What Are the Different Types of Data?

Data is information of any kind. In the context of business and computing, we’ll deal (mostly) with information that’s in a machine-readable format. This is known as structured data.

Structured data

Structured data adheres to a pre-defined data model. This model describes how data is recorded, and it defines the attributes and provides information about the data type (e.g. name, date, number) and restrictions on their values (e.g. number of characters). This level of organisation means that data can be entered, stored, queried, or analysed by machines.

Structured data includes:

• names
• dates
• phone numbers
• currency or prices
• heights or weights
• latitude and longitude
• word count or file size of a document
• credit-card numbers
• product names or numbers
• transaction information.

You’ll often find structured data arranged in a tabular format (sometimes described as rectangular), where columns represent attributes (or variables) and each row represents a record. The intersection of a column and row (usually called a cell), contains the value (or observation) about that attribute for that record.

Unstructured and semi-structured data

Unlike structured data, unstructured data requires human interpretation. Consider a block of text. Computers can read each word, or sentence, but they can’t (yet) determine the meaning or tone of the text without human intervention. As you’ll discover later in the course, data scientists are trying to solve this problem with machine learning and other types of artificial intelligence.

Other examples of unstructured data include:

• images (human- and machine-generated)
• video files
• audio files
• social-media posts
• product reviews
• messages sent by SMS or through online services.

Some data, such as email, is considered to be semi-structured. Email headers contain metadata such as the date, language, and recipient’s email address, which are all structured data. But the email body, which contains your message, is unstructured.

Big data

The term ‘big data’ is used to describe large, complex data sets. Big data sets have been around since the 1960s; however, in the last 20 years there has been a considerable increase in the amount of data being driven, or made available, especially by large online services (YouTube, Netflix, Salesforce etc.). On top of this, the IoT is a new source of big data, as connected devices capture and collate data on customer use and product performance.

Big data has three key properties: volume, variety, and velocity.

Each of these three presents unique challenges.

1. Volume: Data sets contain vast quantities of information that put high demands on systems used for storing, manipulating, and processing the information.
2. Variety: Until recently, spreadsheets, text files, and databases were the main sources of data for most applications. The increase in big data has brought about a diversity in the type and structure of data being analysed. It’s common for systems to process data from many sources, including emails, images, video, audio, readings from IoT devices, and even scanned PDF documents. This variety can pose issues when storing data, extracting information (‘mining’), and for analysis.
3. Velocity: Vast quantities of data are being generated faster than ever, presenting challenges for analysts as more industries use this information. The ability to make instant decisions based on up-to-date information can make or break a business.