1.
[BIG] DATA ANALYTICS
ENGAGE WITH YOUR CUSTOMER
PREPARED BY GHULAM I

2.
ABOUT ME
Currently work in Telkomsel as senior data analyst
8 years professional experience with 4 years in big data
and predictive analytics field in telecommunication
industry
Bachelor from Computer Science, Gadjah Mada
University & get master degree from Magister of
Information Technology, Universitas Indonesia
Lecturer in Muhammadiyah Jakarta University
https://id.linkedin.com/pub/ghulam-imaduddin/32/a21/507
ghulam@ideweb.co.id

3.
WHAT’S IN THIS SLIDE
[BIG] DATA ANALYTICS
Intro & Data Trends
Challenges
Tech Approach
Big Data Tools
Type of Analytics
Tools
Analytics Lifecycle
 Use Cases (Sentiment Analysis)
 What’s Trending
 Where to Start
Methodology

4.
THE WORLD OF DATA
Source: http://www.cision.com/us/2012/10/big-data-and-big-analytics/

5.
DATA VS BIG DATA
Big data is just data with:
 More volume
 Faster data generation (velocity)
 Multiple data format (variety)
World's data volume to grow 40% per year
& 50 times by 2020 [1]
Data coming from various human & machine
activity
[1] http://e27.co/worlds-data-volume-to-grow-40-per-year-50-times-by-2020-aureus-20150115-2/

6.
CHALLENGES
 More data = more storage space
 More storage = more money to spend  (RDBMS server needs very costly
storage)
 Data coming faster
 Speed up data processing or we’ll have backlog
 Needs to handle various data structure
 How do we put JSON data format in standard RDBMS?
 Hey, we also have XML format from other sources
 Other system give us compressed data in gzip format
 Agile business requirement.
 On initial discussion, they only need 10 information, now they ask for 25? Can
we do that? We only put that 10 in our database
 Our standard ETL process can’t handle this

7.
STORAGE COST
In Terms of storage cost, Hadoop has lower comparing to standard
RDBMS.
Hadoop provides highly scalable storage and process with fraction of
the EDW Cost

8.
STORAGE & COMPUTE
TOGETHER
The Hadoop Way
The Old Way
• Hard to scale
• Network is a bottleneck
• Only handles relational data
• Difficult to add new fields & data types
Expensive, Special purpose, “Reliable” Servers
Expensive Licensed Software
Network
Data Storage
(SAN, NAS)
Compute
(RDBMS, EDW)
• Scales out forever
• No bottlenecks
• Easy to ingest any data
• Agile data access
Commodity “Unreliable” Servers
Hybrid Open Source Software
Compute
(CPU)
Memory Storage
(Disk)
z
z
Source: Cloudera Presentation Deck by Amr Awadallah

9.
MAP REDUCE APPROACH
 Process data in parallel way using distributed algorithm on a cluster
 Map procedure performs filtering and sorting data locally
 Reduce procedure performs a summary operation (count, sum,
average, etc.)

10.
HADOOP vs UNSTRUCTURED
DATA
 Hadoop has HDFS (Hadoop Distributed File System)
 It is just file system, so what you need is just drop the file there 
 Schema on read concept
Source Data
Database Table
Load the data
Metadata
Applying schema
User
Application (BI Tools)
RDBMS
APPROACH
HADOOP
APPROACH

11.
HIVE
 The Apache Hive ™ data warehouse software facilitates querying and
managing large datasets residing in distributed storage.
 With Hive you can write the schema for the data in HDFS
 Hive provide many library that enable you to read various data type
like XML, JSON, or even compressed format
 You can create your own data parser with Java language
 Hive support SQL language to read from your data
 Hive will convert your SQL into Java MapReduce code, and run it in
cluster

12.
 Apache spark is fast and general engine for large-scale data processing
 Run programs up to 100x faster than Hadoop MapReduce in memory,
or 10x faster on disk
 You can write spark application in Java, Scala, Python, or R
 Spark support library to run SQL, streaming, and complex analysis like
graph computation and machine learning
 https://spark.apache.org/

13.
ANALYTICS

14.
ANALYTICS IS IN YOUR BLOOD
 Do you realize that you do analytics everyday?
 I need to go to campus faster!
 Hmm.. Looking at the sky today, I think it’ll be rain
 Based on my mid term and assignment score, I need to get at least 80
in my final exam to pass this course
 I stalked her social media. I think she is single because most of her
post only about food :p

15.
DESCRIPTIVE & PREDICTIVE
 Descriptive statistics is the term given to the analysis of data that helps
describe, show or summarize data in a meaningful way such that, for
example, patterns might emerge from the data.
 In Information System Design course, most of the student get C grade (11
people). There is 4 people get A, 7 get B, 7 get D, and 7 get E
 Fulan only post his activity on Facebook at weekend
 Predictive analytics is the branch of data mining concerned with the
prediction of future probabilities and trends.
 The central element of predictive analytics is the predictor, a variable
that can be measured for an individual or other entity to predict future
behavior.
 Fulan should be has a job. Because he always left home at 7 in the morning
and get back at 6 afternoon

16.
PREDICTIVE ANALYTICS
There is 2 types of predictive analytics:
◦ Supervised
Supervised analytics is when we know the truth about something in the past
Example:
we have historical weather data. The temperature, humidity, cloud density and
weather type (rain, cloudy, or sunny). Then we can predict today weather
based on temp, humidity, and cloud density today
Machine learning to be used: Regression, decision tree, SVM, ANN, etc.
◦ Unsupervised
Unsupervised is when we don’t know the truth about something in the past.
The result is segment that we need to interpret
Example:
We want to do segmentation over the student based on the historical exam
score, attendance, and late history

17.
APPLYING THE CONTEXT
Source
Raw
&
unstructured
Location
Socmed data,
Complaint,
Survey
URL access CDR
Device info
IMEI
&
TAC
Point Of Interest, sentiment library, socmed buzzer, website category
Context
Derived
Information
Commute pattern
Hangout location
Idols
Political view
Pain point
Community leader
Family member
Communication spending

18.
ANALYTICS LIFECYCLE
- Defining target variable
- Splitting data for training and
validating the model
- Defining analysis time frame
for training and validation
- Correlation analysis and
variable selection
- Selecting right data mining
algorithm
- Do validation by measuring
accuracy, sensitivity, and
model lift
- Data mining and modeling is
an iterative process
Data
Mining
& Modeling
- Define variables to
support hypothesis
- Cleaning &
transforming the data
- Create longitudinal
data/trend data
- Ingesting additional
data if needed
- Build analytical data
mart
- Gathering problem
information
- Defining the goal to
solve the problem
- Defining expected
output
- Defining hypothesis
- Defining analysis
methodology
- Measuring the
business value
Data
Understanding
Business
Understanding

19.
ANALYTICS LIFECYCLE
- Create monitoring
process for model
evaluation
- Evaluate the model
based on real-world
result
- Monitor and evaluate
the business impact
Model
Monitoring
- Define the model scoring
period
- Integrate model result
with execution system
(campaign system, CRM,
etc)
- Create operational
process that timely,
consistent, and efficient
Model
Operationalization
- Describe the importance
of each variable
- Visualize overall model
by creating decision tree
for example
- Define business action
based on the model
result
Model
Interpretation
Analytics and modeling is an iterative process. Data model will become
obsolete and need to evolve to accommodate changes in behavior

20.
BUILDING THE
METHODOLOGY
Analysis Domain
• What is the analysis domain? Is it for male only? Is it for housewife or worker? Your
“customer” segment has different behavior
Type of Analysis
• Do we need only descriptive analysis? Or we need to go with predictive analysis?
Supervised or Unsupervised?
• Do we need to build unsupervised clustering/segmentation for this analysis?
Define Analysis Time Window
• What time window of data we need for behavior observation?
• What is the prediction time window?
• Is there any seasonal event on that time window?

21.
ANALYTICS TOOLS
Microsoft Excel. Very powerful tools to do statistical data manipulation, pivoting, even doing
simple prediction
SQL is just the language. Your data lying in database? SQL will help to filter, aggregate and
extract your data
RapidMiner provide built-in RDBMS connector, parser for common data format (csv, xml),
data manipulation, and many machine learning algorithm. We can also create our own library.
Latest version of RapidMiner can connect to Hadoop and do more complex analysis like text
mining. Free version is available (community edition)
KNIME. Known as a powerful tools to do predictive analytics. Overall function is similar to
RapidMiner. Latest version of KNIME can connect to Hadoop and do more complex analysis
such as text mining. Free version is available
Tableau is one of the famous tools to build visualization on top of the data. Tableau also
powerful to create interactive dashboard. Free version is available with some limitation
QlikView. Similar to Tableau, QlikView designed to enable data analyst to develop a
dashboard or just simple visualization on top of the data. Free version is available

22.
SAMPLE
USE CASE #1
SENTIMENT ANALYSIS ON TWITTER DATA

23.
BACKGROUND
Objective
Measuring customer sentiment over big tree telecommunication provider in
Indonesia (Telkomsel, XL, Indosat)
Metric
Measuring NPS (Net Promotor Score) for each operator using twitter data.
NPS calculated as percentage of positive tweets minus percentage of
negative tweets.
Putra, B. P. (2015). Analisis Sentimen Layanan Telekomunikasi pada Pengguna Media Sosial Twitter. Jakarta: Universitas Indonesia

24.
WORKFLOW
- Generate word vector
using machine
learning algorithm
based on training
dataset
- Using SVM and C4.5
- The result is 2
different model
- Select the best model
by comparing the
accuracy
Data
Modeling
- Deduplication
- Convert to lower case
- Tokenization
- Filter stop word
Data
Preparation
- Label some sample
for training dataset
- This part done with
crowdsourcing
Data
Labeling
- Create twitter crawler
with python and
twitter API
- Run the crawler with
selected keyword,
parse, and store to
RDBMS
- Collection for tweet
generated in April
2015
Data
Collection

25.
WORKFLOW
- Aggregate scoring
result by telco
provider to get count
of positive tweets
and negative tweets
- Calculate the NPS for
each telco provider
- Visualize the result as
a bar chart
NPS
Calculation
- Using best model,
score the rest dataset
- Scoring result is a
label
(positive/negative/
neutral) for each
tweet
Data
Scoring

26.
DATA COLLECTION
 We run the crawler 3 times, one time for each operator. We only
search tweets containing some keywords
 Parse the json result using json parser library embedded in python 2.7,
form it as CSV (comma separated value)
 Load the csv into database (we use MySQL in this experiment)
• Telepon
• SMS
• Internet
• Jaringan
• Telkomsel
• Indosat
• XL

27.
DATA LABELING
 The objective is to build the ground truth
 Using crowdsourcing approach. We build online questionnaire and ask
people to define each tweets if it is negative, positive, or neutral
 We label 100 tweets by ourselves as a validated tweets for
questionnaire validation
 We put 20 tweets for each questionnaire. 5 tweets for Indosat, 5 for
XL, 5 for Telkomsel, and the rest 5 is random validated tweets
 If 4 out of 5 validated tweets answered correctly, then we flag a
questionnaire as a valid questionnaire
 This approach used to eliminate the answer submitted by people who
do it randomly

28.
DATA PREPARATION
 Deduplication process is to remove duplicated tweets
 Tokenization is a process to split a sentence into words. This should be
done because the model will generate the word vector instead of
sentence.

29.
DATA PREPARATION
 Filtering stop words. We eliminate non useful word (word that doesn’t
reflect to positive or negative means)

30.
TOOLS USED
 Data preparation modeling done with RapidMiner software
 RapidMiner has text analysis function and procedure. We can found
procedure to do tokenize, convert case, deduplication, and filter stop
word
 RapidMiner also has SVM and C4.5 algorithm to do modeling

31.
MODEL ACCURACY
 Model accuracy measurement done by confusion matrix
 In this experiment, we found that SVM performs better than C4.5
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
(𝑇𝑃 + 𝑇𝑁)
(𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁)

32.
NPS Result
 After we do aggregation for scored dataset, we found that Indosat has
higher NPS than the others.
Telco % Promoters % Detractors NPS
Indosat 37% 14% 23%
Telkomsel 30% 27% 3%
XL 19% 37% -18%

33.
SAMPLE
USE CASE #2
SIMPLE DESCRIPTIVE ANALYTICS USING HADOOP
AND SPARK

34.
BACKGROUND
 This is the demonstration how to use Apache Spark to extract some
information from twitter data
 Twitter data collected with some crawler made with python language,
and store as it is (JSON formatted data)

35.
DATA EXPLORATION
 Load JSON data to memory
val tweets = sqlContext.jsonFile("/user/flume/tweets/2015/09/01/*/*")
 Looking the data schema, and select useful field only
tweets.printSchema

36.
DATA EXPLORATION
 Finding top 10 users based on tweet count
tweets.
select("user.screen_name").
rdd.map(x => (x(0).toString,1)).
reduceByKey(_+_).
map(_.swap).
sortByKey(false).
map(_.swap).
take(10).
foreach(println)

37.
DATA EXPLORATION
 Finding top words
tweets.select("text").rdd.
flatMap(x => x(0).toString.toLowerCase.
split(“[^A-Za-z0-9]+")).
map(x => (x,1)).
filter(x => x._1.length >= 3).
reduceByKey(_+_).
map(_.swap).
sortByKey(false).
map(_.swap).
take(20).foreach(println)

38.
DATA EXPLORATION
 Finding top words with stop word exclusion
val stop_words = sc.textFile("/user/ghulam/stopwords.txt")
val bc_stop = sc.broadcast(stop_words.collect)
tweets.select("text").rdd.
flatMap(x => x(0).toString.toLowerCase.split("[^A-Za-z0-9]+")).
map(x => (x,1)).
filter(x => x._1.length > 3 & !bc_stop.value.contains(x._1)).
reduceByKey(_+_).
map(_.swap).sortByKey(false).map(_.swap).
take(20).foreach(println)

39.
DATA EXPLORATION
 Words Chain (Market Basket Analysis)
import org.apache.spark.mllib.fpm.FPGrowth
val stop_words = sc.broadcast(sc.textFile("/user/hadoop-
user/ghulam/stopwords.txt").collect)
val tweets = sqlContext.jsonFile("/user/flume/tweets/2015/09/01/*/*")
val trx = tweets.select("text").rdd.
filter(!_(0).toString.toLowerCase.contains("ini 20 finalis aplikasi")).
filter(!_(0).toString.toLowerCase.contains("telkomsel jaring 20 devel")).
filter(!_(0).toString.toLowerCase.contains("[jual")).
filter(!_(0).toString.toLowerCase.contains("lelang acc")).
filter(!_(0).toString.toLowerCase.matches(".*theme.*line.*")).
filter(!_(0).toString.toLowerCase.matches(".*fol.*back.*")).
filter(!_(0).toString.toLowerCase.matches(".*favorite.*digital.*")).
filter(!_(0).toString.toLowerCase.startsWith("rt @")).
map(x => x(0).toString.toLowerCase.split("[^A-Za-z0-9]+").filter(x =>
x.length > 3 & !stop_words.value.contains(x)).distinct)
val fpg = new FPGrowth().setMinSupport(0.01).setNumPartitions(10)
val model = fpg.run(trx)
model.freqItemsets.filter(x => x.items.length >= 3).take(20).foreach {
itemset =>
println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
}

40.
WORLD TRENDS

41.
2015 HYPE CYCLE
Big data related things in top
of hype curve:
• Advanced analytics
• IoT
• Machine Learning

42.
DATA SCIENTIST
 Data scientist/analyst is one of the sexy and emerging job in the market

43.
WHERE TO START
LETS GET OUR HAND DIRTY

44.
SKILLS NEEDED
DOMAIN KNOWLEDGE

45.
SKILLS NEEDED
 Business Acumen
In terms of data science, being able to discern which problems are
important to solve for the business is critical, in addition to identifying
new ways the business should be leveraging its data.
 Python, Scala, and SQL
SQL skills is a must! Python and Scala also become a common language to
do data processing, along with Java, Perl, or C/C++
 Hadoop Platform
It is heavily preferred in many cases. Having experience with Hive or Pig is
also a strong selling point. Familiarity with cloud tools such as Amazon S3
can also be beneficial.
 SAS or R or other predictive analytics tools
In-depth knowledge of at least one of these analytical tools, for data
science R is generally preferred. Along with this, statistical knowledge also
important

46.
SKILLS NEEDED
 Intellectual curiosity
Curiosity to dig deeper into data and solving a problem by finding a
root cause of it
 Communication & Presentation
Companies searching for a strong data scientist are looking for
someone who can clearly and fluently translate their technical findings
to a non-technical team. A data scientist must enable the business to
make decisions by arming them with quantified insights
Summarized from http://www.kdnuggets.com/2014/11/9-must-have-skills-data-scientist.html

47.
[BIG] DATA SOURCES
 Social media platform. Most of social media provided some API to
fetch the data from there. Twitter and Facebook is the most common
example
 KDNuggets (http://www.kdnuggets.com/datasets/index.html)
 Kaggle (https://www.kaggle.com/)
 Portal Data Indonesia (http://data.go.id/)
 Your WhatsApp group conversation

48.
ONLINE TUTORIAL
 Coursera (https://www.coursera.org/)
 DataQuest (https://www.dataquest.io/)
 Udacity (https://www.udacity.com/)
 TutorialsPoint (http://www.tutorialspoint.com/)
 Youtube, RapidMiner Channel
(https://www.youtube.com/user/RapidIVideos)
 Youtube KNIME TV (https://www.youtube.com/user/KNIMETV)
 Cloudera Quickstart VM (http://www.cloudera.com/content/www/en-
us/documentation/enterprise/latest/topics/cloudera_quickstart_vm.html)
 Hortonworks Sandbox VM
(http://hortonworks.com/products/hortonworks-sandbox/)
 Apache Spark Page (https://spark.apache.org/examples.html)

49.
PREPARE
YOURSELF
TO SURF THE DATA ERA!

50.
Question?