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Data strategy and governance glossary
50 terms from R14, Data strategy and governance — 25 defined in the guide itself and 25 more from the field around it. Every term the guide teaches links to the slide that teaches it.
R14 · How to improve
Data strategy and governance
What data do we need, and who is responsible for it?
Every term below is defined in the words of data strategy and governance, guide R14 of craft guides for educational technologists, and opens the guide at the slide where it is taught. 25 of the 50 are the field’s vocabulary rather than the guide’s own: words a reader will meet around this subject, defined here because the guide assumes them. 1 term is also defined by another guide in the series; where the two differ, both wordings are given. The whole craft glossary holds all of them together.
| Term | Definition | Referred to in | Read further |
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| B | |||
| Business glossary | An agreed list of an organisation's terms, such as "persistent absence" or "enrolled", each with one definition and a named owner. |
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| Business intelligence (BI) | The tools and practices for turning an organisation's data into reports, dashboards and queries that managers use to monitor performance and make decisions. |
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| C | |||
| Caliper Analytics | A standard that describes learning activity, what learners did, in a common form (1EdTech Consortium, n.d.-a). |
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| Central model | A model in which one team collects, stores and serves all data. It is simple to govern and often slow, because everything waits on the central team. |
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| Custodian | The person or team that runs the systems a dataset is stored in, including security, backup and deletion. |
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| D | |||
| Dark data | Stored data whose value is unknown. In a 2016 survey, organisations described 52% of what they stored this way, and another 33% as redundant, obsolete or trivial (Veritas Technologies, 2016). |
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| Data catalogue | A searchable inventory of an organisation's datasets, recording what each holds, where it is stored, who owns it and how to request access. |
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| Data cleansing | Correcting or removing data that is wrong, incomplete, duplicated or badly formatted. It treats the symptom; fixing the point of entry treats the cause. |
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| Data dictionary | A document listing each field in a dataset with its name, meaning, format, allowed values and source, so that everyone reads the data the same way. |
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| Data governance | Policies and procedures covering data across its life, "from acquisition to use to disposal" (Privacy Technical Assistance Center, 2015). |
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| Data governance council | The cross-department group that sets data policy, resolves disputes between owners and approves standards. Also called a data governance committee or board. |
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| Data lake | A store that holds large volumes of data in its raw, original form, structured or not, leaving it to be organised when it is used. |
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| Data lifecycle | The stages a dataset passes through, from planning and collection, through storage, use and sharing, to archiving or deletion. |
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| Data literacy | The ability to read, question, interpret and act on data. For educators, it means turning data into teaching decisions. |
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| Data maturity assessment | A structured review that rates an organisation's data management practices against defined levels, to show where it stands and what to improve first. |
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| Data mesh | A model in which domain teams own their data and serve it to others as a product, on a shared platform and under federated rules (Dehghani, 2022). |
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| Data minimisation | Collecting only the personal data that is adequate, relevant and necessary for a stated purpose (European Union, 2016). |
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| Data model | A description of the things a system holds data about, their attributes and how they relate, such as pupils, classes and enrolments. |
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| Data pipeline | An automated series of steps that moves data from where it is produced to where it is used, transforming and checking it on the way. |
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| Data product | A dataset prepared and maintained for others to use, with documentation, quality guarantees and a named owner, as a team would treat any product. |
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| Data profiling | Examining a dataset's actual contents, such as value ranges, blanks, formats and duplicates, to find quality problems before the data is used. |
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| Data quality | How fit data is for a stated purpose, judged on dimensions such as completeness and accuracy (Government Data Quality Hub, 2020). |
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| Data quality dimensions | The aspects on which data quality is assessed, commonly accuracy, completeness, uniqueness, consistency, timeliness and validity. |
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| Data warehouse | A central store of cleaned, structured data drawn from an organisation's operational systems and arranged for reporting and analysis, not for day-to-day transactions. |
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| Data-driven decision making | The systematic collection and analysis of data to guide decisions in schools and systems. Many now prefer "data-informed", to keep professional judgement in the picture. |
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| Decision register | A table of the decisions a data collection serves, with each decision's owner, date, data needs and threshold. |
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| E | |||
| Ed-Fi Data Standard | A standard covering K–12 information about students and their academic performance, used by a number of US states and districts, for example for records sent to the state (Ed-Fi Alliance, n.d.). |
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| Experience API (xAPI) | A specification for recording what learners did and sending it to a learning record store, published as IEEE 9274.1.1-2023 (IEEE, 2023). |
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| Extract, transform, load (ETL) | The process of copying data out of source systems, reshaping and cleaning it, and loading it into a warehouse or other target store. |
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| F | |||
| FAIR principles | Guidelines that data should be findable, accessible, interoperable and reusable, by machines as well as people. FAIR data need not be open data. |
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| Federated model | A model in which the teams closest to data own it, and a central group sets standards they follow. R08 A way of owning a design system in which a representative, empowered subset of designers and leaders from product teams shape it together, with a central group to document and maintain it (Curtis, 2015). |
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| I | |||
| Identifier | A value that distinguishes one record, such as a student or a school, from every other. |
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| Interoperability standard | An agreed format and method for exchanging a kind of data between systems, such as OneRoster or LTI. |
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| L | |||
| Learning Tools Interoperability (LTI) | A standard that lets learning management systems or platforms integrate remote tools and content in a standard way, such as opening a tool from the LMS (1EdTech Consortium, n.d.-c). |
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| Lineage | A record of where data came from and what has been done to it on the way. |
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| Longitudinal data system | A system that links records about the same learners across years, institutions and sometimes sectors, so that their progress can be followed over time. |
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| M | |||
| Management information system (MIS) | The core administrative system a school uses to hold pupil, staff, attendance and assessment records. Called a student information system (SIS) in North America. |
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| Master data | The shared core records that other data refers to: students, staff, schools, classes and courses. |
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| Metadata | Data that describes data: what a field means, its source, its owner and its quality. |
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| O | |||
| OneRoster | An education data standard for exchanging roster information, course materials and grades between systems. It answers who is in which class (1EdTech Consortium, n.d.-b). |
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| Open data | Data published for anyone to access, use and share freely, usually under an open licence and in a machine-readable format. |
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| Owner | The one person accountable for a dataset: who may use it, what quality it needs and when it ends. |
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| P | |||
| Purpose limitation | Collecting data for specified, explicit purposes and not using it in ways incompatible with them (European Union, 2016). |
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| R | |||
| Record linkage | Matching records that belong to the same person across datasets, using a shared identifier or by comparing details such as name and date of birth. |
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| Reference data | Agreed lists of codes and categories used to classify other data, such as school type, year group or ethnicity codes. |
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| Retention schedule | A list of datasets with how long each is kept, why, and who deletes it. |
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| S | |||
| Steward | The person who defines a dataset's fields and maintains its quality day to day. |
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| Storage limitation | Keeping identifiable personal data no longer than its purpose requires (European Union, 2016). |
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| System of record | The system treated as the authoritative source for a given piece of data. Where copies disagree, its value stands. |
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| U | |||
| Unique pupil number (UPN) | England's 13-character pupil identifier, expected to stay with a pupil throughout their school career, never reissued, and not to be used for any purpose unrelated to education (Department for Education, 2019). |
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