Facets of data (Data Science)

The main categories of data are these:

■ Structured

■ Unstructured

■ Natural language

■ Machine-generated

■ Graph-based

■ Audio, video, and images

■ Streaming.

Let’s explore these data types.

■ Structured data

Structured data is data that depends on a data model and resides in a fixed field within a record. As such, it’s often easy to store structured data in tables within Databases or Excel files.

SQL, or Structured Query Language, is the preferred way to manage and query data that resides in databases. You may also come across structured data that is difficult to store in a standard relational database. Hierarchical data such as a family tree is one such example

■ Unstructured data

Unstructured data is data that isn’t easy to fit into a data model because the content is context-specific or varying. One example of unstructured data is your regular Email. Although email contains structured elements such as the sender, title, and body text, it’s a challenge to find the number of people who have written an email complaint about a specific employee because so many ways exist to refer to a person, for example, The thousands of different languages and dialects out there complicate this even more.

■ Natural language

Natural language is a special type of unstructured data; it’s challenging to process because it requires knowledge of specific data science techniques and linguistics.

The natural language processing community has had success in entity recognition, topic recognition, summarization, text completion, and sentiment analysis, but models trained in one domain don’t generalize well to other domains. Even state-of-the-art techniques aren’t able to decipher the meaning of every piece of text. This shouldn’t be a surprise though: humans struggle with natural language as well. It’s ambiguous by nature. The concept of meaning itself is questionable here. Have two persons listen to the same conversation. Will they get the same meaning? The meaning of the same words can differ depending on whether they are spoken by someone who is sad or joyful.

■ Machine-generated data

Machine-generated data is information that’s automatically created by a computer process, application, or other machines without human intervention. Machine-generated data is becoming a major data resource and will continue to do so.

The analysis of machine data relies on highly scalable tools, due to its high volume and speed. Examples of machine data are web server logs, call detail records, network event logs, and telemetry.

■ Graph-based or network data

“Graph data” can be a confusing term because any data can be shown in a graph.“Graph” in this case points to mathematical graph theory. In graph theory, a graph is a mathematical structure to model pair-wise relationships between objects.

Graph or network data is, in short, data that focuses on the relationship or adjacency of objects. The graph structures use nodes, edges, and properties to represent and store graphical data. Graph-based data is a natural way to represent social networks, and its structure allows you to calculate specific metrics such as the influence of a person and the shortest path between two people.

Graph databases are used to store graph-based data and are queried with specialized query languages such as SPARQL. Graph data presents challenges, but analyzing additive and picture data can be even more difficult for a machine.

■ Audio, image, and video

Audio, image, and video are data types that pose specific challenges to a data scientist. Tasks that are simple for humans, such as recognizing things in pictures, are difficult for computers.

Multimedia data in the form of audio, video, images, and sensor signals have become an integral part of everyday life. Moreover, they have transformed product testing and evidence collection by providing different data sources for quantitative and systematic assessment.

■ Streaming data

While streaming data can take almost any of the previous forms, it has an extra property. The data flows into the system when an event happens instead of being loaded into a data store in a batch. Although this is not a distinct type of data, we treat it as such because you must modify your process to handle this type of information.

Examples are “What’s trending” on Twitter, live sporting or music events, and the stock market.