business analytics

1. WHAT IS BUSINESS ANALYTICS?
• Business analytics is the process of using quantitative methods to
derive meaning from data to make informed business decisions.
• There are four primary methods of business analysis:
• Descriptive: The interpretation of historical data to identify trends
and patterns
• Diagnostic: The interpretation of historical data to determine why
something has happened
• Predictive: The use of statistics to forecast future outcomes
• Prescriptive: The application of testing and other techniques to
determine which outcome will yield the best result in a given
scenario

2. THE BENEFITS OF BUSINESS ANALYTICS
• 1. More Informed Decision-Making
• Business analytics can be a valuable resource when approaching an
important strategic decision.
• 2. Greater Revenue
• Companies that embrace data and analytics initiatives can experience
significant financial returns.
• 3. Improved Operational Efficiency
• Beyond financial gains, analytics can be used to fine-tune business processes
and operations.

3. Skills Business Analysts Need
• To crunch numbers.
• to have critical thinking skills to interpret the results.
• Strong communication skills.
• An effective data analyst has both the technical and soft skills to ensure
an organization is making the best use of its data.

4. Scope of Business Analytics
• Automation
• Business analytics is becoming increasingly automated as organisations look to
simplify processes and reduce costs. Automated solutions can collect, organise
and analyse data quickly and accurately, giving businesses near real-time
insights into their operations. This can help organisations make faster decisions
with greater accuracy.
• Big Data Analytics
• As the amount of available data grows exponentially, businesses are turning to
big data solutions for improved performance. By leveraging the power of big
data tools such as Hadoop or Spark, companies can gain deeper insight into
customer behavior, market trends, and more to optimise their strategies for
maximum success.

5. Contd.:-
• Artificial Intelligence (AI)
• AI technologies such as machine learning algorithms are being used more
frequently in business analytics to detect patterns, make predictions and
optimise decisions. AI-powered solutions are becoming increasingly
sophisticated, making them invaluable for organisations seeking a competitive
edge in the market.
• Cloud Computing
• Cloud computing transforms how businesses handle data and analytics. By
moving their analytical processes to the cloud, companies can reduce costs
and improve scalability while accessing powerful tools for analysis and
leveraging near real-time insights into their operations.

6. contd.:-
• Internet of Things (IoT)
• The IoT revolution is creating vast amounts of corresponding data that can be
analysed for improved performance. Companies can use IoT data to gain
insights into customer behavior, optimise operations or develop new products
and services based on customer needs and requirements.
• Business analytics is an ever-evolving field that businesses increasingly turn to
for improved performance. By leveraging automation, big data analytics, AI,
cloud computing, and IoT, organisations can gain deeper insight into their
operations and make better decisions for long-term success.

7. The Job Prospects in Business Analytics
• Data Scientists
• Data scientists use algorithms and models to analyse data, identify trends, make
predictions, and provide actionable insights. As organisations look to gain a more
comprehensive understanding of their data, the demand for data scientists is
increasing significantly.
• The average salary in India for a data scientist is ₹11 lakh per annum, as per
AmbitionBox.
• Business Analyst
• Business analysts are responsible for developing strategies based on analytical
findings to maximise efficiency and profitability. With an ever-growing array of
tools available for analysis, businesses need experienced professionals who can
interpret the results and develop effective plans for success.
• According to AmbitionBox, the average salary in India for a business analyst in
India is ₹7 lakh per annum.

8. Contd.:-
• Machine Learning Engineer/Data Engineer
• As businesses increasingly rely on machine learning algorithms for improved
performance, the demand for engineers with expertise in this area is overgrowing.
Knowledge of coding languages such as Python and R is essential for this role.
• The average salary in India of a machine learning engineer/data engineer is ₹ 10 lakh
per annum, according to AmbitionBox.
• Business Intelligence Analyst
• Business intelligence analysts are responsible for collecting, organising, and analysing
data to gain insights into customer behavior, market trends, or other business-related
information. With powerful tools available for analysis, such as SQL and Tableau, the
demand for experienced BI analysts is growing rapidly.
• According to AmbitionBox, the average salary in India for business intelligence
analysts is ₹ 7 lakh per annum.

9. Contd.:-
• Visualisation Expert
• With the availability of tools such as PowerBI and Tableau, businesses need
professionals who can use these tools to create meaningful visualisations that
convey complex data in an easy-to-understand way. This requires knowledge
of coding languages such as HTML/CSS, JavaScript, or D3 library.
• The average salary of a visualisation expert in India is ₹14 lakh, as per
AmbitionBox.

10. Process of Business analytics
• Step 1: Address the Business Problems
• Initially, business problems need to be addressed, the purpose
of applying analytics is sometimes designated categorically or
broken into parts. So, relevant data is selected to address these
business problems by business users or business analysts
equipped with domain knowledge.
• Some examples are: keeping modeling for a postpaid
subscription, fraud detection for credit cards, or customer
analysis of a mortgage portfolio. Business experts define
perimeters for the analytical process which is crucial for
assuring general understanding of the goal.

11. Step 2: Identify Potential Interest from Data
• All sources of data having potential interest are required to identify. The key
asset in this step is the more the data, the better it is. All the data will then be
accumulated and consolidated in a data warehouse or data mart or at a
spreadsheet file. Some exploratory data analysis is executed to do the
computation for missing data, removing outliers, and transforming variables.
• For example, time-series analysis graphs are plotted to figure out some
patterns or outliers, scatter plots are used to find correlation or non-linearity,
OLAP system for multidimensional analysis.

12. Step 3: Inspect the data
• Once moving to the analytics step, an analytical model will be predicted on
the prepared and transformed data using statistical analysis techniques like
correlation analysis and hypothesis testing. The analyst figures out all
parameters in connection with the target variable. The business expert also
performs regression analysis to make simple predictions depending upon the
business objective. In this step, data is also often reduced, divided, crumbled
and compared with various groups to derive powerful insights from data.

13. Step 4: Interpretation and Evaluation by Experts
• Finally, after obtaining model results, business experts interpret and evaluate
them. Results may be clusters, rules, relations, or trends known as analytical
models derived from applying analytics. Experts use predictive techniques like
decision trees, neural networks, logistics regression to reveal the patterns and
insights that show the relationship and invisible indication of the most
persuasive variables.
• Several prediction models are executed to select the best performing model
on the basis of model accuracy and consequences. But yet, to explore
unknown though engaging and tribal patterns are challenging that can add
value to data and convert into new turnout opportunities.

14. Step 5: Optimization of Best Possible Solution
• Once the analytical model has been validated and approved, the analyst will
apply predictive model coefficients and conclusions to drive “what-if”
conditions, using the defined to optimize the best solution within the given
limitations and constraints.
• Necessary considerations are how to serve model output in a user-friendly
way, how to integrate it, how to confirm the monitoring of the analytical
model accurately. An optimal solution is chosen based on the lowest error,
management objectives, and identification of model coefficients that are
associated with the company’s goals.

15. Step 6: Decision Making and Estimate conclusions
• Analysts then would make decisions and endure action based on the
conclusions derived from the model in accordance with the predefined
business problems. Spam of period is accounted for the estimation of
conclusion, all the favorable and opponent consequences are measured in
this duration to satisfy the business needs.

16. Step 7: Upgrade performance system
• At last, the outcome of decision, action and the conclusion conducted
from the model are documented and updated into the database. This
helps in changing and upgrading the performance of the existing
system.
• Some queries are updated in the database such as “ were the
decision and action impactful?” “ what was the return or investment
?”,”how was the analysis group compared with the regulating class?”.
The performance-based database is continuously updated once the
new insight or knowledge is extracted.

17. Relationship of business analytics and organisation
•By analyzing consumer trends, we can provide unique
customer experiences.
•Significantly increasing service metrics performance.
•Providing insight into risk management and how to
improve overall management.
•Accounting Processes Are Simplified.
•Enhances the Supply Chain.

18. What is Competitive Advantage?
• Competitive advantage is what distinguishes a company's goods or services
from all other options available to a customer. It refers to the elements that
enable a company to manufacture goods or services more efficiently or at a
lower cost than its competitors.
• This may result in the company gaining a large market share. These elements
enable the producing unit to earn higher sales or higher margins than its
competitors.
• A range of elements contributes to competitive advantages, including cost
structure, branding, product quality, distribution network, intellectual
property, and customer service improving the company's customer base.

19. Contd.:-
• Business analytics drives competitive advantage by generating economies of
scale, economies of scope, and quality improvement. Taking advantage of
economies of scale is the first way organizations achieve comparative cost
efficiencies and drive competitive advantage against their peers.
• The efficiencies that accumulate when a firm embraces big data technology
eventually contribute to a ripple effect of increased production and reduced
business costs.
• Analytics gives companies insight into their customers’ behaviour and needs.
It also makes it possible for a company to understand its brand's public
opinion, follow the results of various marketing campaigns, and strategize
how to create a better marketing strategy.
• The retention of company employees has been a concern for business
enterprises although it is taken more seriously in some niches than it is in
other industries.