Frequently Asked Questions

Quantitative finance applies advanced mathematical, statistical, and computational methods to solve complex problems in finance, such as risk management and portfolio optimization.
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Refers to the idea that a system's properties are best understood by viewing the system as a whole rather than just by its individual parts.
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Asset management involves the balancing of costs, opportunities and risks against the desired performance of assets to achieve an organisation's objectives.
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A hedge fund is a pooled investment fund that holds liquid assets and that makes use of complex trading and risk management techniques to aim to improve investment performance and insulate returns from market risk.
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Social economics, also known as socioeconomics, is a branch of economics that examines how social behavior and economic activity influence each other within a society.
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A Virtual Twin is a dynamic digital representation of a physical asset, process, or system that is used to simulate, analyze, and optimize its real-world counterpart throughout its lifecycle.
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A data source is simply the source of the data. It can be a file, a particular database, or even a live data feed.

A time series is a series of data points listed or graphed in time order.
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Socio-economic indicators are defined as analytical tools that provide quantifiable information to assess social performance and manage complex sustainable development issues related to various stakeholders and their socio-economic contexts.
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Data harmonization involves transforming datasets from different sources to be consistent in their meaning (semantics) and structure (syntax), making them comparable and usable for unified analysis.
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Data curation is the organization and integration of data collected from various sources. It transforms raw data into reliable, high-quality datasets by involving active management of data quality, collection, cleaning, organization, annotation, and storage.
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Data lineage is the process of tracking the entire life cycle of data, detailing its origins, transformations, and ultimate destination, providing a complete audit trail of how data moves and is changed across systems.
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Data observability refers to the practice of monitoring, managing and maintaining data in a way that ensures its quality, availability and reliability across various processes, systems and pipelines within an organization.
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Data imputation is a technique for handling missing values in a dataset by replacing them with estimated values, allowing for complete data analysis and preventing issues like bias, loss of statistical power, and errors in machine learning models.
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Risk analysis is the process of identifying potential future events (risks) that could negatively affect a project or organization, assessing their likelihood of occurrence, and understanding their potential impact, in order to develop strategies to mitigate or manage them effectively.
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Scenario analysis is a strategic planning tool used by businesses and financial experts to understand how multiple plausible future events could impact their performance, decisions, or systems.
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