Data analyst

Data analysts import, inspect, clean, transform, validate, model, or interpret collections of data with regard to the business goals of the company. They ensure that the data sources and repositories provide consistent and reliable data. Data analysts use different algorithms and IT tools as demanded by the situation and the current data. They might prepare reports in the form of visualisations such as graphs, charts, and dashboards.

Excludes people performing managerial, engineering, and programming activities.

The following job titles also refer to data analyst:

data analysts
data warehousing analyst
data storage analyst
data warehouse analyst

Bachelor’s degree is generally required to work as data analyst. 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 analyst is a Skill level 4 occupation.

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

data scientist
data quality specialist
chief data officer
computer scientist
ICT information and knowledge manager

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

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

Information structure: The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
Business intelligence: The tools used to transform large amounts of raw data into relevant and helpful business information.
Data mining: The methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset.
Documentation types: The characteristics of internal and external documentation types aligned with the product life cycle and their specific content types.
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.
Information extraction: The techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources.
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.
Information categorisation: The process of classifying the information into categories and showing relationships between the data for some clearly defined purposes.
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.
Information confidentiality: The mechanisms and regulations which allow for selective access control and guarantee that only authorised parties (people, processes, systems and devices) have access to data, the way to comply with confidential information and the risks of non-compliance.
Unstructured data: The information that is not arranged in a pre-defined manner or does not have a pre-defined data model and is difficult to understand and find patterns in without using techniques such as data mining.
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.
Data models: The techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships.

These skills are necessary for the role of data analyst.

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.
Interpret current data: Analyse data gathered from sources such as market data, scientific papers, customer requirements and questionnaires which are current and up-to-date in order to assess development and innovation in areas of expertise.
Establish data processes: Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
Execute analytical mathematical calculations: Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
Apply statistical analysis techniques: Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.
Perform data mining: Explore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.
Integrate ict data: Combine data from sources to provide unified view of the set of these data.
Analyse big data: Collect and evaluate numerical data in large quantities, especially for the purpose of identifying patterns between the data.
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.
Implement data quality processes: Apply quality analysis, validation and verification techniques on data to check data quality integrity.
Collect ict data: Gather data by designing and applying search and sampling methods.
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.

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

Web analytics: The characteristics, tools and techniques for measurement, collection, analysis and reporting of web data to get information on the users’ behaviour and to improve the performance of a website.
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.
Information architecture: The methods through which information is generated, structured, stored, maintained, linked, exchanged and used.
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.
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.
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.
Ldap: The computer language LDAP is a query language for retrieval of information from a database and of documents containing the needed information.
Data storage: The physical and technical concepts of how digital data storage is organised in specific schemes both locally, such as hard-drives and random-access memories (RAM) and remotely, via network, internet or cloud.
Online analytical processing: The online tools which analyse, aggregate and present multi-dimensional data enabling users to interactively and selectively extract and view data from specific points of view.
Cloud technologies: The technologies which enable access to hardware, software, data and services through remote servers and software networks irrespective of their location and architecture.
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.
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 analyst. However, mastering these skills and competences allows you to have more opportunities for career development.

Gather data for forensic purposes: Collect protected, fragmented or corrupted data and other online communication. Document and present findings from this process.
Manage data collection systems: Develop and manage methods and strategies used to maximise data quality and statistical efficiency in the collection of data, in order to ensure the gathered data are optimised for further processing.
Deliver visual presentation of data: Create visual representations of data such as charts or diagrams for easier understanding.
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.
Create data models: Use specific techniques and methodologies to analyse the data requirements of an organisation’s business processes in order to create models for these data, such as conceptual, logical and physical models. These models have a specific structure and format.

2511 – Systems analysts

 

 

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References

1. Data analyst – ESCO