LESSON 1 - INTRO TO DATA ANALYTICS

Data Analytics

the process of examining, cleaning, transforming and modeling data to discover useful information, draw conclusions, and support decision-making

Data Analytics

transforms raw data into insights

Small Data

• limited in size

• easy to store

• easy to analyze using traditional tools (Excel, SQL)

Small Data

is characterized as:

• structured data (tables, rows, columns)

• generated in small volumes

• historical and static

Microsoft Excel

Google Sheets

MySQL / MS Access

Tools used in Small Data

Student grades in Excel

Monthly sales report of a small store

Attendance records

examples of small data

Big Data

refers to extremely large and complex datasets that cannot be efficiently using traditional data processing tools

Advance analytics

AI

What does big data enables?

Datafication

it is shaping modern life

Volume

Velocity

Variety

Veracity

Value

The 5 V’s of Big Data

Volume

massive amount of data (terabytes, petabytes)

Velocity

High speed of data generation (real-time)

Veracity

data quality and reliability

Value

usefulness of data

Hadoop

Spark

NoSQL Databases (MongoDB)

Cloud Platforms (AWS, Google Cloud)

tools used by big data

Facebook posts, likes, comments

Shopee customer clickstreams

GPS Data from Grab drivers

CCTV and video analytics

Examples of Big data

Datafication

it is the process of turning everyday activities, behaviors, and interactions into quantifiable data

Walking - step counts (smartwatch)

Studying - Learning analytics (LMS)

Examples of datafication

Improve decision-making

Personalize services

Why datafication matters

Supply Chain Analytics

using data analytics to improve procurement, production, inventory, distribution, logistics

Supply Chain Analytics

it improves efficiency and decision-making

Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

4 types of Supply Chain Analytics

Descriptive Analytics

• answers the question: “what happened?”

• focuses on historical data, reporting and dashboards

• uses Excel dashboards, BI tools ((Power BI, Tableau)

Diagnostic Analytics

• answers the question: “why did it happened?”

• focuses on root cause analysis, performance issues

• examples: “why were the deliveries delayed?” , “why did inventory run out?”

Predictive Analytics

• answers the question: “what is likely to happen?”

• focuses on forecasting and trend analysis

• examples: demand forecasting, sales prediction, and supplier risk prediction

• uses tools like machine learning models and time series analysis

Prescriptive Analytics

• answers the question: “what should we do?”

• focuses on optimization and decision support

• examples are dest delivery time, optimal inventory level, and supplier selection