Big Data Analytics - Living in the IT Era

Big Data Analytics refers to the process of examining large and varied data sets, or big data, to uncover hidden patterns, correlations, market trends, customer preferences, and other valuable information. This process helps organizations make more informed decisions, enhance operational efficiency, and create competitive advantages.

2. 8 Data is the new

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4. Big Data Analytics
Data

© Definition from Merriam-Webster Dictionary
factual information (such as measurements or statistics) used
discussion, or calculation
information in digital form that can be transmitted or processed,
information output by a sensing device or organ that includes both usef
or redundant information and must be processed to be meaningful.

O (in computing) information that has been translated into a form that is efficient fo
or processing

5. Big Data Analytics

Data: Idea on Storing It

UNIT
1byte
1kilobyte
1megabyte
1 gigabyte
terabyte

1 petabyte
Texabyte
1zettabyte
1yottabyte
1 brontobyte

VALUE
8 bits (binary digits)
1024 bytes

1024 kilobytes
1024 megabytes
1024 gigabytes
1024 terabytes
1024 petabytes
1024 exabytes
1024 zettabytes
1024 yottabytes

6. Big Data Analytics

(Data) Analytics

© It is a broad term that encompasses the processes, techno

algorithms to extract meaningful insights from data.

categorizing, condensing, and contextualizing the data.

O Analytics is also about the provisioning of data, information, and insights:

processes in an intelligent way.

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7. Big Data Analytics

(Data) Analytics: Goals

© Topredict something

© whether a transaction is a fraud or not, whether it will rain on a particular da
benign or malignant =

© Tofind patterns in the data
© finding the top 10 coldest days in the year, finding which pages are visited the most on'a par

website, or finding the most searched celebrity in a specific year
© Finding relationships in the data =

O finding similar news articles, finding similar patients in an electronic health record system, finding

related products on an eCommerce website, finding identical images, or Finding the correlation

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between news items and stock prices

8. Diagnostic
analytics

Descriptive
analytics

9. Big Data Analytics
Analytics: Types

© Descriptive Analytics
It analyzes past data to present it in a summarized form that cai

be easily interpreted.

What has happened?

Included statistical functions

counts, maximum, minimum, mean, top-N, percentage

10. Big Data Analytics
Analytics: Types

© Diagnostic Analytics
It comprises the analysis of past data to diagnose the reason:

happened

Why did it happen?)

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11. Big Data Analytics
Analytics: Types
© Predictive Analytics

It comprises predicting the occurrence of an event or the likely ou
forecasting the future values using prediction models

learn patterns and trends from the existing data and predict the occurre
or the likely outcome of an event (classification models) or forecast number:
(regression models).

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12. Big Data Analytics
Analytics: Types

o Hess Analytics
It multiple prediction models to predict various outcomes and: t
best course of action for each outcome.
It can predict the possible outcomes based on the current choice
of actions.
It prescribes actions or the best option to follow from the available
options.

6 À

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13. 5 TYPES OF ANALYTICS:

+ Descriptive
+ Diagnostic

«Pre

DESCRIPTIVE
ANALYTICS =

"It combines a number of
intelligent technologies like
ial intelligence,

COGNITIVE machine-learning —
ANALYTICS algorithms, deep learning Raed

etc. to apply human brain Are =

like intelligence to perform

certain tasks."

COGNITIVE
ANALYTICS

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14. Big Data Analytics

Big Data

©

It is defined as collections of datasets whose volume, velocity, or

processing tools. N

Big data can be used to improve operations, provide better customer service and
personalized marketing campaigns -- all of which can increase value for an organizai
"Big Data is data whose scale, distribution, diversity, and/or timeliness require the use.
technical architectures and analytics to enable insights that unlock new sources of business
value.” -- McKinsey & Co.; Big Data: The Next Frontier for Innovation, Competition,
Productivity

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18. Big Data Analytics

Big Data: Notes

© Specialized tools and frameworks are required for big data analysis wl
the volume of data involvedis so large that it is difficult to store, proc:
a single machine,
the velocity of data is very high, and the data needs to be analyzed in real-ti
there is a variety of data involved, which can be structured, unstructured on
andis collected from multiple data sources,

descriptive, diagnostic, predictive, and prescriptive analytics.

20. Big Data Analytics

Big Data: Examples

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behavior
Machine sensor data collected from sensors embedded in industrial and energy
monitoring their health and detecting failures

Healthcare data collected in electronic health record (EHR) systems
Logs generated by web applications

Stock markets data

Transactional data generated by banking and financial applications mo À

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21. ⬡Structured Data
⬡Semi-structured Data
⬡Quasi-structured Data
⬡Unstructured Data
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24. Financial

nalytics + Credit Risk Modeling
ance Monitoring - Fraud Detection
Targeting and

Recommendation

Retail

+ Inventory Management

+ Customer
Recommendation

+ Store Layout

Optimization
- Forecasting Demand

26. Benefits of Big Data
Analytics

27. Results

Almost two-thirds of digital leaders think big dat:
top 2 technologies to deliver competitive advanta

82% of organizations with advanced maturity in data anda
positive year-over-year (YOY) revenue growth over the pa:

Over 50% of executives say they are driving business innovati
data.

92% of data leaders say their company got measurable business
from data and analytics investments.

98% of data leaders think they'll see a return on their data investme

4 out of 10 companies have seen measurable benefits and cost savil
from data and analytics initiatives.

22% of chief executive officers believe using data effectively to develop new
products and services is an important source of their growth in the next five years.

A fifth of digital leaders feel they are effectively using data insights to
generate more revenue.

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28. Challenges
of Big Data
Analytics

Data Quality

Synchronization of Data Sources

Organizational Resistance

Accessibility to Big Data

Maintaining quality data.

Keeping data security.

Finding the right tools and platforms. «

Lack of talent. 5 >

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