Glossary
- analytics-ready data
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data that has been prepared to meet the requirements of analysis. This means the data is clean, consistent, complete and formatted in a way that allows for easy interpretation. To achieve this state, the data undergoes pre-processing steps, such as cleaning, transformation and validation, to align it with the specific goals of the analysis.
- data
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(singular: datum) refers to a collection of raw, unprocessed facts, measurements or observations. Data can take many forms, including numbers, text, symbols, images, audio, video, and more.
- data accessibility
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the ease with which authorised users can locate, retrieve and use data when needed for analysis or decision-making
- data cleaning
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process of identifying and correcting errors, inconsistencies and missing pieces of information in a dataset to improve its quality and reliability
- data completeness
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extent to which all required data fields, including the metadata that describes the characteristics and context of the data, are available and properly filled
- data consistency
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data values remain uniform (identical and accurate) across datasets and systems
- data consolidation
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in a business setting, data consolidation is the process of combining information collected from different systems or tools into one central place
- data content accuracy
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data values are correct and reflect the true state of the object or event they represent
- data contextuality
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refers to whether the data captures the context surrounding an event or transaction, enhancing its interpretability
- data diversity
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the variety of data types and sources, ensuring a multi-dimensional view
- data granularity
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refers to the level of detail in the data. High granularity means the data is very detailed, while low granularity provides a summarised view.
- data integrity
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ensures that relationships between data elements are maintained correctly
- data interoperability
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ability of data to integrate seamlessly across different platforms, systems and tools
- data interpretability
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the extent to which the data can be analysed and understood in a meaningful way
- data pre-processing
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preparing raw data for analysis by applying techniques such as cleaning, transformation, reduction and integration. It ensures that data is in an optimal state for generating reliable and accurate insights.
- data privacy
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the rights of individuals to control how their personal data is collected, stored and shared in compliance with ethical and legal standards
- data reduction
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process of making large datasets smaller and simpler without losing important information
- data relevance
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measures of whether the data is meaningful and applicable to the specific analysis or business question
- data richness
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refers to the depth, breadth and diversity of data available for analysis
- data scalability
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ensures that the data infrastructure can handle growth in data volume, variety and complexity without degrading performance
- data security
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refers to the measures and technologies used to protect data from unauthorised access, breaches, theft or corruption
- data timeliness
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also referred to as data currency ensures that data is up-to-date and available at the moment when it is needed for decision-making
- data traceability
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ability to trace data back to its source and track its transformations during processing
- data transformation
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process of converting raw data into a format suitable for analysis. It involves modifying the structure, format or values of data to meet specific requirements of the analytics task.
- data uniqueness
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no duplicate records exist within a dataset
- data usability
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the ease with which data can be accessed, understood and utilised by end-users
- data validity
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data follows predefined formats, types or business rules
- DIKW hierarchy
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Data-Information-Knowledge-Wisdom (DIKW) hierarchy illustrates how raw data is transformed into meaningful insights and, ultimately, into wise decision-making.
- information
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data that has been processed, organised or structured to provide meaning and context, making it more understandable and relevant to specific purposes. Unlike raw data, information serves a particular goal, often answering specific questions or helping with decision-making.
- knowledge
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created by analysing and synthesising information to identify patterns, relationships or insights that contribute to a deeper understanding. It connects various pieces of information, enabling interpretation based on experience, expertise or reasoning. Knowledge goes beyond basic facts, providing meaningful insights that often lead to actionable recommendations for strategic decisions.
- temporal relevance
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ensures that data corresponds to the appropriate time frame for the specific analysis or decision-making process.
- wisdom
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represents the highest level in the progression from data to decision-making. It is the ability to make sound decisions and judgements based on accumulated knowledge and experience. Wisdom involves applying knowledge with insight, ethics and a long-term perspective, carefully weighing the potential impacts of decisions.
