Glossary and definitions

A glossary of terms used in the data maturity framework.

[A] - [B] - [C] - [D] - [E] - [F] - [G] - [H] - [I] - [J] - [K] - [L] - [M] - [N] - [O] - [P] - [Q] - [R] - [S] - [T] - [U] - [V] - [W] - [X] - [Y] - [Z]

A


Term	Abbreviation	Description
Alteryx		A self-service platform to prepare, blend and analyse data.
Amazon Redshift		Amazon's cloud-based data warehouse.
Amazon Web Services	AWS	Amazon's cloud platform.
Application programming interface	API	For automated data movement between systems and data layers.
Applications		Software to support digital activity, most commonly the Microsoft or Google suites.
Artefacts		Templates or other documentation used within reporting or data governance activities (eg, report specification template).
Artificial intelligence	AI	The intelligence of machines which, in this environment, refers to its use to process and analyse large quantities of data to extract patterns and suggest predictions.
Attribute		Within data modelling, descriptive information relating to entities. (eg, a student entity will have attributes such as student ID and student surname).
Audit balance control	ABC	The controls required in data processing to ensure that tables are populated in a logical order and adhere to dependencies. Poor ABC can result in data processes breaking.

B


Term	Abbreviation	Description
Big data		Large diverse data sources that continue to grow. These may be structured (numeric) or unstructured (emails, web comments). Data sources are likely to be long (contain tens of millions of rows) as well as wide (tens to hundreds of columns) and require specialist processing software.
Blocker		An issue that prevents something from happening or succeeding.
Building digital capabilities	BDC	A Jisc product, which enables individuals to assess their digital understanding and skillsets. On completion, access is given to resources to support learning and development.
Business analyst		This role focuses on process improvement and may be used in the analysis of system provision to ensure a solution meets the needs of users.
Business architect		A role responsible for the development of business architecture.
Business architecture		A practice which considers capability, delivery, structure and information across the organisation. This includes the role of people, processes and strategies and helps to ensure the right activity is taking place to allow the organisation to function effectively.
Business area		An operational or strategic area of your institution or college with a definable set of requirements for reporting (eg, student recruitment).
Business glossary		An element of a data catalogue that includes Business Metadata (eg student ID).
Business intelligence		The processes and technical infrastructure that collects, stores, and analyses the data produced by your organisation’s activities.
Business intelligence analyst		This is a hybrid technical - analytical role: skillsets include requirements gathering, report definition, data analysis, report building in visualisation software and presenting to stakeholders. In some roles, SQL skills enable dataset development.
Business intelligence developer		This role requires a technical skillset, usually SQL, to write data processing routines that transform data in system tables into subsets structured for onward reporting or analysis.
Business owner		The function or head of function responsible for an operational area. They may line manage the business system owner, and are likely to hold some accountability in relation to system data quality.
Business process analysis		The documentation of how system tables update based on user action. This knowledge is needed when deltas are used to find changes to datasets (rather than making complete table copies) in data warehousing.
Business question	BQ	A distinct question that can be asked of a data set, as the starting point for data model definition (eg "How many mature students do we have?" is a simple business question). In data warehousing, a business question may be linked to a definitive business question (eg How many students do you have?).
Business rule		The logic that is applied to the transformation of a dataset, often to define student or staff populations based on definitions required by statutory bodies or funders (eg full time, part time).
Business sub area		A subset of business area, in more complex organisations, enabling the clearer definition of reporting needs or ownership (eg within student recruitment a sub area may be outreach).
Business system owner		The function or individual responsible for an operational system. While IT may procure and configure systems, this is done in conjunction with the admistrative section.

C


Term	Abbreviation	Description
Cloud		On demand data storage and computing power from an external provider. This is an alternative to on premise server management and can provide an agile solution to data storage.
Conceptual data model		A high-level description of information needs that typically includes only the main concepts and the main relationships among them.
Core reporting		A defined set of automated corporate reports aimed at user groups, typcially delivered by a central team to an agreed schedule. They provide a user friendly single version of the truth for commonly required data and can reduce the colume of ad hoc queries. They should enable efforts to focus on more complex and bespoke queries.
Corporate system		A system to support adminsitrative activity that is maintained or is overseen by a central IT team. Good corporate systems negate the need for siloed, ad hoc or spreadsheet solutions in departments or professional services.
Cube		A multidimensional data model that stores optimized, summarized or aggregated data represented as dimensions and facts. Typically used to decrease processing time so report filtering is faster than when pointed at an entire database. They are of less use when the database is well structured or processing power is strong.
Curriculum analytics		The use of data to analyse and optimise the structure of the curriculum (eg defining assessment methods based on data that evidences how different student cohorts perform).
Customer relationship management	CRM	A system for recording interactions with customers or organisations. They hold contact details, account information and all meetings and discussions between parties.

D


Term	Abbreviation	Description
Dashboard		A visual representation of the data needed to understand a reporting area. Dashboards are usually automated to enable the user to filter options. Good dashboards adhere to UX and accessibility considerations.
Data aggregation		Combining raw transactional data in system tables to form aggregated values (eg sum, average).
Data analysis expressions	DAX	A language used within Microsoft products.
Data analyst		A role that examines, cleans, tranforms and models data for onward use in tools such as MS excel or SSMS. This role may be relatively early career and a starting point for progression into more specialist roles - BI developer, BI analyst, data engineer.
Data architect		A role responsible for the development of data architecture.
Data architecture		The consistent management and documenation of data types across the institution or college, including meta data, master data and reference data, and the tools that enable this.
Data catalogue		An inventory of all data in the organisation. This is the tool that can help you manage data as an asset by helping you know what data you have.
Data champion		A senior leader who represents and presents data related considerations in strategic discussion.
Data consultancy		A Jisc service - helping institutions analyse their data and develop their data maturity.
Data denormalisation		This is a technique used in data warehousing for reducing the number of joins between tables, which can affect speed. One larger table may replace several smaller, adding duplication but improving querying. Duplicating data can however make the table harder to maintain.
Data dictionary		The definition of technical metadata, and data lineage, for a specific database or system.
Data domain		A definable set of corporate data, typically from a single database or system, or several systems linked by agreed meta data (eg student, finance) with agreed ownership.
Data driven		An organisation that looks to maximise the value of the data it holds and use it proactively for the benefit of the business through investing in data skills.
Data engineer		A role that acquires datasets, creates algorithms to transform the data and tests and validates the outputs. This may support operational reporting or data science.
Data engineering		The design and build of systems to collect, store and analyse data by data engineers.
Data exploitation		In data warehousing, the creation of flags or markers in the database to enable standard definitions across all downstream outputs (eg, once disparate system codes for age are understood, a young/mature flag can be created that can be used across all data sources).
Data exploration		Examining data sources to see what insight they may provide. This contrasts with reporting which is typically requirements driven. Where core reporting is in place, a mature team will be resourced to explore new uses from data assets.
Data fabric		A design concept that optimizes data management by automating repetitive tasks such as profiling datasets, discovering and aligning schema to new data sources, and at its most advanced, resolving failed data integration.
Data governance		In simple terms, ensuring that data is managed, or doing the right things.
Data governance - accountability		The clarity on individual, team and functional responsibilities in relation to data assets.
Data governance - accountability - data ownership		The visibility and defined accountability for all data assets in the university, provider or college.
Data governance - acountability - data roles		The roles needed to support data management and governance. These may be specialist standalone posts or form part of a wider remit.
Data governance - accountability - resolution		The collaboration between functions and individuals to ensure data quality or governance issues are resolved successfully.
Data governance - assurance		The mechanisms to monitor the effectiveness of data governance activities.
Data governance - assurance - issue management		The processes and planning in place to ensure data management activities are identified and resolved.
Data governance - assurance - monitoring and measurement		The metrics and targets that enable assessment of the success of data management activity.
Data governance - assurance - policies		The principles we adhere to in our data management activities and the guidance for how to manage breaches to these.
Data governance - change Impact		The impact on data assets of predicted and unpredicted change activity.
Data governance - change impact - managing turnover		Succession planning and mitigating the risks of single points of failure in teams.
Data governance - change impact - new systems		The visbility of data within systems, flows and dependencies that enables successful system replacement activity.
Data governance - change impact - organisational structure		The management of institution, provider or college hierarchies to ensure changes do not impact data or reporting provision.
Data governance - engagement		The effective management of relationships with stakeholders and approach to the barriers to effective data governance.
Data governance - engagement - barriers		Collective understanding of data governance, and the cultural barriers (for example, lack of engagement) that risk good practice.
Data governance - engagement - behaviours		The reality of data management in the organisation - is there best practice?
Data governance - engagement - stakeholders		Understanding the stakeholders in data management and how proactively they are engaged.
Data governance - risk		The appetite for and approach to the management of risk in relation to data assets.
Data governance - risk - business continuity		How integral is data management and use within business continuity activity?
Data governance - risk - privacy		The understanding of legislation relating to identifiable data, including GDPR.
Data governance - risk - risk assessment		The processes in place to monitor, manage and mitigate risks in relation to data and data usage.
Data governance - value		The understanding of the value that can be gained from data assets and how that can be realised.
Data governance - value - data lineage		The visibility and documentation of how data is transformed as it moves across the organisation, enabling effective error checks and management.
Data governance - value - data relationships		Collective understanding of how data sources or individual tables relate, so they can be joined and then leveraged for value added activity.
Data governance - value - definitions and standards		Consensus on the best format for a data dictionary that will work across all data assets.
Data lake		A centralised repository storing data in raw, unstructured form.
Data lakehouse		A unified platform to store and analyse diverse data types, combining the capability of a data lake and a data warehouse.
Data lineage		An understanding of the source, transformation and movement of data through the organisation, to provide visibility of the impact of changes.
Data literacy		The ability to read, analyse and communicate with data. It empowers your teams, helps them make good use of the data available. Relates to senior leaders as well as the broader skillsets of all staff.
Data management		In simple terms, managing your data to achieve goals - or, doing the right things right. This includes the data that is required, and the management of data at the end of its lifecycle - when it is no longer needed.
Data mesh architecture		A structured approach to enable decentralised teams to work in an agile manner to deliver and consume data products for each data domain.
Data pipeline		A set of processes and tools that automate the flow of data from source tables to target destinations, including processing and transformation.
Data platform		The range of elements required to support data management including data architecture, data warehousing, data analysis and data sharing. It also includes security considerations and policies.
Data processing		The activity of handling and transforming data from one state to another, typically involving joining tables, applying business rules and aggregation into metrics.
Data quality		Quality in this instance means fir for purpose - or good enough for the task it is intended for. This may require complete data accuracy, or sufficient accuracy for the task in hand.
Data query		A request to fetch data from a database, in order to process the data for onward use.
Data refresh		The update of a dataset in use for analysis or dashboarding. Refresh can be automated to a schedule (less than a minute to annually) or performed manually. The manually refresh of datasets used in interactive reporting software is a common hidden resource challenge.
Data science		The use of statistics, probability and scientific methods for data analysis and insight.
Data silo		A database or other data repository (often a spreadsheet) not accessible to other parts of the universary or college. Silos affect efficiency and trust and are typcially not structured consistently (eg not following standard master or reference data) creating integration challenges.
Data steward		A role responsible for maximising the value of data assets - through oversight of processes and driving consistency.
Data strategy		The definition of how data assets and their value are utilised and support the institutional or college strategy.
Data strategy - executive accountability		The role and responsibilities of the executive in the delivery of effective data management activities.
Data strategy - executive accountability - framework		The formal structure that has been agreed to ensure data is managed effectively and accountability is clear.
Data strategy - executive accountability - oversight		The role of the executive or senior team in ensuring data is managed well.
Data strategy - executive accountability - security		The safe storage and use of data in relation to current legislation and organisational risk appetite.
Data strategy - executive engagement		The priority assigned to the management and use of data as an organistional asset by the executive or senior leadership team.
Data strategy - executive engagement - investment		The availability of funds to support data improvement activities, and the visibility and support of these activities among senior leaders.
Data strategy - executive engagement - senior awareness		The approach of senior leaders to data, and whether it is viewed offensively as a capability or defensively as a risk.
Data strategy - executive engagement - senior experience		Previous experience in the delivery of data and analytics services within the executive team.
Data strategy - information lifecycle		The university, college or provider approach to managing information, its appetite for risk and awareness of constraints.
Data strategy - information lifecycle - asset value		Statement on how data assets can support organisational strategy and objectives.
Data strategy - information lifecycle - constraints and ethics		Consideration and communication on the ethical use of data so that it supports but does not risk or exploit students and staff.
Data strategy - information lifecycle - risk appetite		Statement on organisational appetite for risk and how this defines thinking on the benefits of innovation versus the threat of change.
Data strategy - priorities		The definition of how data can support organisational objectives.
Data strategy - priorities - change		Organisational awareness of the impact of change activities - new systems or processes - on data and onward reporting products and how this will be mitigated.
Data strategy - priorities - delivery		How data and analytics activities are prioritised. This may include the definition of specific areas that will support organisational objectives, for example, the collection of data needed to understand student starting points.
Data strategy - priorities - outcomes		The expectations of what data will deliver for the organisation - which may include products or may focus on value, for example, supporting student wellbeing.
Data strategy - responsibilities		The definition of capabilities and functions required for data to support organisational objectives.
Data strategy - responsibilities - collaboration		The expectation of how teams will collaborate in data management and reporting, to help address functional and individual siloes.
Data strategy - responsibilities - people		The approach to recruiting people with the skills and experience to deliver effective data operations.
Data strategy - responsibilities - resourcing		The balance between the role of IT teams and the capabilities needed in other functional areas.
Data strategy - usage		The definition of how teams and individuals will use data.
Data strategy - usage - access		Clarity on how data should be made available as an institutional asset - and the interdependence with effective information governance and security.
Data strategy - usage - decisions		Organisational expectations on how data should be used by individuals and collectively to support effective decision making.
Data strategy - usage - value		The definition of what value is attributed to data as an organisational asset.
Data warehouse architect		A role responsible for the conceptual design and maintenance of a data warehouse using data warehousing methodologies (eg Kimball).
Data warehouse developer		A role responsible for the physical build and maintenance of a data warehouse using data warehousing methodologies, based on agreed standards.
Data warehouse		A highly structured collection of tables for analysing and reporting data which blend fields from different sources for onward use. This contrasts with an ODS which is largely copies of system tables. Data is typically denormalised and supports dimensional modelling, based on conceptual thinking from Kimball or Inmon.
Database		An organised collection of information or tables of data that mulitple users and processes can access and edit.
Decision making - administration		The availability of operational reporting to support process delivey.
Decision making - administration - granularity		Access to information at multiple levels of aggregation, from a subject view down to an individual student.
Decision making - administration - information		The provision of central operational reporting to support day to day processes (eg, a student option change).
Decision making - administration - latency		Availability of information that at required frequency to support decisions and processes (eg, course places available).
Decision making - data literacy		The capability of teams to work effectively with reporting products.
Decision making - data literacy - analysis		The ability to analyse data, understand materiality and use it to support or dissuade thinking.
Decision making - data Literacy - capability		Clarity on the skillsets required in different roles and support to teams to develop these skills.
Decision making - data literacy - communication		Effective communications in relation to data - understanding the appropriate detail for an audience and demystifying concepts.
Decision making - identifying improvements		The availability of management information and its use in enabling decisions.
Decision making - identifying improvements - baseline		The provision of central management information reports that establish an internal "single version of the truth" in business areas.
Decision making - identifying Improvements - benchmarking		The availabliity of comparator information (eg, sector trends, league tables, regulatory metrics (Ofsted, OFS, UKRI) and it's effective use.
Decision making - identifying improvements - causality		The ability to query and interpret data to understand the influence of different factors, helping to target the root cause of issues or differentiation.
Decision making - informing policy		The availability of insight that enables the university, college or provider to change or innovate.
Decision making - informing policy - innovation		The aspiration for sector leading activity in data usage and outcomes.
Decision making - informing policy - market differentiation		The use of data for competitive advantage, which in this context relates to how to support students most effectively and an understanding of the sector and your offer within it.
Decision making - informing policy - optimisation		The use of data in defining the best structure for subject areas, departments or professional support functions and the appropriate metrics to monitor their success.
Decision making - measuring change		The processes to monitor performance.
Decision making - measuring change - benefits realisation		The use of data to define success factors for project and change activity and monitor these post delivery.
Decision making - measuring change - key performance indicators		The use of appropriate, actionable and measurable metrics to monitor progress against organisation objectives, as defined within the institutional, college or provider strategy.
Decision making - measuring change - performance indicators		The use of appropriate, actionable and measurable metrics to understand the progress made within individual functions.
Decision making - statutory obligations		The resourcing and support for regulatory requirements.
Decision making - statutoryobligations - FOIs		The processes and data available to respond to freedom of information requests.
Decision making - statutory obligations - PSRBs		The data available to support professional and regulatory demands from funders and accreditors.
Decision making - statutory obligations - statutory returns		The teams in place to successfully interpret statutory guidance and the data available to fulfil data specifications.
Definitive business question	DBQ	A statement used in dimensional modelling design to define a fact and it's dimensions from a series of business questions (eg "how many mature students do you have?" and "how many students do you have from the local area?" share the DBQ "how many students do you have?" with age and location dimensions joined).
Delta		In data warehousing, a way of storing or transmitting data in the form of differences (deltas) between sequential data rather than complete files.
Descriptive analytics		The process of using current and historical data to identify trends and relationships using metrics such as mean, mode, median and counts.
Digital elevation tool	DET	A Jisc service for the FE sector - Helping the further education and skills sector get a clear picture of their digital elevation journey with access to resources and services to support next steps.
Digital experience insights	DEI	A Jisc service to survey and understand your students, teaching and professional service staff use of technology.
Dimension		In dimensional modelling, the contextual background for facts (eg in student demographics).
Dimensional modelling		The organisation of data into fact and dimension tables for effective onward analysis. The physical imagery of this design led to the term star schema. This modelling technique is associated with data warehouses.

E


Term	Abbreviation	Description
Enterprise		Considerations across the entire university or college systems, process and capability landscape.
Entity		Within data modelling, the main elements of the domain (eg, student, course).
Entity relationship diagram	ERD	A structural diagram containing the symbols and connectors that visualize major entities and the inter-relationships among them.
Extract, load, transform	ELT	A variant of ETL where extracted data is loaded into the target system first - used where processing power supports transformation in the data warehouse (eg, using cloud technologies).
Extract, transform, load	ETL	A three-phase process where data is extracted, transformed (cleaned) and loaded into an output table or location (eg, a data warehouse). Data can be collated from one or more sources and it can also be output to one or more destinations.

F


Term	Abbreviation	Description
Fact		In dimensional modelling, a collection of associated data items, consisting of measures and context data. It typically represents business items or business transactions (eg a student registration).

I


Term	Abbreviation	Description
Inmon		A top down enterprise methodology for data warehouse conceptual design.

K


Term	Abbreviation	Description
Key performance indicator	KPI	Type of performance monitoring to help measure or define how successful an organisation is at meeting its aims and objectives.
Kimball		A bottom up iterative methodology for data warehouse conceptual design.
Key performance indicator	KPI	Type of performance monitoring to help measure or define how successful an organisation is at meeting its aims and objectives.

L


Term	Abbreviation	Description
Learning analytics	LA	A Jisc service to help providers manage and optimise their students' learning anf engagement.
Logical data model		A data model of a specific domain expressed in terms of data structures such as relational tables and columns.

M


Term	Abbreviation	Description
Machine learning		A field of artificial intelligence that uses algorithms trained on data sets to create models that enable machines to perform tasks including categorizing and analyzing data, and predicting changes.
Master data		Core information about entities (eg, a student, a course) that remains relevatively stable over time (eg student name, course name).
Meta data		Data about data - it provides information about the structure, provenance and meaning of data (eg date, time, type, last updated). This is where you define what you mean by "a student".
Microsoft Azure		Microsoft’s cloud platform.
Microsoft Data Factory		A Microsoft Azure product for data integration services - in effect, the cloud version of SSIS.
Microsoft Fabric		A Microsoft implementation of data mesh architecture, providing a cloud based integrated solution for analytics.
Multi dimensional model		A structure in the form of a cube that enables data to be modeled and viewed in multiple dimensions through the use of dimensions and facts.

N


Term	Abbreviation	Description
National student survey	NSS	An annual survey run by the OFS for HE students to assess their satisfaction with their teaching, feedback and facilities.

O


Term	Abbreviation	Description
On premise		Software managed and maintained by the university or college, rather than flexibly by a third party in the cloud.
Operational data store	ODS	A database containing live or near live copies of system tables, sometimes with some light touch transformation (for example, aggregating a series of transactions). It is used for operational reporting and may form the starting point for data warehousing or other analytical products
Oracle		Database software and technology provider.

P


Term	Abbreviation	Description
Performance indicator	PI	Type of performance monitoring to help measure or define how successful a process, service, team or change initiative is at meeting its aims and objectives.
Physical data model		The model that details the database artefacts required to create relationships between tables, such as indexes, constraints, links and partitions.
POLAR4		POLAR classifies local areas into five groups - or quintiles - based on the proportion of young people who enter higher education aged 18 or 19 years old. The methodology to calculate POLAR changes over time and POLAR4 is the most recent.
Power apps		Microsoft's suite of apps, services and data connectors to build custom apps for your university or college.
Power BI		Microsoft's data dashboard and analytics platform.
Power Query		Microsoft's ETL tool for data transformation that integrates with MS Excel and Power BI.
Predictive analytics		A form of analytics applying machine learning to generate a predictive model.
Prescriptive analytics		A form of analytics which suggests options to take advantage of opportunity or mitigate future risk. It helps you consider "the best course of action to take" in the light of information derived from descriptive and predictive analytics.
Protected characteristics		Data that needs more protection because it is sensitive for example age, disibility, ethnicity and others. To process this data, you must identify both a lawful basis under GDPR and process it appropriately.
Professional, statutory and regulatory bodies.	PSRB	A diverse group of professional and employer bodies, regulators and those with statutory authority over a profession or group of professionals.
Python		A general purpose programming language of particular use in data analysis and machine learning.

Q


Term	Abbreviation	Description
Qlik		A dashboard and analytics platform, previously called QlikView.

R


Term	Abbreviation	Description
R		A programming language for statistical analysis and graphics.
Reference data		Information used to categorise or classify entities (eg subject area, department name).
Relational database		Where data is organised into one or more tables of columns and rows, with a unique key identifying each row. Relationships are a logical connection between different tables and can be modelled as an entity-relationship model (ERD).
Report specification		A customer facing definition document detailing the purpose, audience and key business rules in a report. This should be supported by technical documentation or standards detailing transformations for the data engineering or analysis functions.
Reporting - data exploration		The investigation of how data can support new approaches.
Reporting - data exploration - data science		The analysis of data within the data platform or reporting layer to discover new insights or drive future reporting provision.
Reporting - data exploration - predictive analytics		The use of AI tools to enhance analytical capabilities and outputs.
Reporting - data exploration - strategic outputs		The ad hoc analysis of corporate date for executive customers.
Reporting - modelling and datasets		The creation of datasets in a reporting layer or repository, their audience and purpose.
Reporting - modelling and datasets - availability		The definition of permission based access to datasets within the reporting layer.
Reporting - modelling and datasets - integration		The blending of data from different systems of record to enable value added automated reporting.
Reporting - modelling and datasets - purpose		The understanding of approaches to data storage and the method that best suits the needs of the organisation.
Reporting - orchestration		The extraction and transformation of data from systems or a data platform into a reporting layer or repository.
Reporting - orchestration - maintenance		The governance processes that ensure ETL is undertaken effectively and accurately.
Reporting - orchestration - organisation		The journey to automation of daily data processing activity.
Reporting - orchestration - transformation		The value added during data processing to ensure datasets enable automated reporting.
Reporting - report providers and users		The definition of reporting roles and functional collaboration.
Reporting - report providers and users - approach		The journey to automation of daily report build and analysis.
Reporting - report providers and users - functions		Our investment in analytics capability.
Reporting - report providers and users - hub and spoke		The collaboration between teams and individuals to ensure reporting is fit for purpose.
Reporting - reporting offer		The provision and deployment of effective reporting.
Reporting - reporting offer - core reporting		The specifications and delivery of automated reports and dashboards that support generic requirements.
Reporting - reporting offer - report access		The provision of a repository for reports and datasets accessible as required.
Reporting - reporting offer - visualisation		The use of charts, colour and storytelling to support report interpretation.
Reporting - requirements traceability		The gathering, analysis and translation of reporting requirements into technical specifications to move away from ad hoc delivery.
Reporting - requirements traceability - business questions		The definition of business requirements to enable the creation of a conceptual data model.
Reporting - requirements traceability - business rules		The definitions applied to a data set to ensure it meets business requirements (eg, for student populations).
Reporting - requirements traceability - reporting architecture		The structured approach to defining core reporting.
Reporting architecture		A customer focused structure for dataset and core reporting production.
Reporting offer		The provision of reporting and services from a central team or collaborative team to the wider university or college. This may be supported by a Service Level Agreement.
Requirements translation		The translation of business requirements or questions into a data specification. This requires hybrid business and data knowledge and is often undertaken by a business intelligence analyst.
Research excellence framework	REF	The REF was first carried out in 2014, replacing the previous Research assessment exercise. The REF is undertaken by the four UK higher education funding bodies: Research England, the Scottish Funding Council (SFC), the Higher Education Funding Council for Wales (HEFCW), and the Department for the Economy, Northern Ireland (DfE).
Role based permissions		The assignment of permissions to systems and data based on a role (eg, Head of Department) rather than by an individual (eg staff name) to ensure appropriate access.

S


Term	Abbreviation	Description
Solution architect		A role that creates the overall technical vision for a specific solution to a business problem.
Solution architecture		The design of specific business operations or activities and how they are supported by IT.
SQL Server Analysis Services	SSAS	Online analytical processing tool within SQL Server to analyze and make sense of information across multiple databases, tables or files.
SQL Server Integration Services	SSIS	Data migration tool within SQL server for data integration and workflow applications. It includes a data warehousing tool used for ETL and can automate maintenance of the SQL Server database and updates to cubes.
SQL Server Management Studio	SSMS	Used for configuring, managing, and administering all components within Microsoft SQL Server.
SQL Server Reporting Services	SSRS	A server-based report generating software system from Microsoft and part of a suite of Microsoft SQL Server services. Administered via a web interface, it can be used to prepare and deliver a variety of interactive and printed reports.
Staff establishment		The posts agreed by the university or college for the delivery of processes and functions. At any one time, the total establishment will vary from total staff numbers due to recruitment or absence.
Staff record system		Software that holds the staff and recruitment database.
Statistical analysis		Analysing data using statistical techniques. These can include applying confident intervals, using regression and hypothesis tests.
Statutory return		Mandatory submission of defined data to regulatory body.
Structured query language	SQL	A programming language designed for managing data held in a relational databases and particularly useful in handling structured data (eg, relations between entities).
Student record system		Software platform that holds student/learner database.
Subject matter expert	SME	A person who is an expert in a particular area of the project being delivered. They may be brought onto a project to provide expert guidance or information to ensure the success of the project.
Super user		Software user that has extended permission levels due to role/knowledge.
Synapse analytics		Microsoft's cloud based data warehouse.
System of record		The agreed master source of data for a business area. Many systems hold copies of system data (eg a student record will hold staff information for access permissions) but good data management should include definition of the system of record for all data assets.
Systems and processes - business architecture		The relationship between the "business" - university, college or provider - and its data.
Systems and processes - business architecture - governance		Clarity on the roles of IT and the rest of the business in managing data.
Systems and processes - business architecture - processes		The relationship between data management and our business processes.
Systems and processes - business architecture - requirements		The understanding of why the data you have is collected.
Systems and processes - data assets		The consistent standards and data definitions that support a unified data landscape.
Systems and processes - data assets - data catalogue		The inventory of all data stored by the organisation.
Systems and processes - data assets - master data		The consistent definition and use of master data across the organisation (eg student name, course name).
Systems and processes - data assets - reference data		The consistent definition and use of reference data across the organisation (eg domiciles).
Systems and processes - data flows		How information is added to systems and how data moves between them.
Systems and processes - data flows - automation		How data is integrated between corporate systems (eg, between the student record and the staff record).
Systems and processes - data flows - controls		The auditing of data as it moves between systems, so that issues are identified and the downstream impact on other systems managed.
Systems and processes - data flows - validation		The processes that support accurate data entry - for example, meta data based field level validation.
Systems and processes - data models		The delivery of good governance - how data is managed well and documented effectively.
Systems and processes - data models - data management		The effective management of data in accordance with data governance policies and frameworks.
Systems and processes - data models - documents and ERDs		The presentation of systems tables and integrations using documents and visual diagrams.
Systems and processes - data models - meta data		The definition of organisational data terms to enable consistency of application and interpretation (eg. what is a "student"?)
Systems and processes - enterprise architecture		The benefits of enterprise architecture on effective data management.
Systems and processes - enterprise architecture - components		The understanding of data architecture as an aspect of enterprise thinking.
Systems and processes - enterprise architecture - design		Our approach to the data lifecycle.
Systems and processes - enterprise architecture - organisational agility		Our ability to respond to opportunities and manage risk.
Systems and processes - technology and applications		The systems that hold your data.
Systems and processes - technology and applications - applications		The approach to an abstract data layer that ensures there is no reliance on point to point system integrations.
Systems and processes - technology and applications - business systems		The corporate systems in your university, college or provider and their adoption as the system of record.
Systems and processes - technology and applications - servers and storage		The effective storage of data and processing power available to the organisation.
Systems architecture		The conceptual model that defines the elements of a system, the relationships between those elements, and the rules governing them. This may consist of hardware, software, documentation, facilities, manual procedures, and the roles played by the organsation or people.

T


Term	Abbreviation	Description
Tableau		A dashboard and analytics platform owned by Salesforce.
Tableau pulse		An AI-enabled analytics solution to enhance Tableau dashboard interactivity.
Tabular model		A data model using column storage in memory that can provide fast results for some measures (eg, distinct counts) than a multidimensional model (cube).
Tailored data set	TDS	A Jisc service providing bespoke datasets from the student, staff, estates or finance record.
Technical metadata		The technical characteristics of the physical assets in systems (eg file, database, application, ETL job).
Technology architecture		A high-level map or plan of the techncial means in the organization,including machines, computer systems and even the physical design of the building that holds the hardware.
Time series		Data collected over time - weekly, monthly, annually - and analysed for trends. It is the starting point for forecasting or other predictive activity.
Transactional data		Specific data that details individual processes (eg a student course registration).

V


Term	Abbreviation	Description
Visual Studio		A Microsoft development environment which can be used to build and deploy cubes.