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Data source

As explained in Data for All

A data source is a system, observation, activity, or other origin from which data is obtained.

A data source is a system, observation, activity, or other origin from which data is obtained. A single source, such as location data, can reveal patterns in where people go and what they do. section 2.4.6

The value of data sources grows when organizations combine them. Multifaceted data can support analysis, modeling, prediction, prescription, simulation, and optimization of people, behaviors, situations, and markets. section 2.4.6 section 6.1

Sources may include:

  • Facts customers provide directly to companies. section 2.5
  • Macroeconomic and public-interest data, including crime, housing, unemployment, social assistance, food banks, charities, sales, and offers. section 2.5
  • Market and location data, such as traffic, population, demographics, competing facilities, pricing, and employment. section 6.1

Choosing a facility location

A site-selection decision for an urgent care clinic or electric-vehicle charging station can combine traffic, population, demographics, competitors, pricing, and employment. Earlier approaches relied mainly on traffic and population density; broader sources create a richer picture of potential locations. section 6.1

Multiple sources also create power imbalances. Companies can model situations involving individuals and their networks, then target offers toward company goals, which may conflict with a person's immediate needs. section 2.5

How Data for All covers it

How deep it goes
Traces the shift from single-source aggregation to integrated multifaceted data, without detailing collection or integration procedures.
Examples
Uses location data, customer facts, macroeconomic indicators, and facility site-selection variables as concrete examples.
What you can do after
Evaluate/understand how combining data sources produces richer models, targeted actions, and possible conflicts of interest.
How it connects
Connects data sources to analytics, data aggregation, data integration, modeling, prediction, and targeted offers.

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This explainer was generated by Manning's knowledge graph from the text of Data for All. The section links point to the parts of the book it draws on.