science

Information quality

Information quality is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Information quality rather than just read about it. In short: Information quality (IQ) is a contextual property of or a perspective to the content within information systems. There exist two complementary yet partially conflicting definitions of high-quality: firstly, information is considered high quality if it is fit for its intended purpose ; secondly, it is deemed high quality if it conforms to specified requirements .

Key takeaways

Information quality belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Information quality to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Information quality from memory before moving on to harder problems.

Reference excerpt

Information quality (IQ) is a contextual property of or a perspective to the content within information systems. There exist two complementary yet partially conflicting definitions of high-quality: firstly, information is considered high quality if it is fit for its intended purpose ; secondly, it is deemed high quality if it conforms to specified requirements . The primary distinction between these definitions is that Juran's perspective focuses on the suitability of information for its intended purpose, which can be measured by the success of its application even without direct access to or exact knowledge of the data. For example, a black-box AI with access to English Wikipedia can work well for users' purposes but using Estonian Wikipedia fails for the same purposes. Given that the AI remains the same, it can be concluded that English version data would be of higher quality in comparison to Estonian version, even without exact comparison of data contents and their properties in each version. In contrast, Crosby emphasizes adherence to predefined specifications, assuming specific criteria rather than measuring the success of its use; for instance, information in Wikipedia could be proven to be good based on criteria such as existing peer validation and academic references, even if the AI results are poor. This approach falls into problems when data is not completely accessible or all quality properties cannot be known and measured leading to false impression of quality due to lacking and misleading metrics. Numerous IQ frameworks and methodologies provide tangible approach to assess and measure DQ/IQ in a robust and rigorous manner.

Conceptual problems Although the foundational definitions are usable for most everyday purposes, specialists often use more complex models for information quality. It has been suggested, however, that higher the quality the greater will be the confidence in meeting more general, less specific contexts.

Dimensions and metrics of information quality "Information quality" is a measure of its fitness for use or conformance to requirements. In this way, "quality" is considered contextual and it can then vary across users and uses of the information. The exact degree of quality is often described with dimensions such as accuracy, timeliness, completeness, and similar scales. Although a huge amount of academic research has been directed to these dimensions, there does not exist consensus on their definitions or practical usefulness . Historically, Richard Wang and Diane Strong proposed a list of dimensions or elements used in assessing Information Quality is:

Intrinsic IQ: accuracy, objectivity, believability, reputation Contextual IQ: relevance, value-added, timeliness, completeness, amount of information Representational IQ: interpretability, format, coherence, compatibility Accessibility IQ: accessibility, access security Other authors propose similar but different lists of dimensions for analysis, and emphasize measurement and reporting as information quality metrics. Larry English prefers the term "characteristics" to dimensions. However, a considerable amount of information quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. Research has recently shown the huge diversity of terms and classification structures used.

Quality metrics

Source:

Authority/verifiability Authority refers to the expertise or recognized official status of a source. Consider the reputation of the author and publisher. When working with legal or government information, consider whether the source is the official provider of the information. Verifiability refers to the ability of a reader to verify the validity of the information irrespective of how authoritative the source is. To verify the facts is part of the duty of care of the journalistic deontology, as well as, where possible, to provide the sources of information so that they can be verified

Scope of coverage Scope of coverage refers to the extent to which a source explores a topic. Consider time periods, geography or jurisdiction and coverage of related or narrower topics.

Composition and organization Composition and organization has to do with the ability of the information source to present its particular message in a coherent, logically sequential manner.

Objectivity Objectivity is the bias or opinion expressed when a writer interprets or analyze facts. Consider the use of persuasive language, the source's presentation of other viewpoints, its reason for providing the information and advertising.

Integrity Adherence to moral and ethical principles; soundness of moral character The state of being whole, entire, or undiminished Comprehensiveness Of large scope; covering or involving much; inclusive: a comprehensive study. Comprehending mentally; having an extensive mental grasp. Insurance. covering or providing broad protection against loss. Validity Validity of some information has to do with the degree of obvious truthfulness which the information carries

Uniqueness As much as 'uniqueness' of a given piece of information is intuitive in meaning, it also significantly implies not only the originating point of the information but also the manner in which it is presented and thus the perception which it conjures. The essence of any piece of information we process consists to a large extent of those two elements.

Timeliness Timeliness refers to information that is current at the time of publication. Consider publication, creation and revision dates. Beware of Web site scripting that automatically reflects the current day's date on a page.

Reproducibility (utilized primarily when referring to instructive information) Means that documented methods are capable of being used on the same data set to achieve a consistent result.

Professional associations IQ International—the International Association for Information and Data Quality IQ International is a not-for-profit, vendor neutral, professional association formed in 2004, dedicated to building the information and data quality profession. CDOIQ Society Chief Data Officers and Information Quality Society is a global professional society supporting data leaders with networking, meetings, best practices, experience, certification, and training.

Information quality conferences A number of major conferences relevant to information quality are held annually:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Information quality

Start with the simplest possible case. Write down what Information quality claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Information quality before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Information quality ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Information quality

In research: Information quality appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Information quality in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Information quality is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data quality, Information science, so understanding it makes those chapters shorter.
In everyday life: Look for Information quality outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Information quality in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Information quality means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Information quality out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Information quality in simple terms?

Why does Information quality matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Information quality?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Information quality.