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This document presents an overview of the concepts of 'data and 'information', in a form intended to be directly relevant to the practice of information systems (IS), and to IS researchers who intend their research to be relevant to IS practice. For completeness, an outline of the philosophical underpinnings is provided as an Appendix.
Data is any symbol, sign or measure that is in a form accessible to a person or an artefact. Technically, 'data' is a plural, with the singular form being 'datum'. But it's mostly used as if it were a singular noun, or perhaps a generic noun. Hence it's usual to say either 'the data is stored' or 'data is stored', rather than '(the) data are stored'.
In the Real World, Things exist, and Events happen. Things and Events have Properties. Humans and artefacts can't directly 'know' those Properties, but they can sense them. So Data doesn't exist in the Real World, but in an abstract, Information World.
Data can be recorded against a number of different Measurement Scales. The Data may be rather vague, such as 'a pile of' iron ore, or 'some' logs. That's referred to as Data on a Nominal Scale, because all that the Data comprises is a name. Data can convey greater precision by using words that bring some order to knowledge and allow comparisons to be made. Data such as 'a small' amount, 'a moderate' amount, and 'a large' amount is on what's called an Ordinal Scale.
Data is more useful still if the space between the meanings of successive words is the same, in which case the Data is on an Interval Scale. In the Celsius temperature scale, the size of each degree is the same, so it's meaningful to say things like 'there was a 10-degree drop in temperature when the storm-front came through'. On the other hand, it's not sensible to say '20 degrees C is twice as hot as 10 degrees C'. That's because multiplication and division don't apply unless the Data is on what's called a Ratio Scale.
A Ratio Scale requires 'a natural zero'. The Kelvin temperature scale has a natural zero, at 0 degrees K - which corresponds to -273.16 degrees C. That's because something at that temperature contains no heat. So it's correct to say that '546.32 degrees K is twice as hot as 273.16 degrees K', i.e. seriously hot water-steam contains twice the amount of heat as the same amount of water-ice. (The following statement is also correct: '273.16 degrees C is twice as hot as 0 degrees C').
An example of Ratio Scale Data that's useful in business is 100 tonnes of clean river gravel, which is twice as much as 50 tonnes. It's common for researchers to make an assumption that the gaps between, say, 'very small, small, average, big, very big' are all the same. That enables them to use powerful statistical tools on their survey data — maybe legitimately, maybe not.
The term Empirical Data refers to Data that is intended to represent a Property in the Real World. On the other hand, Synthetic Data is Data that bears no direct relationship to any Real-World phenomenon. Examples of Synthetic Data include the output from a random-number generator, or data created as a means of testing the performance of software under varying conditions. A special case of Synthetic Data is data generated from Empirical Data by some perturbation or substitution process.
When Empirical Data comes into existence, terms like Data Capture and Data Collection are often used. It's far preferable, however, to think of the process as Data Creation. That's because Data is generally created by humans or artefacts. Some kind of human or artefact sensor may be used as a basis for the act of creation, such as human eyes or a trip-wire counting items passing along a conveyor belt, or a human eye or an analogue-digital converter (ADC) recording the temperature, or a human ear or a sound-sensor being used to create data to represent the pitch of a note. All such mechanisms are error-prone, and hence the quality of data varies, depending on, for example, the tiredness and concentration of the human, the cleanliness of the equipment and perhaps the recency of the equipment's calibration against an authoritative scale.
The vast majority of Properties of Real-World Things and Events do not give rise to Data. The background noise emanating from all points of the universe has been ignored for millions of years, although radio-astronomers now sample a tiny amount of it. In a business that uses trucks to carry goods in and out of a company's gates, it's valuable to create Data that represents some Properties but not others. Which truck carried what in, and what out, and when, is potentially useful, e.g. to invoice customers, and to detect theft. But the company seldom has any motivation to measure, let alone record, the pressure in the tyres on the trucks, the number of chip-marks in the paintwork, the condition of the engine-valves, or perhaps even the number of consecutive hours the driver has been at the wheel.
Of the Real-World Things and Events for which Data is created, many kinds are very uninteresting. The streams of background noise emanating from various parts of the sky might on occasions contain a signal from a projectile launched from the earth, and just possibly might contain some pattern from which an Event, sometime, somewhere, can be inferred. Usually, however, the Data is devoid of any value to anyone. Similarly, a great deal of the Data stored by commerce, industry and government is of interest for only a very short time, or 'just for the record' and kept only for contingencies, or because it was easier or cheaper to keep it than delete it.
In the Abstract World in which IS operate, a Property of a Real-World Thing or Event is represented by a Data-Item, which is a storage-location in which a discrete Data-Item-Value can be represented. A Data-Item-Value is the content stored in a particular instance of a particular Data-Item. For example, Properties of cargo-containers relevant to running a cargo business might be expressed as Data-Items and Data-Item-Values of Colour = Orange, Owner = MSK (indicating Danish shipping-line Maersk), Type = Half-Height, Freight-Status = Empty. The set of valid Data-Item-Values that can appear in any particular Data-Item is referred to as the Domain on which it is defined.
A Record is a collection of Data-Items that refers to a single Real-World Thing or Event. A collection of similar Records may be referred to as a File or Data-Set. A collection of Files or Data-Sets may be a called a Database. A common way to visualise a File is as a two-dimensional data-table. Each row represents a Record. Each column represents a particular Data-Item. The cell at each intersection between rows (Records) and columns (Data-Items) is able to store a discrete Data-Item-Value.
The term Record-Key refers any one or more Data-Items held in a Record whose value(s), alone or in combination, are sufficient to distinguish that Record from other Records. It may be a purpose-designed Data-Item such as a container-ID or customer-number; or it may be a combination of Data-Items (such as a customer's given-name, surname, street-name and postcode).
The term Metadata refers to Data that describes some attribute of other Data. Examples are the date-of-creation, date-of-most-recent-amendment, document-identifier from which it was taken and/or the username that created it. Metadata may be explicitly expressed or captured, by cataloguers; or it may be automatically generated, i.e. inferred by software. It may be stored with the Data to which it relates, or stored separately.
The term Storage-Medium refers to a Real-World Object whose purpose is to store Data. Examples include sheets of paper or card, books (in the sense of paper-sheets bound into volumes or codices), punched-cards, punched-paper-tape, magnetic-tape or -strip, microfilm, magnetic hard disk devices (HDDs), optical disks, solid-state devices (SSDs, e.g. computer-memory, chips in plastic cards, and 'USB sticks' / 'thumb-drives'), and plastic cards (which carry print, embossing, magnetic stripes and chips).
The term Content is a collective word for Data, in particular for that Data which is included within a particular instance of a Storage-Medium (e.g. all of the Data on the magnetic stripe in a passport). It is commonly used where the Data represents audio, image or video.
The term Data-Format refers to a technical specification for the recording of Data on a Storage-Medium. Many different standards exist, each designed for a particular kind of Content and/or Medium.
The various forms of human-readable Storage-Media (paper, vellum, etc.) are still much-used for Text, by which is meant Data in a Data-Format intended to convey a natural language. These are mostly sets of glyphs (i.e. visual shapes) that make up alphabets, supplemented by digits and punctuation-marks. In addition to alphabets, however, there are syllabaries (such as those for cuneiform, hiragana, katakana and Cherokee) and sets of logographic characters, i.e. pictograms and ideograms (such as hieroglyphs, 'Chinese characters', Japanese kanji and Korean hanja).
The various forms of machine-readable Storage-Media (particularly magnetic, optic and solid-state storage devices) support various Data-Formats intended for particular kinds of Data, including:
The term 'information' is used in many different ways. Frequently, it is used without clarity as to its meaning, and often in a manner interchangeable with Data. One particular use arises from a theory of communication, in which information is used to refer to a measure of the quantity of data in a message (Shannon & Weaver 1949). This has been valuable in applying electromagnetic signals to the function of transferring Data from one location to another; but it is not the meaning of Information as the term is used in IS.
A definition of Information provided at an early stage of the IS discipline is "data that has been processed into a form that is meaningful to the recipient and is of real or perceived value in current or prospective decisions" (Davis 1974, p.32). A simpler form is more effective, however: Information is Data that makes a difference, or Data that has value, or Data that has Relevance in a particular Context. Context refers to the prevailing circumstances, or in decision theory a collection of settings of environmental variables (Miller & Starr 1967). Relevance of particular Data refers to the capacity of that Data to affect a particular Context. Until it is in an appropriate Context, Data is not Information, and once it ceases to be in such a Context, Data ceases to be Information.
A common Context in which Data has value is when it has Relevance to a Decision. A Decision is a commitment to a course of action. The act of making a commitment is commonly preceded by a trigger of some kind, which stimulates the gathering of Information, the generation of one or more alternative courses of action, the use of that Information to evaluate the alternative(s), and the application of decision criteria in order to adopt or reject the course of action being considered, or make a choice among two or more alternatives.
The Relevance of the weather to a person depends on whether that person has an interest in the conditions outside a building, such as when the person is considering going out there, or if they want to plant seeds or harvest crops. Data about the delivery of a particular batch of baby-food to a particular supermarket is lost in the bowels of the company's Database, never to come to light again, unless and until something exceptional happens, such as the invoice being disputed, the customer complaining about short delivery or poor product quality, or an extortionist claiming that poison has been added to some of the bottles.
The question as to which Data is Relevant to a particular Context is not always clear-cut. On a narrow interpretation, Data is relevant and of value only if it actually, in practice, makes a difference. This is equivalent to putting the focus on the particular Data-Item-Value (i.e. what is stored in one particular cell). A broader interpretation is that Data is relevant and therefore of value if, depending on whether or not it is available, it could, in principle make a difference. In this case, the focus is on the Data-Item (i.e. the whole column). A loan applicant's current level of debt is in principle relevant to a decision about a new loan; but in practice it is not relevant if the applicant's current level of debt is low, or is low given that much of the existing debt is on a principal-only repayment arrangement for the duration of the new loan being sought.
In addition to decision-making, there are other kinds of Context in which Data can be interesting or valuable. When we read text, listen to audio, or watch 'infotainment' programs, we are seldom making decisions, and yet we perceive informational value in some of the Data presented to us. Sometimes it is merely humorous. Humour is a visceral reaction to an unexpected clash between two lines of logic or frames of reference, or a sudden realisation that an important item of Information is problematically ambiguous (Koestler 1964).
Humour is a special case of Information that is 'Data that has Surprisal Value', because the Data-Item-Value is not what was expected. ('A training-session injury will keep the star player out of the Grand Final!', 'You wouldn't believe it: Putin has been stupid enough to launch an attack on the Ukraine'). This is a form of Refutative Information, i.e. evidence that contradicts our tentative judgement or opinion. Alternatively, Data may have value because it is Confirmatory Information, i.e. it is evidence reinforcing our tentative judgement or opinion. In other cases, it may be something that fits into a pattern of thought we have been quietly and perhaps only semi-consciously developing for some time, and which seems, for no very clear reason, to be worth filing away.
Data may be of good or bad quality, and many factors are involved. The quality of Information is an even more complex issue, because further factors come into play. A framework for assessing data quality is presented in Clarke (2016a). It draws on a range of sources, importantly Huh et al. (1990), Wang & Strong (1996), Mueller & Freytag (2003) and Piprani & Ernst (2008). Table 1 distinguishes two groups of quality factors. The first group of seven Data Quality factors can be assessed at the time of data acquisition and subsequently. The second group of six Information Quality factors, on the other hand, can only be evaluated at the time of use.
Adaptation of Table 1 of Clarke (2016a)
A range of contexts is investigated in Clarke (2016a) in which low Data Quality and/or low Information Quality is likely to lead to inferences that are unreliable or simply wrong and potentially very harmful. Where inadequate Quality intersects with inadequate clarity about the meaning of a Data-Item or a Data-Item-Value, the likelihood of error and harm increases (Clarke 2016b).
In the cases of Data in image, video and audio formats, the scope for abuse has exploded during the digital era. In the case of textual data, complex patterns arise, as evidenced by the prevalence of propaganda, misinformation, rumour-mongering, 'false news', 'alternative facts', 'fact checkers', 'explainers' and most recently AI-based authorship.
Two further commonly-used terms are usefully distinguished from Data and Information. Sometimes a depiction if provided showing a simple pyramid. See the representation in Figure 1. It shows large volumes of Data forming the base layer, smaller volumes of Information at the second-lowest layer, a slimmer, second-highest layer called Knowledge, and a layer at the peak called Wisdom. Such representations of Knowledge and Wisdom are not merely simple-minded but also dangerous (Weinberger 2010).
The pragmatic, useful approach is to recognise that two different, mainstream interpretations of Knowledge co-exist:
For example, an omelette recipe is Codified Knowledge. On the other hand, the expertise to interpret the recipe, to apply known techniques and tools to the activity, to recognise omissions and exceptions, to deliver a superb omelette every time, to sense which variants will work and which won't, and to deliver with style, are all examples of Tacit Knowledge. IS professionals recognise the value of applying both sets of ideas.
Wisdom is on an entirely different plane from Data, from Information, from Codified Knowledge and even from Tacit Knowledge. To the extent that Wisdom exists, it is most usefully thought of as being one of the following:
A System is a set of interacting elements that together gives rise to behaviour that is valuably different from the behaviour of the elements individually. More specifically, an Information System (IS) is a set of interacting elements that performs one or more functions involving the handling of Data and Information, including data creation, editing, processing and storage; and information selection, filtering, aggregration, presentation and use. See Checkland (1981). The elements in IS commonly comprise both humans and artefacts, with designed-for-the-purpose artefacts referred to as Information Technology (IT).
The IS profession and the IS discipline are concerned with systematic data creation and information production, communication and use, and their management, impacts and implications. Mostly, they are concerned with systems operating within and between organisations; but the field also encompasses systems involving only individuals.
A proportion of IS can be observed from a technical system viewpoint. Such IS include highly-automated production lines; automated control systems for the flow of water and other liquids; and the low-level, automated processes that keep aircraft attitude stable and keep ships 'on an even keel'. In these cases, it is natural for the primary focus to be on IT.
With most IS, however, it is futile to think primarily about IT, and essential to keep both both the human and artefact components and processes in view at all times, with an emphasis on their interactions. This is the socio-technical view of systems (Abbas & Michael 2022).
The purpose of this document has been to explain the fundamental notions underlying the practice of Information Systems. The notions have been discussed at sufficient depth that they provide a basic intellectual tool-kit to support understanding, analysis, design and operation of IS, despite the enormous range, scale and scope of systems that exist after six decades of maturation of the profession. Further concepts that build on these foundations relate to such matters as the authentication of assertions, and the handling of the multiple identities adopted by artefacts, and by human and organisational entities.
The purpose of this document is to provide a basis for effective IS practice, together with instrumentalist forms of IS research intended to support IS practice. To achieve that aim, a particular philosophical viewpoint or worldview has been adopted. In intellectual terms, I'm bringing a particular set of 'meta-theoretic commitments' to the work.
That worldview is pragmatic, in the sense that it is concerned with understanding phenomena and supporting action. It takes as a given that there is a reality, outside the human mind, where things exist and events occur. (In the branch of philosophy called ontology, concerned with the study of existence, this assumption is referred to as 'realism').
However, humans cannot directly know or capture those things or events. They can sense and measure things, they can create data reflecting things, and they can construct an internalised model of things. (In ontological terms, this assumption is referred to as 'idealism').
The pragmatic view adopted here blends the two notions of realism and idealism, by adopting a compromise (along the lines of 'Cartesian dualism'). Phenomena and their properties (such as the wavelength of electromagnetic radiation, hardness and brittleness of things, and event-duration) inhabit the Real-World; whereas ideas (such as numbers, colours, hardness, brittleness and time, and lists of the intended functions of artefacts) are of the Abstract-World. See Weber (1997).
A pragmatic approach to IS has to work not only in contexts that are simple, stable and uncontroversial, but also where there is no expressible, singular, uncontested 'truth'. So a truce is also declared between the view that knowledge is derived from sensory experience (in the branch of philosophy called epistemology, concerned with the study of knowledge, this position is referred to as 'empiricism'), and an alternative view that knowledge can be innate and/or derived from the human faculty of reason (in epistemological terms, 'apriorism' or 'rationalism'). Hence the ideas of Codified Knowledge (which is empiricist) and Tacit Knowledge (which is apriorist) can co-exist. See, for example, Mingers (2001), Becker & Niehaves (2007) and Myers (2018).
In Figure A1, an overview is provided of the 'pragmatic metatheoretic model' on which the presentation is built. The Real World features phenomena referred to as Things and Events, each of which has Properties. These are modelled conceptually as Entities and Transactions, each of which has Attributes. In order to support IS practice, the Entities and Transactions need to be operationalised as Data. (The discussion in this document intentionally skips the Conceptual Model, because its primary uses are during the design of IS, whereas this document is mainly concerned with the operational use of IS). The term Record-Key is applied to those Data-Items that are useful for distinguishing between Records within a Data-Set.
This basic model is extended in further papers, which have a particular focus on identity management issues. See Clarke (2021, 2022, 2023a, 2023b).
The extensions begin by distinguishing Physical Things (which have corporeal existence, such as inanimate objects and artefacts, active artefacts such as computing devices and robots, animals, and people) from Virtual Things (which do not, such as processes running in devices, 'legal persons' such as corporations, and roles played by individual human Entities).
In Figure A2, the correlates in the Abstract World are shown. A Physical Thing is modelled by an Entity, and a Virtual Thing by an Identity. These have relationships, which can be complex, including in relation to their cardinality and the question of whether the relationship is known, or unknowable, or currently unknown but knowable.
At the Data-Model level, the Record-Key for an Identity is an Identifier (such as a process-id or a customer-code), and that for an Entity is an Entifier (such as a device-id or an animal or human biometric). The process of Identification involves the gathering of an Identifier, and the process of Entification involves the collection of an Entifier. A more precise descriptor for an Identifier is appropriate in circumstances in which the mapping to an Entity is unknowable (for which Anonym is suitable), or where the mapping is in principle knowable, but not currently known (which is a Pseudonym).
Some of the ideas in this document are of very long standing. Some are found in introductory IS text-books, but it is unusual to find a systematic treatment of all of them. This document is intended to draw together all of the fundamental notions, and present them in an accessible form. The paper is accordingly lightly referenced, rather than being overloaded with a comprehensive suite of citations.
Abbas R. & Michael K. (2022) 'Socio-Technical Theory: A review' In S. Papagiannidis (Ed), 'TheoryHub Book', TheoryHub, 2022, at https://open.ncl.ac.uk/theories/9/socio-technical-theory/
Becker J. & Niehaves B. (2007) 'Epistemological perspectives on IS research: a framework for analysing and systematizing epistemological assumptions' Information Systems Journal 17, 2 (2007) 197-214
Checkland P. (1981) 'Systems Thinking, Systems Practice' Wiley, Chichester, 1981
Clarke R. (1990) 'Information Systems: The Scope of the Domain' Xamax Consultancy Pty Ltd, January 1990, at http://rogerclarke.com/SOS/ISDefn.html
Clarke R. (1992a) 'Fundamentals of Information Systems' Xamax Consultancy Pty Ltd, September 1992, at http://rogerclarke.com/SOS/ISFundas.html
Clarke R. (1992b) 'Knowledge' Xamax Consultancy Pty Ltd, September 1992, at http://rogerclarke.com/SOS/Know.html
Clarke R. (2010) 'A Sufficiently Rich Model of (Id)entity, Authentication and Authorisation' Xamax Consultancy Pty Ltd, February 2010, earlier version presented at the 2nd Multidisciplinary Workshop on Identity in the Information Society (IDIS 2009), LSE, London, 5 June 2009, at http://www.rogerclarke.com/ID/IdModel-090605.html
Clarke R. (2016a) 'Big Data, Big Risks' 'Information Systems Journal 26, 1 (January 2016) 77-90, PrePrint at http://www.rogerclarke.com/EC/BDBR.html
Clarke R. (2016b) 'Quality Assurance for Security Applications of Big Data' Proc. EISIC'16, Uppsala, 17-19 August 2016, PrePrint at http://www.rogerclarke.com/EC/BDQAS.html
Clarke R. (2021) 'A Platform for a Pragmatic Metatheoretic Model for Information Systems Practice and Research' Proc. Australasian Conf. Infor. Syst., December 2021, PrePrint at http://rogerclarke.com/ID/PMM.html
Clarke R. (2022) 'A Reconsideration of the Foundations of Identity Management' Proc. 35th Bled eConf., Slovenia, June 2022, pp.1-30, PrePrint at http://rogerclarke.com/ID/IDM-Bled.html
Clarke R. (2023a) 'A Generic Theory of Authentication to Support IS Practice and Research' Xamax Consultancy Pty Ltd, January 2023, at http://rogerclarke.com/ID/PGTA.html
Clarke R. (2023b) 'The Authentication of Assertions Involving Entities and Identities' Xamax Consultancy Pty Ltd, January 2023, at http://rogerclarke.com/ID/IDM-IEA.html
Davis G.B. (1974) 'Management Information Systems: Conceptual Foundations, Structure, and Development' McGraw-Hill, 1974
Huh Y.U., Keller F.R., Redman T.C. & Watkins A.R. (1990) 'Data Quality' Information and Software Technology 32, 8 (1990) 559-565
Koestler A. (1964) 'The Act Of Creation' Hutchinson, 1964
Miller D.W. & Starr M.K. (1967) 'The Structure of Human Decisions' Prentice Hall, 1967
Mingers J. (2001) 'Combining IS Research Methods: Towards a Pluralist Methodology' Information Systems Research 12, 3 (2001) 240-259, at https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.335.8089&rep=rep1∓type=pdf
Mueller H. & Freytag J.-C. (2003) 'Problems, Methods and Challenges in Comprehensive Data Cleansing' Technical Report HUB-IB-164, Humboldt-Universitart zu Berlin, Institut fuer Informatik, 2003, at http://www.informatik.uni-jena.de/dbis/lehre/ss2005/sem_dwh/lit/MuFr03.pdfhttp:/www.informatik.uni-jena.de/dbis/lehre/ss2005/sem_dwh/lit/MuFr03.pdf
Myers M.D. (2018) 'The philosopher's corner: The value of philosophical debate: Paul Feyerabend and his relevance for IS research' The DATA BASE for Advances in Information Systems 49, 4 (November 2018) 11-14
Piprani B. & Ernst D. (2008) 'A Model for Data Quality Assessment' Proc. OTM Workshops (5333) 2008, pp 750-759
Shannon C.E. & Weaver W. (1949) 'A Mathematical Model of Communication' University of Illinois Press, 1949
Wang R.Y. & Strong D.M. (1996) 'Beyond Accuracy: What Data Quality Means to Data Consumers' Journal of Management Information Systems 12, 4 (Spring, 1996) 5-33
Weber R. (1997) 'Ontological Foundations of Information Systems' Coopers & Lybrand Research Methodology Monograph No. 4, 1997
Weinberger SD. (2010) 'The Problem with the Data-Information-Knowledge-Wisdom Hierarchy' Harvard Bus. Rev., 2 Feb 2010, at https://hbr.org/2010/02/data-is-to-info-as-info-is-not
Roger Clarke is Principal of Xamax Consultancy Pty Ltd, Canberra. He is also a Visiting Professor associated with the Allens Hub for Technology, Law and Innovation in UNSW Law, and a Visiting Professor in the Research School of Computer Science at the Australian National University.
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