What is Data, What is Information? Here’s The Difference?
The terms ‘information’ and ‘data’ (datum in the singular) are often used in the same context, even though they have different meanings. An isolated datum is just a character or symbol. A collection of information and data and the combination of languages, graphics, images and gestures increase the information density of news, statements, claims etc. It is semantics that gives meaning to data and turns it into information. This describes the part of a message that is valuable to the recipient. Recipients can be persons or companies. Companies use the internet and information technology (IT) to connect data sets and link information (data processing) to customers (see e-commerce).
Editorial comment: The characters/symbols mentioned above are values/numerical values resulting from observations, statistical surveys and technical measurements.
What types of information are there?
Nowadays, we strive for individual semantics to provide the necessary degree of information (or information content). We differentiate between perfect and imperfect information. The less perfect the degree of information, the harder it is to make a decision based on the information.
We focus on the following types of information*:
Factual Information – This relates exclusively to true events and is generally verifiable (scientific statements on the climate, the risk of contracting Covid).
Prognostic Information – This is information that allows estimates to be made about future events (elections).
Normative Information – This represents an opinion-based conclusion as to what would be desirable. For example: “We should do more trade with China because...”.
Explanatory Information – This is information that explains/interprets content and elaborates on the ‘why’ (scientific papers, expert opinions).
Conjunctive Information – This contains neutral/indirect considerations that usually leave room for interpretation/speculation (political statements, economic statements).
Logical Information – This limits the amount of detail required to generate an understanding and allow coherent conclusions to be drawn. The more details I receive about a question, the more confident and detailed my answer becomes. For example, if I receive the information that an animal can swim, as the recipient of the information I may initially assume that it is a fish. But if we add more details to the existing information, for instance, that it is a whale, new conclusions can be drawn: This is a mammal that can swim but resurfaces regularly to breathe.
*Source (in German): Wirtschaftslexikon, Ralf Capurro / Der Informationsbegriff in der Informationswirtschaft / Chapter 1
Facts – Information Becomes Knowledge
To deduce concrete knowledge from bits of information, these need to be structured hierarchically. Example: A datum leads to information, which may lead to knowledge. The knowledge serves different goals. More knowledge of customers leads to more personalised communication (see information and e-commerce). More detailed knowledge can only be gained from combining several pieces of information, ideally different types of information. Big data and smart data are closely linked to this concept. Simply put, big data usually refers to large volumes of unconnected data, smart data to the knowledge generated from it for human use.
Information and E-commerce
Without structured information, there would be no digital transformation. Online retail, or e-commerce, in particular, is not sustainable for companies without structured customer information/relationships. What in a PIM system is referred to as the ‘golden record’ would be the ‘golden profile’ in e-commerce. Both ideas revolve around the perfect, most complete data set. To monitor stock levels, create personalised offers and have a complete view of customers, data from various systems (see omnichannel) needs to be stored, analysed and merged in a central location. Outdated, incomplete or faulty data sets make it harder to sell in digital markets.
E-commerce – Example
One datum might be ‘26101980’, but the sequence of numbers alone does not tell us much.
Add more context to this datum, for instance, that it indicates a date of birth, and it becomes information (date of birth 26-10-1980).
This information is still not very helpful. The more information you add, the more accurate your knowledge becomes, for instance, about a person or situation.
If you add a personalised email address (john.smith@example.com), this date of birth becomes knowledge linked to a person.
If you add more types of information, such as prognostic information, specific probabilities can be calculated for John Smith. How likely is it, for example, that John Smith will order something from the online shop in the next seven days? The online retailer can then induce John Smith to make a purchase, based on the purchasing behaviour on the shop platform and in a personalised form – for instance, by providing a personalised discount voucher.
Based on knowledge alone and with the help of a linked, structured, flawless database, the data can help you make the right decisions.
Information in the Age of Data Privacy/GDPR
In economic terms, information has always been a major asset. Since 2018, however, personal data has enjoyed special status in the European Union (EU). This is when the General Data Protection Regulation (GDPR) was introduced. Across Europe and even beyond, it protects individual users and their personal data from misuse and ensures their right to privacy.
Personal Data
The General Data Protection Regulation protects personal data that is used to identify individual persons. The GDPR deliberately excludes the type of data processing: According to the EU, the regulation is “technology-neutral and applies to both automated and manual data processing”. The downstream type of data storage is also not relevant – regardless of whether the data is stored “within an IT system, by means of video surveillance or on paper”.
Examples of Personal Data
First name, last name
Private address
Personal details (date of birth, gender, nationality)
Personalised email address (containing the full name)
ID card number
Social Security number
Location data (GPS)
IP address
Cookies (usually non-essential cookies)
Advertising IDs of smartphones
Personal hospital or medical records
Property
Online customer data (e-commerce, ERP)
Banking data
Health data
Religious beliefs
Editorial comment: The transfer of personal data from the European Union to so-called secure third countries already required a legal basis before the GDPR. A company must ensure that a transfer of personalised information is in line with the general reasoning of the GDPR – including the necessary data processing.
Exception: The USA is not a secure third country. Investigative authorities and intelligence services based there retrieve personal data from US and EU citizens with few bureaucratic requirements. The latter is prohibited under the GDPR. And the Privacy Shield adequacy decision that was previously negotiated is no longer valid. Since 4 June 2021, specific standard contractual clauses provided by the EU Commission as a template have been applied. With regard to the US companies Microsoft, Facebook, Apple and Amazon, however, data protection authorities doubt that the level of protection is as promised. This refers in particular to US services hosted in the cloud.
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