Business Analytics

Business analytics (BA) is a set of disciplines and technologies for solving

business problems using data analysis, statistical models and other
quantitative methods. It involves an iterative, methodical exploration of an
organization's data, with an emphasis on statistical analysis, to drive decision-
making.

Data-driven companies treat their data as a business asset and actively look
for ways to turn it into a competitive advantage. Success with business
analytics depends on data quality, skilled analysts who understand the
technologies and the business, and a commitment to using data to gain
insights that inform business decisions.

Types of business analytics

Different types of business analytics include the following:

 Descriptive analytics, Descriptive analytics typically offer a rearview

look into the past through the data of the present. They can tell a
user what happened in the business. Descriptive analytics is usually
performed in the preliminary stage of data processing to create a
summary of historical events and a foundation for further analysis and

 understanding. Data aggregation and data mining are the primary

methods used. Descriptive analytics is generally known as the
simplest type of analytics. In fact, most businesses perform
descriptive analytics in their everyday reporting such as inventory,
workflow, and sales.
 Predictive analytics, is focused on forecasting the likelihood of
potential outcomes and events in your business and is modeled on
historical data. Predictive analytics use techniques like statistical
modeling and machine learning and typically need data scientists and
statisticians to execute. Organizations can use patterns found in past
and current data to forecast trends, detect risks, and opportunities in
the near or far future. Predictive analytics offers a confidence level for
businesses to look into the future to save and even earn more
revenue. Examples of predictive analytics that are currently in use
include retailers that use predictive models to forecast inventory,
manage shipping and set up stores to maximize sales. Airlines often
use predictive analytics to set ticket prices based on past demand and
trends. Hotels can use predictive analytics to determine capacity to
ensure they are prepared for any sudden surges in bookings.

 Pescriptive analytics, Prescriptive analytics is the most mature stage

of the analytics journey in business analytics. It tells the business
what they should do and recommend next best actions or actions they
should be taking given a variety of choices. This type of analytics can
tell you the outcomes of each choice that you made and recommend
 the best action you should take based on all of the possibilities.
Prescriptive analytics is related to descriptive and predictive analytics
but focuses on actionable insights rather than just data monitoring.
To perform prescriptive analytics, you need deep learning and
complex neural networks. A great example of prescriptive analytics
are recommendation engines.

The 7-step Business Analytics Process

Step 1. Defining the business needs

The first stage in the business analytics process involves understanding what the
business would like to improve on or the problem it wants solved. Sometimes, the goal
is broken down into smaller goals. Relevant data needed to solve these business goals
are decided upon by the business stakeholders, business users with the domain
knowledge and the business analyst. At this stage, key questions such as, “what data is
available”, “how can we use it”, “do we have sufficient data” must be answered.

Step 2. Explore the data

This stage involves cleaning the data, making computations for missing data, removing
outliers, and transforming combinations of variables to form new variables. Time series
graphs are plotted as they are able to indicate any patterns or outliers. The removal of
outliers from the dataset is a very important task as outliers often affect the accuracy of
the model if they are allowed to remain in the data set. As the saying goes: Garbage in,
garbage out (GIGO)!

Once the data has been cleaned, the analyst will try to make better sense of the data.
The analyst will plot the data using scatter plots (to identify possible correlation or non-
linearity). He will visually check all possible slices of data and summarise the data using
appropriate visualisation and descriptive statistics (such as mean, standard deviation,
range, mode, median) that will help provide a basic understanding of the data. At this
stage, the analyst is already looking for general patterns and actionable insights that
can be derived to achieve the business goal.

Step 3. Analyse the data

At this stage, using statistical analysis methods such as correlation analysis and
hypothesis testing, the analyst will find all factors that are related to the target variable.
The analyst will also perform simple regression analysis to see whether simple
predictions can be made. In addition, different groups are compared using different
assumptions and these are tested using hypothesis testing. Often, it is at this stage that
the data is cut, sliced and diced and different comparisons are made while trying to
derive actionable insights from the data.

Step 4. Predict what is likely to happen

Business analytics is about being proactive in decision making. At this stage, the
analyst will model the data using predictive techniques that include decision trees,
neural networks and logistic regression. These techniques will uncover insights and
patterns that highlight relationships and ‘hidden evidences’ of the most influential
variables. The analyst will then compare the predictive values with the actual values and
compute the predictive errors. Usually, several predictive models are ran and the best
performing model selected based on model accuracy and outcomes.

Step 5. Optimise (find the best solution)

At this stage the analyst will apply the predictive model coefficients and outcomes to run
‘what-if’ scenarios, using targets set by managers to determine the best solution, with
the given constraints and limitations. The analyst will select the optimal solution and
model based on the lowest error, management targets and his intuitive recognition of
the model coefficients that are most aligned to the organisation’s strategic goal.

Step 6. Make a decision and measure the outcome

The analyst will then make decisions and take action based on the derived insights from
the model and the organisational goals. An appropriate period of time after this action
has been taken, the outcome of the action is then measured.
Step  7. Update the system with the results of the decision

Finally the results of the decision and action and the new insights derived from the
model are recorded and updated into the database. Information such as, ‘was the
decision and action effective?’, ‘how did the treatment group compare with the control
group?’ and ‘what was the return on investment?’ are uploaded into the database. The
result is an evolving database that is continuously updated as soon as new insights and
knowledge are derived.

Applications of Business Analytics with Examples

Finance

The finance industry has a long history with business analytics. Everything from the stock
market to the risk analysis of large companies uses business analytics. A great example is
the investment bank, Barclays. Per reports, utilize business analytics to:

 Work through complex issues to find a pragmatic route that helps the firm reach its
goals.
 Interpreting, scoping, and analyzing data at each step of a business approach before
practical execution.
 Classify various cases from legal to compliance.
 Support the designing and management phase of plans and resources that deliver
various portfolio goals.
 Identifying and mitigating assumptions, dependencies, and risks.

As with Barclays, most other financial firms also use business analytics in various ways that
help support their business growth. This makes business analysis a high-in-demand role in
financial firms across the globe.
Marketing

A successful marketing strategy simply cannot work without proper market research
conducted before it. Today’s market data isn’t as simple as it used to be since most
consumers and businesses have had to make the switch to digital.

E-commerce firms such as Amazon have always had to rely on digital data for their market
research and are therefore heavily invested in business analytics for their marketing
department. Here’s how Amazon uses business analytics for marketing:

 Analyzing the purchase patterns of customers, their wish lists, searches, and even
social media interactions to generate a customer profile.
 Using the customer profile to make highly optimized recommendations that are
designed to trigger impulsive purchases.
 Presenting users with related items to their cart before checkout to encourage more
purchases in each order.
 Targeted marketing on various websites and social media platforms.

Other marketing firms also use similar methods to help them expand profits and grow their
business. Therefore, business analytics plays an important role in the marketing sector from
start to finish of their strategy.

HR

While HR is meant to be the most human of all business divisions, it can also benefit from
marketing. For instance, Google has been using business analytics to completely
revolutionize the HR industry. Here’s what they are doing:

 Project Oxygen analyzes internal team data and determines the characteristics of
great managers, the best ways to boost retention and productivity, and optimize
leadership.
 Google’s PiLab also works on effective approaches for boosting employee
satisfaction. Moreover, they work on analyzing various aspects of the office
environment including employee health and caloric intake.
 Google also utilizes predictive modeling and analytics to help optimize its people
management problems and opportunities. This allows them to effective workforce
planning in a rapidly evolving business environment.

Google is certainly leading the way in business analytics-powered HR systems. So far, this
has helped them improve workplace satisfaction and boost employee retention as well. If
you specialize in business analytics and HR, this field will have immense opportunities for
you in the future.
Know more about Proschool’s Business Analytics Course

CRM

Customer relationship management is another field of concern for modern businesses. To

facilitate this, firms such as Apple utilize advanced CRM analytics to benefit their growth
strategy. Here’s what Apple does right:

 Using analytics to know their customers.

 Anticipating various customer needs via predictive modeling.
 Optimize the customer experience in their stores.
 Fine-tune their branding to match their target audience.

Apple’s use of CRM analytics has influenced firms across the world to hire analysts for
optimizing their CRM strategy as well.

Manufacturing

The manufacturing industry is another surprising benefactor of business analytics. Here’s

how Tata Steel utilizes business analytics to help its business grow:

 Tracking the manufacturing process and optimizing steps.

 Optimize logistics.
 Mitigating risks and eliminating delays.

Seeing the success they have had with business analytics, Tata Steel has also started
hiring more business analytics professionals for their manufacturing process.

Credit

Credit firms have long depended on business analytics to help them make better decisions
on giving out loans. For instance, let’s go over how HDFC Bank uses credit analysis in their
everyday business:

 Analyzing customer credit history.

 Using predictive modeling to judge default probability.
 Analyze purchase habits to detect fraudulent transactions.

This is not just for HDFC Bank, but rather all credit firms have similar usage of business
analytics.
Importance of Business Analytics

Some of the below points will give in-depth knowledge about the importance of business
analytics in business:-

1) Helps the Organization to track its costs and to find Effective Solutions
The company also struggles due to a lack of policy or preparation for financial management.
Development and execution of financial and management processes are important to achieve
market success. 

2) Get the best out of your Investments

Internet marketing has now become the most effective and cost-effective way for organizations
to identify answers. Marketers can create ideal campaigns and plans by leveraging market
intelligence, ultimately improve the likelihood of higher ROIs.

3) Enhanced Publicity
Publicity is expensive; advertisers also need to know how to get the highest investment return.
Therefore, computational approaches like A / B and C split-testing are employed. Both landing
pages, pop-ups, and even descriptions of the items are tested and tweaked for maximal
performance when it comes to online ads. Even to detect the best spot for better interaction and
purchases, the placement of goods is measured on the website.

4) Better Handling of Goods

They have over a thousand items to sell to distribution firms for which data interpretation is the
solution. These companies evaluate which are according to the area and the season the most
common items. These data are then used to advertise at the right time the right product, which
has a positive effect on revenue.
5) Fix Problems
In case a crisis happens, a corporation sometimes ceases existing operations, leading to an
immense loss. Here comes the Importance of Business analytics in decision making, which helps
the company to take an educated decision to eliminate those scenarios by presenting information
to detect possible threats and mitigate failure. The raw data will be used by these experts to
recognize a malfunction in the current system and enable the company owners to repair it as
quickly as possible.

6) Carry out an Analysis of Competitor

Today nearly every corporation understands its rivals clearly. A successful way to move them
forward is by understanding their tactics, USP’s, and so on. Through collecting these results, you
can preview how your company works relative to your rivals by performing a SWOT study.