1.1–1.2 Data, Information and Knowledge, and Big Data GapFill

We often use the terms ‘data’ and ‘information’ interchangeably. This is  correcttruepartially trueincorrect. They are linked, but they are not the same.

 InformationStructureDataBias can be meaningless at first – values without context often described as ‘raw facts’. It can include numbers, text, phrases and characters, in columns, strings or tables, which have been collected.

However, when we ‘process’ that data, we give it meaning, through structure and  contextdatainformationmeaning – the data becomes  mobiledata pointsirrelevantinformation. 

Information can be further studied and processed to gain understanding – this is known as   knowledgeinformationdatameaning.

These three elements can come from a variety of different  sourcesformulaspoliciesrules. For example, a user's search history, a checkout in a supermarket, and sensors on a machine all produce data. Information can come from places like the Internet, but can also come from non-computer-based sources such as books or spoken word.

Organisations can use data to improve services, make informed  dataknowledgeconsequencesdecisions, and gain a competitive advantage. Individuals can use data for many things, such as navigation, banking, entertainment, and purchasing goods and services.

However, collecting large amounts of data brings some limitations. Organisations must ensure they keep data secure, and some people have   culturalprivacyregularmoral concerns about how their data is stored and used.

The term   knowledgelarge databig datahuge data is used to describe a very large amount of data. This can come from a variety of sources, such as   a photographa diarya booksocial media, machine sensors, and retail transaction data.

It can be used by companies to identify trends and   analyticsadvantagespatternsprocessing, and ultimately enable them to make better decisions. It could be used to make better recommendations to customers, to detect fraud or suspicious activity, or to manage a company's stock or their vehicle fleet.

Using big data can help companies to better understand their customer base or identify links between data they collect that they were previously unaware of. Collecting a massive amount of data can be expensive. The theft of big data could have disastrous consequences, so the data must be stored  without a passwordon paperin the cloudsecurely. Finally, when collecting a large amount of data, there can be concerns about   coolingaccuracyarchivaldeletion, as it would be very time-consuming, or perhaps impossible, to check it all.