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...data or data related processes with a view of establishing the level of [[Data Quality]]. The precise elements depend heavily on context. === Data Gathering, Input and Handling Activities === ...

''Progress in adopting the Principles for effective risk data aggregation and risk reporting''. ...The Principles, issued in January 2013, aim to strengthen banks' risk data aggregation and risk reporting with a view to improving their risk management, decision ...

...''' (also ''Data Integrity'') refers to the condition of information sets (data) that are to be used as inputs for qualitative or quantitative risk assessm Regulated institutions are required to have in place a formal [[Data Quality Management Framework]]<ref>ECB guide to internal models - Credit Ri ...

'''Territorial GHG Accounting''' is the collection and aggregation of multiple [[GHG Inventory | GHG inventories]] by combining only the scope ...ation sector in particular, policy makers should seek to collect emissions data from cities based on comparable methods. For instance, the fuel sales metho ...

== Aggregation Levels == [[Category:Reference Data]] ...

'''GHG Emissions Aggregation''' is the process of summarizing an entities total [[GHG Emissions]] from a ...existing reporting tools and processes, and take advantage of any relevant data already collected and reported by facilities to division or corporate offic ...

...rmation flows ([[Data Generation Process]], [[Data Sourcing]], [[Risk Data Aggregation]]) within the organization that are relevant for [[Credit Risk Management]] ...(usually off-the-shelf) software and hardware components comprising the [[Data Infrastructure]] are ...

'''IT Data Integrity Risk''' is the risk that data stored and processed by IT systems are incomplete, inaccurate or inconsiste ...controlling data extractions, transfers and processing, including rendered data outputs), impairing the ability of an institution to provide services and p ...

* [[Risk Data Aggregation]] ...

...is performed as scheduled. The following is the hierarchy of all possible aggregation levels of credit risk, starting with the most elementary level and ending u * While some layers of aggregation are fairly natural (e.g. all exposure to one legal entity) others may admit ...

...its simplest form, the risk silo phenomemon may hinder efforts for [[Risk Aggregation]], that is collecting and compiling a complete overview of exposure to cert * [[Risk Data Aggregation]] ...

* The Factual Basis (Data) underpinning the framework ** Portfolio Aggregation Consistency ...

...ices as articulated by the BIS<ref>BIS, Principles for effective risk data aggregation and risk reporting, 2013</ref> ...management reports should accurately and precisely convey aggregated risk data and reflect risk in an exact manner. Reports should be reconciled and valid ...

== Data Requirements == ...concentration requires detailed information about [[Exposure]] (loan level data, accurate sector assignement etc.) ...

...nts fall generally into the following categories, grouped according to the aggregation level at which the constraints apply * Data-Type Constraints. Values in a particular table column must be of a particul ...

...ibuted to high compilation costs, lack of physical data at the appropriate aggregation levels, lack of reproducibility and continuity in available datasets along ...

Stratification is the sorting of data attributes concerning any group of entities (people, objects etc) into dist ...in the understanding and communication of the information content of such data sets. ...

Assuming the exposure data are given, it is easy to calculate the HHI by first forming the fractional * Relatively moderate data requirements (but see qualification below) ...

...arching and sector-specific accounting and reporting guidance for sourcing data and calculating emissions, including calculation methods and equations ...facilitate insight through benchmarking - and aggregation - of comparable data. ...

...categories of models (which generally overlap) and very many variations in data inputs, model specification and model calibration: ...he losses for each obligor, given the default / migration variable and the aggregation of all exposures to that obligor ...