What is AI and ML?
Artificial Intelligence is the application of computer science techniques to perform a range of decision-making and prediction activities.
A collection of common terms, themes and topics used in the data management ecosystem.
Artificial Intelligence is the application of computer science techniques to perform a range of decision-making and prediction activities.
Environmental, Social and Governance refers to a collection of criteria used to evaluate an organisation’s operations and measure their sustainability.
KYC and AML are fundamental components of regulatory compliance in financial institutions, referring to the prevention of money laundering and other financial crimes.
Customer 360 refers to a 360-degree view of a customer’s journey through an organisation, including accounts, interactions and enquiries.
Microsoft Power BI is a technology-enabled business intelligence platform for gathering, analysing and visualizing data.
Metadata is a way to describe and make sense of data. It’s a shorthand representation of the data, which helps data stewards easily understand the information in front of them.
Data profiling is the process of reviewing data, including its source, to provide helpful summaries of information about the data, including potential data quality issues.
Launched in 2010, Microsoft Azure is one of the world’s leading public cloud computing software, offering over 200 preconfigured services including AI, storage, networks and integration.
Self Service Data Governance (SSDG) is an enterprise-wide initiative incorporating data disciplines such as data lineage, data quality and data analytics.
A data catalog is an inventory of an organisation’s data assets, which can be accessed by data stewards and scientists in order to quickly retrieve the information they need.
Data lineage is the process of mapping where your data has originated from and where it ends up. It enabled enterprises to track the flow of data and quickly identify where errors have occurred during the data lifecycle.
Data governance refers to a collection of disciplines working together to achieve effective use, monitoring and reporting of enterprise data.
Master Data Management provides a consolidated, single source of truth for business-critical data.
Data integration is the process of connecting various data sets into one consolidated view, often in a data warehouse or cloud solution.
Data quality refers to how fit your data is for serving its intended purpose. Good quality data should be reliable, accurate and accessible.