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.

Excludes people performing managerial and development activities.

The following job titles also refer to data quality specialist:

data quality specialists
data quality expert
data integrity officer
data quality officer
data integrity specialist

Bachelor’s degree is generally required to work as data quality specialist. However, this requirement may differ in some countries.

ISCO skill level is defined as a function of the complexity and range of tasks and duties to be performed in an occupation. It is measured on a scale from 1 to 4, with 1 the lowest level and 4 the highest, by considering:

• the nature of the work performed in an occupation in relation to the characteristic tasks and duties
• the level of formal education required for competent performance of the tasks and duties involved and
• the amount of informal on-the-job training and/or previous experience in a related occupation required for competent performance of these tasks and duties.

Data quality specialist is a Skill level 4 occupation.

These occupations, although different, require a lot of knowledge and skills similar to data quality specialist.

data scientist
data analyst
computer scientist
ICT test analyst
chief data officer

These occupations require some skills and knowledge of data quality specialist. They also require other skills and knowledge, but at a higher ISCO skill level, meaning these occupations are accessible from a position of data quality specialist with a significant experience and/or extensive training.

This knowledge should be acquired through learning to fulfill the role of data quality specialist.

Information structure: The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
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.
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).
Query languages: The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.

These skills are necessary for the role of data quality specialist.

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.
Manage database: Apply database design schemes and models, define data dependencies, use query languages and database management systems (DBMS) to develop and manage databases.
Establish data processes: Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
Handle data samples: Collect and select a set of data from a population by a statistical or other defined procedure.
Perform data cleansing: Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.
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.
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.
Implement data quality processes: Apply quality analysis, validation and verification techniques on data to check data quality integrity.
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.
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.
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.
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.

This knowledge is sometimes, but not always, required for the role of data quality specialist. However, mastering this knowledge allows you to have more opportunities for career development.

Mdx: 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.
Visual presentation techniques: 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.
Xquery: 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.
Sparql: 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.
Statistics: 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.
Business processes: Processes which an organisation applies to improve efficiency, set new objectives and reach goals in a profitable and timely manner.
Ldap: The computer language LDAP is a query language for retrieval of information from a database and of documents containing the needed information.
Linq: 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.
Data quality assessment: 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.
N1ql: 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.

These skills and competences are sometimes, but not always, required for the role of data quality specialist. However, mastering these skills and competences allows you to have more opportunities for career development.

Manage schedule of tasks: 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.
Perform data analysis: 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.
Execute analytical mathematical calculations: Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
Execute ict audits: 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.
Perform project management: 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.
Build business relationships: 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.
Train employees: 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.

2519 – Software and applications developers and analysts not elsewhere classified

 

 

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References

1. Data quality specialist – ESCO