Topic 2: Variables and Data Flashcards
characteristic of members of a population
Variables
observations of variable
Data
contains variables and observations (arrows & columns)
Data sets
Data that can be measures with Numbers
Quantitative
Data that can be measure with Non-numerical data
Qualitative
Whole number that can’t be broken down
Discrete
number that can be broken down eg. Height
Continuous
Number with known difference between variables eg. time (No true zero)
Interval
Number that have measurable intervals where difference can be determined e.g height (Has True Zero)
Ratio
Data use for naming variables eg. Hair color
Nominal
Data used to describe order of values
Ordinal
(4) Consumer analytics data (ADBI)
Attitudinal; Descriptive; Behavioral; Interactive
DATA COLLECTION METHODS USED IN ANALYTICS (Sources of Data)
•Surveys
• Transactional Tracking (POS System)
• Interview and Focus groups
• Observation
• Online Tracking (Internet Cookies)
• Forms
collected directly from users by your organization (consumer data)
first-party data
Sources of first-party data include:
- Website or app behavior
- Email and newsletter subscribers
- Lead generation campaigns
- Surveys
- Social media
- Subscriptions
- Customer feedback
- Customer service/sales conversations
- Online chat
Benefits of First-party data
- Personalization and integration
- Target the right customers
- Accuracy and control
- Strengthen customer relationships
- Compliance with privacy laws (GDPR & DPA)
- Lower cost
Six effective ways to use your first-party data (consumer data) -SPELLA
Social Media; Paid ads; Email; Loyalty programs; Landing Pages; Account based marketing
data shared by another organization about its customers (or its first-party data)
Second-party data
data that’s been aggregated and rented or sold by organizations that don’t have a connection to your company or users
third-party data
Benefits of Second-party data
-It enables you to scale by connecting with new audiences that match your own audience data.
- Combine it with your first-party data to build improved predictive models.
- You can develop better audience insights by analyzing a more extensive audience group.
Data Quality (VACCU)
Validity; Accuracy; Completeness; Consistency; Uniformity
Ensure your data is close to the true values.
accuracy
If it measures what it is supposed to measure
validity
The degree to which all required
data is known.
completeness
Ensure your data is consistent within the same dataset and/or across multiple data sets.
consistency
The degree to which the data is specified using the same unit of measure.
uniformity
(4) Data Preparation
Data cleansing; Scripting; Data transformation; Data warehousing
is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
Data cleansing
A script is a series of Analytics commands that are executed sequentially and used to automate work within
Scripting
ETL process (Extract, Load, Transform). or to extract data from a source, convert it into a usable format, and deliver it to a destination.
Data transformation
a central repository of information that can be analyzed to make more informed decisions
Data warehousing
Data Life Cycle (6)
Create-Store-Use-Share-Archive-Destroy
Data that cannot be counted, measured or easily expressed using numbers
Qualitative
Information a company collects directly from its customers and owns
First-party data
The process of cleaning and transforming raw data prior to processing and analysis.
Data preparation
Any information collected by an entity that does not have a direct relationship with the user the data is being collected on.
Third-party data
Indicates how reliable a given dataset is across key dimensions like completeness, consistency, accuracy, and more
Data quality
Information all about consumers, from their household, demographic and lifestyle details to their behavior and buying
(Consumer analytics data)
A numerical type of data that includes whole, concrete numbers with specific and fixed data values determined by counting.
Discrete
Ensure your data is consistent within the same dataset and/or across multiple data sets.
Consistency
Data that can take any value (Infinite numbers)
Continuous
Data that can be counted or measured in numerical values.
Quantitative
