Data, Information and Fit for Purpose

Data, Information and Knowledge

When we talk about what computers work with, three terms come up again and again: data, information and knowledge. It is easy to muddle them, but they mean distinct things. Data consists of raw facts and figures. On their own they carry no meaning: the number 37 could be a temperature, a bus route or someone's age. Information is what we get when data has been processed by a computer to give it context — 37°C tells us it is a dangerously high body temperature. Knowledge takes things one step further: it is derived from information by applying rules. A doctor who knows that a temperature of 38°C or higher in a baby under 3 months needs urgent medical advice is applying knowledge — rules built on clinical information.

This hierarchy matters in ICT because computers process data to produce information, and well-designed systems encode the rules that turn information into actionable knowledge. When you type your date of birth into a form, you are providing raw data. The system processes it to tell you that you are 16 years old (information), and then applies a rule to decide whether you are old enough to create an account (knowledge in action).

Why Data Must Be Good Quality

For information to be useful, the data that feeds it must be fit for purpose. Good-quality data has several key characteristics. It must be accurate — if a patient's medication dosage is recorded incorrectly, the information produced from it could be dangerous. It must be complete — missing fields can make records unusable. It must be up to date — a customer address that was correct three years ago may have changed. It must also be relevant — collecting data that is never used wastes storage and processing resources. Poor-quality data leads to poor-quality information, which in turn leads to bad decisions. This is sometimes summed up as "garbage in, garbage out": if the raw data going into a system is wrong, no amount of processing will fix the output.

Encoding Data

Encoding is the process of converting data into a shorter or standardised form. For example, instead of storing the full word "Female", a database might store the single character "F". Instead of "Yes" or "No", a system might use 1 and 0. Encoding has clear benefits: it reduces the amount of storage space needed, speeds up sorting and searching because shorter codes are faster to compare, and reduces the chance of input errors because staff choose from a fixed set of codes rather than typing free text. However, encoding can also have disadvantages — codes may be misunderstood or misremembered, and they can make data harder for a non-expert to read without a reference key. There is also a risk that a code designed for one purpose becomes ambiguous over time as organisations grow and change.

Advances in ICT hardware have improved both the speed of access to data and the amount of storage available. Solid-state drives (SSDs) retrieve data far faster than older spinning hard drives. Cloud storage means an organisation is no longer limited by the physical capacity of the hardware on site. These improvements mean that organisations can store larger volumes of data and retrieve it more quickly, which directly improves the quality and timeliness of the information they produce.

Advantages and Disadvantages of Using ICT to Store Data

Storing data using ICT systems offers significant advantages. Large volumes of data can be searched in seconds — a task that would take days in a paper-based system. Data can be backed up automatically to prevent loss. Multiple authorised users can access the same records simultaneously from different locations. Data can be sorted, filtered and processed to create reports almost instantly. Despite these advantages, there are also real risks and drawbacks. Digital data is vulnerable to hacking, malware and unauthorised access. Hardware failure or software corruption can destroy records if backups are not in place. There are ongoing costs for hardware, software licences and security measures. Organisations also have legal obligations under data protection law when handling personal information digitally, and a breach can have serious financial and reputational consequences.

File Types and File Properties

Not all files are the same. Different types of data are stored in different file formats. Common file types include: text files (.txt, .docx) for written documents; image files (.jpg, .png, .bmp) for photographs and graphics; audio files (.mp3, .wav) for sound; video files (.mp4, .avi) for moving images; and spreadsheet files (.xlsx, .csv) for numerical data. Each file type has different properties and is suited to different purposes. Choosing the wrong file type can reduce quality or cause compatibility problems.

Every file also has properties that describe it: its name, file type (extension), size (measured in bytes, kilobytes, megabytes or gigabytes), date created, date modified, and the author or owner. File size is particularly important in ICT because large files take longer to transfer over a network, require more storage space, and can slow down a system. Understanding file properties helps ICT users manage storage efficiently and choose the right format for their needs.

Data Compression

Data compression is the process of reducing a file's size. There are two main types. Lossless compression reduces file size without losing any data — when the file is decompressed it is identical to the original. ZIP files and PNG images use lossless compression. Lossy compression permanently removes data that is considered less important — the file is smaller but cannot be perfectly restored to its original state. JPEG images and MP3 audio files use lossy compression. The benefit of compression is faster file transfer over networks and less storage space used. The trade-off with lossy compression is reduced quality — though for many uses (streaming music, sharing photos online) the quality loss is not noticeable to most people.