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.

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.

• Information structure

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

• 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, that includes their purpose, characteristics, terminology, models and use such as XML databases, document-oriented databases and full text databases.

Skills

• Perform data cleansing

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

• 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.

• Implement data quality processes

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

• 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.

• 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.

• 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.

• 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.

• Manage standards for data exchange

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

• 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.

• Handle data samples

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

• 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.

• 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.

• Establish data processes

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

• Manage database

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

Optional knowledge and skills

Source: Sisyphus ODB