BIG DATA and DATA ANALYTICS
Program Studi: Manajemen Bisnis Telekomunikasi & Informatika Mata Kuliah: Big Data And Data Analytics Oleh: Tim Dosen
UNDERSTANDING BIG DATA
BIG DATA and DATA ANALYTICS
1. RPS / Course Plan (13 weeks) 2. Lab Activity (week 3-week 12) 3. Grade Percentage TASK1:UTS:TASK2:UAS -> 25%:25%:25%:25% 4. Class Rules : Be Active, On Time (Before Time) 5. Class Coordinator (pick one) 6. Lab Activity using the following tools : R Language (R Studio), Orange, Weka, RapidMiner, Gephi
COURSE OBJECTIVE 1.
Understand conceptual, framework, opportunity and challenge of Big Data 2. Understand concept, theory, framework from Data Analytics activities 3. Able to choose and perform Data Analytics activities based on the contextual business problem 4. Able to build description model and prediction model using available data o o Introduction, Background & Definitions o Data Driven Decision Making o Big Data Properties o Big Data Complexity Big Data Framework and State of the Art
OUTLINE o o Big Data for Business Case Study Large Scale Data
Large Scale Data
Background o o We are generating huge amounts of data (from UGC to mobile habit) Our society is leaving behind a digital footprint (so our behavior / o attitudes) Finding unexpected pattern is so exciting (also useful for predictive o analytics) The need to find the usage of Large Scale of Data Warehouse
(internal data)
Definition o Big Data : It is a term for data sets that are so large or complex that traditional data processing tools are inadequate to process. The challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information o privacy (wikipedia) Data Analytics : It is the science of examining raw data with the purpose of drawing conclusions about that information. Data Analytics is used in may industries to allow companies and organization to make better business decisions and in the sciences
to verify or disprove existing models or theories (wikipedia)
Definition o Social Computing: It is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or recreating social conventions and social o contexts through the use software and technology (wikipedia) Data Science : It is interdisciplinary field about processes and system to extract knowledge or insight from data in various forms, either structured or unstructured. This field is continuation of some the data analysis field such as statistics, data mining, and predictive analytics(wikipedia)
Example (in a supermarket) :
1. Descriptive : Total Product A,
B, C, D sold. Retailer will know which product are sold / popular
2. Predictive : People who buy product A, mostly also buy product B. Retailer know / predict the future event
3. Prescriptive : Giving recommendation what product to buy based on our profile / requirement. Giving recommendation how to achieve the goal Data Driven Decision Making
- Data science involves principles, processes, and techniques for understanding phenomena via the (automated) analysis of data
- The ultimate goal of data science as improving decision making, as this generally is of direct interest to business
- Statistically, the more data-driven a firm is, the more productive it is
—even controlling for a wide range of possible confounding factors. And the differences are not small. One standard deviation higher on the DDD scale is associated with a 4%
- –6% increase in productivity.
- DDD also is correlated with higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal
Big Data Properties
Big Data Complexity Big Data Analytics Constructor
Big Data Approach Framework
Some people prefer 3Vs,
6Vs or 7Vs even 12Vs to explain big data. But the original “bigness” measurement metrics are volume, velocity, and variety.
For example 7Vs:
1. Volume
2. Velocity
3. Variety
4. Variability
5. Veracity
6. Visualitazion
7. Value Big Data State of The Art
The.Jungle.Book.2016.720p.BluRay.x264-[YTS.AG].mp4 Big Data for Business
- Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction
Big Data for Business
Market-Basket transactions TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke Example of Association Rules
{Diaper} {Beer},
{Milk, Bread} {Eggs,Coke},
{Beer, Bread} {Milk},
Implication means co-occurrence, not causality! Big Data Use Case by Industry Energy
Telecommunication Retail
- Smart Meter Analytics • Distribution Load Forecasting and Scheduling • Condition-Based Maintenance • Network Performance • New Products and Service Creation • Call Detail Records (CDRs) Analysis • Customer Relationship Management • Dynamic Price Optimization • Localized Assortment • Supply-Chain Management • Customer Relationship Management
Manufacturing Banking Insurance
- Supply Chain Management • Customer Care Call Centre • Preventive Maintenance and Repairs • Customer Relationship Management • Fraud Detection • Trade Surveillance • Compliance and Regulatory • Customer Relationship Management • Catastrophe Modeling • Claims Fraud • Reputation Management • Customer Relationship Management
Public Media
Healthcare
- Fraud Detection • Fighting Criminality • Threats Detection • Cyber Security • Large-Scale Clickstream Analytics • Abuse and Click Fraud Prevention • Social Graph Analysis and Profile Segmentation • Campaign Management and Loyalty Programs
- Clinical Trials Data Analysis • Patient Care Quality and Program Analysis • Supply Chain Management • Drugs Discovery and Development Analysis
Business Intelligence Vs Data Science
Based on data analytics types, Data Science’s practices focuses on prediction and prescription. Case Study : Hurricane Frances
“Hurricane Frances was on its way, barreling across the Caribbean, threatening a direct hit on Florida’s Atlantic coast. Residents made for higher ground, but far away, in Bentonville, Ark., executives at Wal-Mart Stores decided that the situation offered a great opportunity for one of their newest data-driven weapons
… predictive technology A week ahead of the storm’s landfall, Linda M. Dillman, Wal-Mart’s chief information officer, pressed her staff to come up with forecasts based on what had happened when Hurricane Charley struck several weeks earlier. Backed by the trillions of bytes’ worth of shopper history that is stored in Wal-Mart’s data warehouse, she felt that the company could
‘start predicting what’s going to happen, instead of waiting for it to happen,’ as she put it. (Hays, 2004 )” Case Study : Customer Voice (Telco) Telkomsel XL
Research Roadmap
1. NEW DATA
Example: eCommerce capturing clickstream 2.
UNLOCKING VALUE
Example: Sentiment analysis from social networkds 3.
SHAPING THE FUTURE
Example: modelling the future, anticipating & incluencing To wrap up, Why Big Data Analytics Matters Assignment o Find a Case Study of Big Data Implementation / Application for o Business or others o State the objective, problems, solution idea o State the methodology used
State the model, measurement, accuracy o
Learn Big Data online free course (www.bigdatauniversity.com)