Four Sources of Domestic Data for Analytics and Analysis

Original article was published by Sarah Mason on Artificial Intelligence on Medium


Four Sources of Domestic Data for Analytics and Analysis

Photo by Breno Assis on Unsplash

There are many datasets publicly available online. I search for different terms combined with the word “dataset” to view available datasets out of interest. When it comes to big data and data, there is a stereotype that value is in scientific and advanced technology. There are many uses for big data and analytics for finance and marketing. Understanding patterns and behavior for consumer choice, customer demographics, and influences is important for value creation in goods and services, profit and loss. This information can be found in domestic data, or household information, that is a broad category with data about people and living.

“The improvement of the understanding is for two ends; first, for our own increase of knowledge; secondly, to enable us to deliver and make out that knowledge to others.” John Locke

Why?

Turning data into an assessment for proposals to increasing or sustaining returns requires analysis of variables for a pattern or trend. Value creation is in determining profit and loss for finance, and benefits for customers. This requires data for descriptive, predictive, prescriptive analysis to obtain this goal.

Marketing

Value Creation in marketing requires data to examine and determine choice. What does a customer want? Why does a customer want a product or service? These two questions can become different after data based analysis. How do I create a product or service of value to a customer? How do I define and target my customer?

Finance

Profit and loss often use data to determine trends and determine business strategy. This is a classic use of data in finance.

How Household Data is Used

Photo by Chris Barbalis on Unsplash

Example 1:

Analyzing a dataset of usage of disposable cups and plates by household statistics and geographical data is an example of using public household datasets to determine and learn behavior of people in relationship to using plastic dining ware. Is this important? It is if this information can generate value by providing insights to consumer behavior and choice to target consumers who are likely to buy plastic cups or to convince the demographic to avoid disposables. The value are informed business decisions that are successful.

Photo by Juliane Liebermann on Unsplash

Example 2:

Family size statistics help understand what people are like or determine demographics for forecasting sales or predict consumer response to advertisement. Value Creation is in understanding consumer behavior through data analysis to use regression or clustering for insight to build a model to improve or create product.

Four Household Data Sources

Statista (www.statista.com)

Household Cleaners https://www.statista.com/statistics/939718/household-cleaner-sales-by-product-type-us/

Disposable or eco savvy https://www.statista.com/statistics/275467/us-households-usage-of-disposable-cups-and-plates/

UN (data.un.org)

Global stastics on family size http://data.un.org/Data.aspx?d=POP&f=tableCode:50

Knoema (knoema.com)

How to buy fruit globally https://knoema.com/atlas/topics/Fruits/datasets

Data of the World (data.world/datasets)

Housing types https://data.world/datasets/household