the following
1.Descriptive Analytics: This technique describes the past or present
situation of the organization's activities.
2.Diagnostic Analytics: This technique discovers factors or reasons
for past or current performance.
3.Predictive Analytics: This technique predicts figures and results
using a combination of business analytics tools.
4.Prescriptive Analytics: This technique recommends specific
solutions for businesses to drive their growth forward.
A complete business analytics life cycle
starts from raw data received from the
devices or services, then collecting data in
an unstructured type, then processing and
analyzing data to draw actionable insights.
These are then integrated into business
procedures to deliver better outcomes for
Business Analytics Applications
Business Analytics is now systematically
integrated across several applications in
the field of supply chain management,
customer relationship management,
financial management, human resources,
manufacturing, and even build smart
strategies for sports too.
Importance of Business Analytics
•Business analytics can transform raw data into more
valuable inputs to leverage this information in decision
•With Business Analytics tools, we can have a more
profound understanding of primary and secondary data
emerging from their activities. This helps businesses refine
their procedures further and be more productive.
•To stay competitive, companies need to be ahead of their
peers and have all the latest toolsets to assist their decision
making in improving efficiency as well as generating more
The Scope of Business Analytics
Business Analytics has been applied to a wide variety of
applications. Descriptive analytics is thoroughly used by businesses
to understand the market position in the current scenarios.
Meanwhile, predictive and prescriptive analytics are used to find
more reliable measures for businesses to propel their growth in a
In the last decade, business analytics is among the leading career
choices for professionals with high earning potential and assisting
businesses to drive growth with actionable inputs.
We have understood well about what Business Analytics is, let us
next understand its benefits.
The Benefits of Business Analytics
To club in one phrase: Business Analytics brings actionable insights
for businesses. However, here are the main benefits of Business
1.Improve operational efficiency through their daily activities.
2.Assist businesses to understand their customers more precisely.
3.Business uses data visualization to offer projections for future
4.These insights help in decision making and planning for the future.
5.Business analytics measures performance and drives growth.
6.Discover hidden trends, generate leads, and scale business in the
Difference Between Business Intelligence and Business
Business Intelligence(BI) uses the past and present to
identify trends and patterns in the organizational
procedures, while Business Analytics determines the
reasons and factors that led to present situations. Business
Intelligence focuses mainly on descriptive analysis, while
Business Analytics deals with predictive analysis. BI tools
are part of Business Analytics that helps understand the
Business Analytics process better.
A Career in Business Analytics
The role of Business Analytics professionals may change accordingly to meet
organizational goals and objectives. Several individual profiles are closely
associated with business analytics when dealing with data.
In this competitive age, business analytics has revolutionized the procedures to
discover intelligent insights and gain more profits using their existing methods
only. Business Analytics Techniques also help organizations personalize
customers with more optimized services and even include their feedback to
create more profitable products. Large organizations today are now competing to
stay top in the markets by utilizing practical business analytics tools.
Several business analytics tools are available in the market that offers specific
solutions to match requirements. Professionals might need business analytics
skills, like understanding and expertise of statistics or SQL to manage them.
Big data’s potential just keeps
growing. Taking full advantage
means companies must incorporate
analytics into their strategic vision
and use it to make better, faster
Is big data all hype? To the contrary: earlier research
may have given only a partial view of the ultimate
impact. A new report from the McKinsey Global Institute
(MGI), The age of analytics: Competing in a data-driven
world, suggests that the range of applications and
opportunities has grown and will continue to expand.
Given rapid technological advances, the question for
companies now is how to integrate new capabilities into
their operations and strategies—and position
themselves in a world where analytics can upend entire
A 2011 MGI report highlighted the transformational
potential of big data. Five years later, we remain
convinced that this potential has not been oversold. In
fact, the convergence of several technology trends is
accelerating progress. The volume of data continues to
double every three years as information pours in from
digital platforms, wireless sensors, virtual-reality
applications, and billions of mobile phones. Data-storage
capacity has increased, while its cost has plummeted.
Data scientists now have unprecedented computing
power at their disposal, and they are devising algorithms
that are ever more sophisticated.
Earlier, we estimated the potential for big data and
analytics to create value in five specific domains.
Revisiting them today shows uneven progress and a great
deal of that value still on the table (exhibit). The greatest
advances have occurred in location-based services and in
US retail, both areas with competitors that are digital
natives. In contrast, manufacturing, the EU public sector,
and healthcare have captured less than 30 percent of the
potential value we highlighted five years ago. And new
opportunities have arisen since 2011, further widening the
gap between the leaders and laggards.
Leading companies are using their capabilities not only
to improve their core operations but also to launch
entirely new business models. The network effects of
digital platforms are creating a winner-take-most
situation in some markets. The leading firms have
remarkably deep analytical talent taking on various
problems—and they are actively looking for ways to
enter other industries. These companies can take
advantage of their scale and data insights to add new
business lines, and those expansions are
increasingly blurring traditional sector boundaries.
Where digital natives were built for analytics, legacy
companies have to do the hard work of overhauling
or changing existing systems. Adapting to an era of
data-driven decision making is not always a simple
proposition. Some companies have invested heavily
in technology but have not yet changed their
organizations so they can make the most of these
investments. Many are struggling to develop the
talent, business processes, and organizational
muscle to capture real value from analytics.
The first challenge is incorporating data and analytics
into a core strategic vision. The next step is developing
the right business processes and building capabilities,
including both data infrastructure and talent. It is not
enough simply to layer powerful technology systems on
top of existing business operations. All these aspects of
transformation need to come together to realize the full
potential of data and analytics. The challenges
incumbents face in pulling this off are precisely why
much of the value we highlighted in 2011 is still
The urgency for incumbents is growing, since leaders are staking out
large advantages, and hesitating increases the risk of being
disrupted. Disruption is already happening, and it takes multiple
forms. Introducing new types of data sets (“orthogonal data”) can
confer a competitive advantage, for instance, while massive
integration capabilities can break through organizational silos,
enabling new insights and models. Hyperscale digital platforms can
match buyers and sellers in real time, transforming inefficient
markets. Granular data can be used to personalize products and
services—including, most intriguingly, healthcare. New analytical
techniques can fuel discovery and innovation. Above all, businesses
no longer have to go on gut instinct; they can use data and analytics
to make faster decisions and more accurate forecasts supported by
a mountain of evidence.
The next generation of tools could unleash
even bigger changes. New machine-
learning and deep-learning capabilities
have an enormous variety of applications
that stretch into many sectors of the
economy. Systems enabled by machine
learning can provide customer service,
manage logistics, analyze medical records,
or even write news stories.
These technologies could generate productivity
gains and an improved quality of life, but they
carry the risk of causing job losses and
dislocations. Previous MGI research found
that 45 percent of work activities could be
automated using current technologies; some
80 percent of that is attributable to existing
machine-learning capabilities. Breakthroughs in
natural-language processing could expand that
Data and analytics are already shaking up
multiple industries, and the effects will only
become more pronounced as adoption reaches
critical mass—and as machines gain
unprecedented capabilities to solve problems
and understand language. Organizations that
can harness these capabilities effectively will
be able to create significant value and
differentiate themselves, while others will find
themselves increasingly at a disadvantage.