business analytics
The disciplines and technologies for solving business problems through employing quantitative methods such as data analysis and statistical models.
digital
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.
Competences and skills
67 ESCO relationsEssential competences 1 competence
Occupation specific
0 competencesNo competences in this bucket.
Sector-specific
0 competencesNo competences in this bucket.
Cross-sector
0 competencesNo competences in this bucket.
Essential knowledge 19 competences
Occupation specific
0 competencesNo competences in this bucket.
Sector-specific
13 competencesdata mining
The methods of artificial intelligence, machine learning, statistics and databases used to extract content from a dataset.
digital
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.
digital
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.
digital
documentation types
The characteristics of internal and external documentation types aligned with the product life cycle and their specific content types.
information categorisation
The process of classifying the information into categories and showing relationships between the data for some clearly defined purposes.
digital
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.
digital
information extraction
The techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources.
digital
information structure
The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
digital
query languages
The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.
digital
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).
digital
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.
digital
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.
digital
Cross-sector
6 competencesbusiness intelligence
The tools used to transform large amounts of raw data into relevant and helpful business information.
digital
data engineering
The process of developing and constructing systems for implementing data collection and analysis at large scale.
digital
data ethics
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.
data science
The field of study that deals with big amount of data using AI techniques such as machine learning algorithms to predict patterns and obtain useful information to make business decisions
digital
data visualisation software
A computer tool or application that creates a graphical and visual representation of data, allowing a better understanding and interpretation of complex data through visual elements such as maps, charts, infographics or graphs.
digital
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.
Essential skills and competences 16 competences
Occupation specific
0 competencesNo competences in this bucket.
Sector-specific
6 competencesdefine 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.
digital
integrate ICT data
Combine data from sources to provide unified view of the set of these data.
digital
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.
digital
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.
digital
perform data mining
Explore large datasets to reveal patterns using statistics, database systems or artificial intelligence and present the information in a comprehensible way.
digital
- Scope note
- Includes activities on big data.
Cross-sector
10 competencesanalyse big data
Collect and evaluate numerical data in large quantities, especially for the purpose of identifying patterns between the data.
digital
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.
digital
collect ICT data
Gather data by designing and applying search and sampling methods.
digital
execute analytical mathematical calculations
Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
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.
digital
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.
perform data cleansing
Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.
digital
use data processing techniques
Gather, process and analyse relevant data and information, properly store and update data and represent figures and data using charts and statistical diagrams.
digital
use databases
Use software tools for managing and organising data in a structured environment which consists of attributes, tables and relationships in order to query and modify the stored data.
digital
Optional knowledge 21 competences
Occupation specific
0 competencesNo competences in this bucket.
Sector-specific
13 competencescloud technologies
The technologies which enable access to hardware, software, data and services through remote servers and software networks irrespective of their location and architecture.
digital
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.
digital
Hadoop
The open-source data storing, analysis and processing framework which consists mainly in the MapReduce and Hadoop distributed file system (HDFS) components and it is used to provide support for managing and analysing large datasets.
digital
healthcare analytics
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.
information architecture
The methods through which information is generated, structured, stored, maintained, linked, exchanged and used.
digital
LDAP
The computer language LDAP is a query language for retrieval of information from a database and of documents containing the needed information.
digital
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.
digital
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.
digital
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.
digital
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.
digital
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.
digital
statistical modeling techniques
The approaches for employing statistical analysis to dataset within the data science field. It seeks to elaborate reality predictions through statistical models and explicit assumptions.
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.
digital
Cross-sector
8 competencesdatabase
The classification of databases, that includes their purpose, characteristics, terminology, models and use such as XML databases, document-oriented databases and full text databases.
digital
game theory
The study of mathematical models of strategic decisions among stakeholders. It has applications in all fields of social science, as well as in systems science, computer science and logic. Essential concepts are the Nash Equilibrium and the Prisoner's Dilemma.
image recognition
A process designed to detect and identify a feature or object in an image or video. This process is used in medical imaging, security surveillance or defect detection, among other fields. Key technique for a wide range of applications such as automated driving, image classification, or visual inspection.
digital
marketing analytics
The set of processes for employing data to improve the effectiveness of marketing activities. It involves analysing metrics such as the Return on Investment (ROI) for identify opportunities of improvement.
digital
multidisciplinary research
The research technique where a common issue is investigated using approaches from different disciplines with the aim of finding a comprehensive solution to it.
research design
The set of methods and techniques of research that are used to conduct a study. It includes practical steps in research such as purpose statement, data collection, methodology, and data analysis.
social network analysis
The research method that is mainly explored in sociology and communication science and focuses on the analysis of the relations between individuals and among organisations and states.
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.
digital
Optional skills and competences 10 competences
Occupation specific
0 competencesNo competences in this bucket.
Sector-specific
4 competencescreate 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.
digital
gather data for forensic purposes
Collect protected, fragmented or corrupted data and other online communication. Document and present findings from this process.
manage cloud data and storage
Create and manage cloud data retention. Identify and implement data protection, encryption, and capacity planning needs.
digital
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.
digital
Cross-sector
6 competencesdeliver visual presentation of data
Create visual representations of data such as charts or diagrams for easier understanding.
digital
make data-driven decisions
Collect data such as Key Performance Indicators (KPIs) for an organisation and use the information to formulate actions and strategies.
manage quantitative data
Gather, process and present quantitative data. Use the appropriate programs and methods for validating, organising and interpreting data.
digital
- Scope note
- Excludes .
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.
store digital data and systems
Use software tools to archive data by copying and backing them up, in order to ensure their integrity and to prevent data loss.
digital
use spreadsheets software
Use software tools to create and edit tabular data to carry out mathematical calculations, organise data and information, create diagrams based on data and to retrieve them.
digital