information structure
The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
Data quality specialists review organisation's data for accuracy, recommend enhancements to record systems and data acquisition processes and assess referential and historical integrity of data. They also develop documents and maintain data quality goals and standards and oversee an organisation's data privacy policy and monitor compliance of data flows against data quality standards.
No competences in this bucket.
The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.
The query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).
The subfield of ethics that assess whether data practices are considerable ethical. It assesses processes such as collecting, analysing and disseminating structured and unstructured data that might negatively impact the society.
The classification of databases, that includes their purpose, characteristics, terminology, models and use such as XML databases, document-oriented databases and full text databases.
Combine characters from a specific alphabet using well defined rules to generate character strings that can be used to describe a language or a pattern.
Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.
Draft a database scheme by following the Relational Database Management System (RDBMS) rules in order to create a logically arranged group of objects such as tables, columns and processes.
Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
Administer all types of data resources through their lifecycle by performing data profiling, parsing, standardisation, identity resolution, cleansing, enhancement and auditing. Ensure the data is fit for purpose, using specialised ICT tools to fulfil the data quality criteria.
Set and maintain standards for transforming data from source schemas into the necessary data structure of a result schema.
Reduce data to their accurate core form (normal forms) in order to achieve such results as minimisation of dependency, elimination of redundancy, increase of consistency.
Identify the strengths and weaknesses of various abstract, rational concepts, such as issues, opinions, and approaches related to a specific problematic situation in order to formulate solutions and alternative methods of tackling the situation.
Collect and select a set of data from a population by a statistical or other defined procedure.
Apply quality analysis, validation and verification techniques on data to check data quality integrity.
Apply database design schemes and models, define data dependencies, use query languages and database management systems (DBMS) to develop and manage databases.
Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.
Enter information into a data storage and data retrieval system via processes such as scanning, manual keying or electronic data transfer in order to process large amounts of data.
Produce research documents or give presentations to report the results of a conducted research and analysis project, indicating the analysis procedures and methods which led to the results, as well as potential interpretations of the results.
Gather, process and analyse relevant data and information, properly store and update data and represent figures and data using charts and statistical diagrams.
No competences in this bucket.
The process of revealing data issues using quality indicators, measures and metrics in order to plan data cleansing and data enrichment strategies according to data quality criteria.
The use of qualitative and quantitative methods to analyse patterns in healthcare data to the aim of improving healthcare administration, quality in patient care and diseases diagnosis.
The computer language LDAP is a query language for retrieval of information from a database and of documents containing the needed information.
The computer language LINQ is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Microsoft.
The computer language MDX is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Microsoft.
The computer language N1QL is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Couchbase.
The computer language SPARQL is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the international standards organisation World Wide Web Consortium.
The visual representation and interaction techniques, such as histograms, scatter plots, surface plots, tree maps and parallel coordinate plots, that can be used to present abstract numerical and non-numerical data, in order to reinforce the human understanding of this information.
The computer language XQuery is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the international standards organisation World Wide Web Consortium.
Processes which an organisation applies to improve efficiency, set new objectives and reach goals in a profitable and timely manner.
The process of developing and constructing systems for implementing data collection and analysis at large scale.
The study of statistical theory, methods and practices such as collection, organisation, analysis, interpretation and presentation of data. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments in order to forecast and plan work-related activities.
No competences in this bucket.
Apply design principles for an adaptive, elastic, automated, loosely coupled databases making use of cloud infrastructure. Aim to remove any single point of failure through distributed database design.
Organise and execute audits in order to evaluate ICT systems, compliance of components of systems, information processing systems and information security. Identify and collect potential critical issues and recommend solutions based on required standards and solutions.
Establish a positive, long-term relationship between organisations and interested third parties such as suppliers, distributors, shareholders and other stakeholders in order to inform them of the organisation and its objectives.
Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
Maintain an overview of all the incoming tasks in order to prioritise the tasks, plan their execution, and integrate new tasks as they present themselves.
Collect data and statistics to test and evaluate in order to generate assertions and pattern predictions, with the aim of discovering useful information in a decision-making process.
Manage and plan various resources, such as human resources, budget, deadline, results, and quality necessary for a specific project, and monitor the project's progress in order to achieve a specific goal within a set time and budget.
Lead and guide employees through a process in which they are taught the necessary skills for the perspective job. Organise activities aimed at introducing the work and systems or improving the performance of individuals and groups in organisational settings.