Google Refine AnalysisA Business Perspective

April, 08 2012

Sathishwaran.R - 10BM60079Vijaya Prabhu - 10BM60097

Vinod Gupta School of Management, IIT Kharagpur

This Tutorial was created using Google Refine Version 2.5 on a Windows 7 platform

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Data Cleansing

• Data cleansing is identifying the wrong or inaccurate records in the data set and making appropriate corrections to the records.

• It involves identifying incomplete, inaccurate, and incorrect parts of data and then either replacing them with correct data or deleting the incorrect data

• Data cleansing results in data which is consistent with the other standard data and is useful for performing various analysis

• The error in the data could be due to data entry error by the user, failure during transmission of data or improper data definitions.

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Need for Data Cleansing

• Incorrect or inaccurate data may lead to false conclusions and can cause investments to be misdirected in finance.

• Also government needs accurate data on population and census for directing the funds to the deserving areas.

• Many organizations tap into customer information. If the data is not accurate, for eg. If the address is not accurate then the business runs the risk of send wrong information, thus losing customers.

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Challenges Data Cleansing

• Loss of Information: In many cases the record may be incomplete, hence the whole record may require to be deleted which leads to loss of information. It could become costly if huge number of data is deleted.

• Maintenance of Data: Once the data is cleansed then any change in the data specification needs to affect only the new values. Hence data management solutions should be designed in such a way that the process of data entry and retrieval are altered to provide correct data.

• Data cleansing is an iterative process which needs significant work in exploration and corrction of entries.

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About Google Refine

• Google Refine is a powerful tool that can be effectively used for data cleansing.

• It helps in working with raw data, cleaning it up, transforming from one format to other, encompassing it with web services and linking it to databases.

• It is very easy to use and has a web interface.• It is freely available and works well with any browser.• Google Refine is a desktop application and it runs a

small web server on your system and we need to point our browser to the server to use refine.

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Getting Started - Installation

1. Download the zip file (appropriate Windows, Mac, Linux versions) from the link http://code.google.com/p/google-refine/wiki/Downloads?tm=2

2. Uncompress the files from the zip file.3. Run the “google-refine.exe” file.4. A command window opens and Google refine

runs taking the user to the home page in the default browser.

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Google Refine Homepage

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Importing Data

• Google Refine supports TSV, CSV, Excel (.xls and .xlsx), JSON, XML, and Google data document formats.

• Once imported the data is in Google Refine’s own data format.

• We have used TSV data on Disasters worldwide from 1900-2008 available from http://www.infochimps.com/datasets/disasters-worldwide-from-1900-2008 for the tutorial.

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Importing Data

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Importing Data

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Creating ProjectData Uploaded

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Creating Project Project Created

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Faceting

• Faceting is about seeing the big picture and filtering based on rows to work on data you want to change in bulk.

• We can create a facet for a column to get the details about that column and then we can filter to a subset of rows with a constraint.

• We can perform text facet, Numeric facet, timeline facet and scatterplot facet. Also various customized facets can be designed.

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Faceting

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Faceting

The Column Type has 18

unique options

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Removing Redundancy

Even though they are of same type, shows as

different options due to case

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Removing Redundancy

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Removing Redundancy

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Removing Redundancy

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Removing Redundancy

Reduced to 15 unique options

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Numeric Faceting

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Numeric Faceting

Highly clustered towards low

values

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Numeric Faceting

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Numeric Faceting

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Numeric Faceting

Cost column is blank and has no

value

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Numeric Faceting

Calamities with low cost

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Numeric Faceting

Calamities with high cost

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Clustering• Clustering is used to merge choices which look similar.

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Clustering

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Clustering

Data Merged

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Using Expressions• Expressions are used to transform existing data to create new data

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Using Expressions

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Using Expressions

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Data Augmentation

• Reconciliation option in Google refine allows data to be linked to web pages. Suppose we want details on the country where the calamity has struck we can perform the following steps

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Reconciliation

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Reconciliation

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Reconciliation

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Reconciliation

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Reconciliation

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Data Enrichment

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Data Enrichment

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Data Enrichment

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Data Enrichment

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Export

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Step 1

Step 2

How to Use Twitter Data

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Step 3

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Step 4

Step 5

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Step 6

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Step 7 Step 8

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Output

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

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Friends Events using Facebook data

• After splitting the cell using separator },{

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Friends Events using Facebook data

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Friends Events using Facebook data• After updating for other columns and rearranging it we get the events as

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DISLIKED

LIKED

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Thank You