Profession data quality specialist

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.

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Personality Type

  • Conventional / Investigative

Knowledge

  • Query languages

    The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.

  • Resource description framework query language

    The query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).

  • Database

    The classification of databases, their purpose, terminology, characteristics, models and use such as document-oriented databases, XML databases and full text databases.

  • Information structure

    The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.

Skills

  • Handle data samples

    Collect and select a set of data from a population by a statistical or other defined procedure.

  • Implement data quality processes

    Apply quality analysis, validation and verification techniques on data to check data quality integrity.

  • Establish data processes

    Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.

  • Define data quality criteria

    Specify the criteria by which data quality is measured for business purposes, such as inconsistencies, incompleteness, usability for purpose and accuracy.

  • Process data

    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.

  • Manage database

    Apply database design schemes and models, define data dependencies, use query languages and database management systems (DBMS) to develop and manage databases.

  • Manage data

    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.

  • Manage standards for data exchange

    Set and maintain standards for transforming data from source schemas into the necessary data structure of a result schema.

  • Perform data cleansing

    Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.

  • Report analysis results

    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.

  • Address problems critically

    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.

  • Utilise regular expressions

    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.

  • Normalise data

    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.

  • Design database scheme

    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.

Optional knowledge and skills

perform data analysis n1ql execute analytical mathematical calculations sparql build business relationships manage schedule of tasks execute ict audits visual presentation techniques ldap statistics linq train employees continue learning xquery mdx perform project management business processes data quality assessment

Common job titles

  • Data quality specialist
  • Data quality specialist - aus
  • Quality data specialist
  • Data quality specialist 1
  • Quality data collection specialist
  • Data quality specialist - edi management
  • Field data specialist