Brief deck for the 3 most important steps on data exploration. - Web Scraping (Import.io) - Data Cleaning (Spreadsheets) - Data Visualization (Tableau) Semana de las comunicaciones 2015

1. Data Exploration & BI
Cristian Guajardo Garcia

2. CristiánGuajardo-García
www.5561.cl
MBA Politecnico di Milano
& IIM Lucknow (India) B-
Schools.
Has worked in Chile &
Italy with companies such
as Universidad Andrés
Bello, ProChile, IBM,
Luxottica.

3. Agenda
1. Why data matters | BI
2. Data gathering: Scraping with Import.io
3. Data cleaning: Spreadsheets
4. Data visualization: Tableau
5. Insights for Marketing
6. Business Case: Luxottica
7. Q&A

4. 1. Why data matters | BI
2. Data gathering: Scraping with Import.io
3. Data cleaning: Spreadsheets
4. Data visualization: Tableau
5. Insights for Marketing
6. Business Case: Luxottica
7. Q&A

5. Why Data matters | BI
Today we have several sources generating tons of data per
second. Businesses need to anticipate the consumer in order
to remain competitive.
SQL = Sequence or structured
NOSQL = Unstructured data.
The goal of BI is to make the right information available to
the right people in the right time.
Big Data is nothing else but gathering tons of structured
and unstructured data, filtering it, cleaning it,
visualizing it and last but not least, getting insights from
it.

7. How and where do I start?
By Exploring the Data I don’t actually know where this will
take me. I just collect data from several sources and start
to explore it
The 2 most common scenarios to
start working with dat
By Answering a Question I know I have one question to ask.
This will lead my research and let me get rid of data that
is useless for this stage.

8. The Steps
1. Web Scraping (gather data)
2. Clean
3. Visualize and Analyze

9. 1. Why data matters | BI
2. Data gathering: Scraping with Import.io
3. Data cleaning: Spreadsheets
4. Data visualization: Tableau
5. Insights for Marketing
6. Business Case: Luxottica
7. Q&A

10. Data Gathering: Scraping with
Import.io

11. What is Scraping
Scraping is a technique used to extract data from one place
to another one, which is usually, a table.
Tabula = Extracts data from PDF
OCR = Extracts data from images
Import.io = Extracts data from
the web
Extractor Crawlers Connectors
Scraping is a very basic -yet useful- artificial
intelligence technique.

12. What is it?
● Machine reading the web
● Real time crawling through API
● Map Data of website
● Point & Click UI
● Turn data to structured data
● Tailor made crawlers
● Cloud scaling
● Wide integration options
From a minimum input get a
maximized output.

13. How does it work
Answer Question: What is the average € of
a Nike sneaker on eBay Italy?
1. Open Import.io
2. Create a new Connector
3. Go to ebay.it
4. Click “I’m there” button
5. Click the red button which will
record our click trail (now Import.
io will start recording your clicks)
6. Click stop button
7. Now you tell import.io what matters
to you and what is it (image, text,
link etc).
Pieces of information

14. Now you have the data you needed
You will create a bot that
basically gets pieces of
information that will be stored
in a table.
Once you have trained the bot to
crawl the whole results, you can
clean columns that you might not
use.
Now is time to manipulate the
data and get info like average
price, most common products and
so on.

15. 1. Why data matters | BI
2. Data gathering: Scraping with Import.io
3. Data cleaning: Spreadsheets
4. Data visualization: Tableau
5. Insights for Marketing
6. Business Case: Luxottica
7. Q&A

16. Data Cleaning: Spreadsheets

17. Store and clean
Once you have gathered the data, you might want to hide or
erase columns. Fill the n/a spaces or do some pivot table
maneuver. Whatever the case, Spreadsheet is a great way to
go.
Pivot Table: summarize big info
HLookup and Vlookup: target
specific info store in columns
and rows.

18. 1. Why data matters | BI
2. Data gathering: Scraping with Import.io
3. Data cleaning: Spreadsheets
4. Data visualization: Tableau
5. Insights for Marketing
6. Business Case: Luxottica
7. Q&A

19. Data Visualization: Tableau

20. What is it for?
Tableau is the ultimate desktop and cloud solution for
visualizing data coming from several sources.
Remember: privacy is an illusion
It works perfectly merging info from several sources:
Survey data, Social media, SEM and Analytics visualized in
one dashboard.
Perfect for reporting and meeting the needs of several
clients.

21. Why is it useful?
1. Tailor made dashboards
2. Several layers (and sources) of
information
3. Set clear goals and KPI’s
4. Easy to export
5. Works for several industries
and roles

22. Visualize and analyze data
Example of
Tableau

23. 1. Why data matters | BI
2. Data gathering: Scraping with Import.io
3. Data cleaning: Excel
4. Data visualization: Tableau
5. Insights for Marketing
6. Business Case: Luxottica
7. Q&A

24. How we can harness the power of the web
When we start working with data we stop “believing” and
start thinking. All the data available can help us to create
consumer profiles, specific interests, potential issues with
our product or even new ways to connect with them.
1. Forecast (where the puck is going)
2. The Rise of the Robots (automation)
3. Cross selling and tailor made dashboards per client
4. Insights like you’ve never seen before

25. Business Case
Applied to Marketing

26. The scenario
Untapped customer intelligence Luxottica
needed to analyze historical data pertaining
to more than 100 million customers to
increase marketing effectiveness.
© Copyright IBM Corporation 2013
The Impact
Centralized analytics The company deployed
advanced analytics technology from IBM to
create a 360-degree view of customers.
Actionable insights By identifying the
highest-value customers and creating
individualized marketing campaigns,
Luxottica anticipates a 10 percent boost in
marketing effectiveness.
1. Anticipates a 10 %
improvement in marketing
effectiveness
2. Identifies the highest-value
customers out of nearly 100
million
3. Targets individual
customers based on unique
preferences and histories

27. Recommendations
Adds on for Google Sheets
● Merge Sheets
● Data Everywhere
● Mapping Sheets
● Find Fuzzy Matches
● DukeDeploy
● BlockSpring
● Text Analysis
● Translate My Sheet
● AppSheet
● BigML

28. Q&A