University of Michigan - Ann Arbor

Unstructured data either does not have a pre-defined data model or is not organized in a pre-defined manner. It has internal structure but is not structured via pre-defined data models or schema. Unstructured data may be textual or non-textual, and human- or machine-generated. It may also be stored within a non-relational database like NoSQL.

Unstructured Data

Structured data is comprised of clearly defined data types whose pattern makes them easily searchable. Data may be human or machine generated as long as the data is created within an RDBMS structure. This format is eminently searchable both with human generated queries and via algorithms using type of data and field names, such as alphabetical or numeric, currency or date.

Structured Data

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Examples of human-generated unstructured data:
- text files
- email
- social media
- websites
- mobile data
- media: audio files, video files, digital photos

Examples of machine-generated unstructured data:
- satellite imagery
- scientific data
- digital surveillance
- sensor data

Examples of Unstructured Data

A demographic dataset with statistics on different cities' population, GDP per capita, economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False?

Though classification algorithms succeed in ignoring background data for the sake of efficiency, most tasks require a complete understanding of input data, and are thus more computationally expensive. Modeling a distribution with a lookup table in the absence of other processing creates issues of memory and efficiency.
These issues can be minimized through identifying and only modeling direct - and not indirect - interactions between variables, i.e. Structured Modeling.

Challenges of Unstructured Modeling

Human-generated structured data:

- phone numbers
- SSNs
- zip codes
- name
- Customer data

Machine-generated structured data:

- Web metrics from Web logs
- Point-of-sale data


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