Business intelligence (BI) focuses on analyzing past business data to provide insights for decision-making, while data science aims to predict future trends through analyzing patterns in data using machine learning and other advanced analytics techniques. BI uses structured data to identify strengths and weaknesses, while data science can leverage both structured and unstructured data to develop forecasts. Data science requires stronger technical skills than BI and can manage more dynamic data sources.

1. TOPIC:
BUSINESS INTELLIGENCE VS DATA SCIENCE

2. INTRODUCTION:-
Business intelligence (BI) and data science are both data-
focused processes, but there are some key differences between
the two.
In general, business intelligence focuses on analyzing past
events, while data science aims to predict future trends.
Data science requires a more technical skill set compared to
business intelligence.

3. What is Business Intelligence?
Business intelligence is based on the concept of using data to
drive actions. It aims to provide business leaders with
actionable insights through data processing and analysis. For
example, a business analyzes its KPIs (key performance
indicators) to identify its strengths and weaknesses. Thus, the
management team can decide in which area the company can
improve its operating efficiency.
BI(Business Intelligence) uses a set of processes, technologies,
and tools to transform raw data into meaningful information
and then transform information to provide knowledge. Then
afterward some beneficial insights can be extracted manually
and by some software then the decision-makers can make an
impactful decision on the basis of insights.

4. TOOLS USED IN BI:
1. Microsoft Power BI

5. 2. Tableau

6. 3.QlikSense

7. 4. Sisense

8. What is Data Science?
Data science involves extracting information from datasets and creating
forecasts. It uses machine learning, descriptive analytics, and other
sophisticated analytics tools. The process of data science starts from
collecting and maintaining data. The second step is to process data through
data mining, modeling, and summarization, By analyzing the data, the
patterns behind the raw data can be discovered to forecast future trends.
Data science is broadly used in many industries. Businesses can use such an
approach to develop new products, study customer preferences, and predict
market trends. For example, auto-driving developers collect extensive
amounts of data for statistical analysis. The developers work to improve the
auto-driving system so that it can be responsive to different situations
through machine learning.

9. TOOLS USED IN DATA SCIENCE:
1. APACHE HADOOP

10. 2. Data Robot

11. 3.BigML

12. 3.TensorFlow

13. 4.R Studio

14. ALGORITHMS USED IN DATA SCIENCE:
1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Naive Bayes
5. KNN(K-Nearest Neighbors)
6. Support Vector Machine (SVM)
7. K-Means Clustering
8. Principal Component Analysis (PCA)
9. Neural Networks
10. Random Forests

15. DIFFERENCE BETWEEN BI & DATA SCIENCE

16. S.
No.
Factor Data Science Business Intelligence
1. Concept
It is a field that uses mathematics, statistics and various
other tools to discover the hidden patterns in the data.
It is basically a set of technologies, applications and
processes that are used by the enterprises for
business data analysis.
2. Focus It focuses on the future. It focuses on the past and present.
3. Data
It deals with both structured as well as unstructured
data.
It mainly deals only with structured data.
4. Flexibility
Data science is much more flexible as data sources can
be added as per requirement.
It is less flexible as in case of business intelligence
data sources need to be pre-planned.
5. Method It makes use of the scientific method. It makes use of the analytic method.
6. Complexity
It has a higher complexity in comparison to business
intelligence.
It is much simpler when compared to data science.
7. Expertise It’s expertise is data scientist. It’s expertise is the business user.
8. Questions
It deals with the questions of what will happen and what
if.
It deals with the question of what happened.
9. Storage The data to be used is disseminated in real-time clusters. Data warehouse is utilized to hold data.
10. Integration of data
The ELT (Extract-Load-Transform) process is generally
used for the integration of data for data science
applications.
The ETL (Extract-Transform-Load) process is
generally used for the integration of data for
business intelligence applications.

17. SUMMARY:
•Business intelligence converts data into information that can support business
leaders in decision-making.
•Data science involves creating forecasts by analyzing the patterns behind the
raw data.
•Business intelligence is backward-looking that discovers the previous and
current trends, while data science is forward-looking and forecasts future
trends.
•Compared to business intelligence, data science is able to manage more
dynamic and less organized data. Yet, it also requires more technical skills and
resources.

18. THANK YOU❤