Data enrichment enhances existing data by appending additional information, improving accuracy, customer engagement, segmentation, and personalization. Techniques include data cleansing, appending data, and creating derived attributes, while types encompass socio-demographic, geographic, and purchase intent data. Tools for data enrichment can automate the organization and cleaning of data from various sources.

1. Data
Enrichment
#techtuesdays

2. #techtuesdays
Data Enrichment is the
process of adding value to
existing data by enhancing or
appending data to an existing
dataset with additional
information
What is Data Enrichment?

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Improves data accuracy
Aids in customer engagement
Helps in segmentation of customers
Improves lead scoring
Enhance Personalisation
Why is Data Enrichment
Required?

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Data Scraping: Extraction of data from
websites or other online sources using
specialized softwares
Manual Research: Involves data being
manually entered into a spreadsheet
or database
Data Enrichment tools: Specialized tools that
gathers, organizes, cleans, and formats data
from third-party sources and aggregates it
from different sources.
Methods of Data Enrichment

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Data cleansing means removing
inaccurate and duplicate data.
Data Enrichment is collecting additional
data for a dataset and appending it to
cover the missing information
Difference between Data
Enrichment and Data
Cleansing

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Data Enrichment Techniques
Appending Data - by appending data from
various sources to a dataset accuracy, and
consistency of a dataset is increased to give
a comprehensive picture.
Data Segmentation - data from a dataset is
grouped based on some predefined
variables like age, gender, income to be
accurately described as a category
Data Manipulation - Technique of substituting
missing or inconsistent data inside fields

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Data Enrichment Techniques
Derived Attributes - creation of new fields
from existing values of a dataset like deriving
age from date of birth or state of residence
from zipcode
Entity Extraction - Extraction of useful structured
data from semi-structured or unstructured data
Data Categorization - Process of classifying
unstructured information via sentiment
analysis or topication to make it analyzable

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Types Of Data Enrichment
Socio-demographic data - It is the
process of enriching a dataset with specific
demographic information with the intent
of targeting a special category of users
Geographic data - It is the process of adding
geographic data to an existing dataset with
the intent of location based marketing

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Types Of Data Enrichment
Purchase intent data - It is the process of
enriching a dataset with shopping data and
product view frequency for granular
marketing and precise view of customer’s
willingness to buy
Data Enrichment based on app usage
Enriching dataset based on app usage
information to identify overall customer
experience and preferences

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Tools for Data Enrichment

11. Thank you
for your time.
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