Overview

ATOME: Matter is a data model management platform designed to map policy requirements into precise, easily navigable, traceable and clear data concepts. It is used to collaboratively create, review, edit, version or extend data models and integrate them immediately with IT environment.

Sample Use Cases

Cyber risk taxonomy
Structured data for AI
STP for granular data
Single Data Dictionary

Better cyber risks understanding to enable compliance, build cyber-threat intelligence and strengthen operational resilience.

 

PROBLEM:
World Bank: “Cyber risk has emerged as a key threat to financial stability. (…) Data on cyber risk is notoriously scarce since there is no common standard to record them.”
Moody’s and S&P warn, that cyber risks evaluation could impact their credit ratings. Ability to understand and map cyber risks and translate risk exposure into quantitative measurements is in focus for regulators and the market. Proper data is needed to comply, build cyber-threat intelligence and strengthen operational resilience. But cyber-security data is still fragmented, unstructured or poorly defined.

SOLUTION:
A uniform organisation-wide cyber-threat taxonomy and model – not only for IT but for business-oriented professionals – bring common ground for interpreting, reporting, mapping and analysing actual risks and potential losses. They streamline the understanding and interpretation of cyber incidents and risks.

VALUE:
Understandable, extensible and organised data model for incident reporting, risk assessment and management, developed using ATOME: Matter will enable more dynamic defences and leverage cyber threat intelligence. Greater understanding of cyber incidents will improve company’s operations and allow for informed decision-making and communication at the management level.

Metadata-driven agile solution to transform understanding of your information assets and enable valuable input for your AI and ML projects.

 

PROBLEM:
Poorly defined and difficult to understand input for ML/AI algorithms lowers the usefulness of output. Scarcity of expert resources required to prepare input slows the development and automation offered by ML/AI. Data locked in dispersed models and multiple formats fails to present a full potential and makes poor input for AI and ML algorithms slowing the automation and digitisation.

SOLUTION:
ATOME: Matter metadata-driven and agile platform brings structure to data and transforms the understanding of information assets in an easy and collaborative way. With data models that are immediately deployable on the IT side you ultimately achieve input to feed into your ML and AI projects, tools and databases.

VALUE:
Structured data for human AI and ML – connects the human know-how to the power of AI and ML – fosters AI and ML ideas giving you an edge in maturing into a machine learning and AI-enabled world – gives easy and low cost data integration capabilities – engages business experts along with IT and data architects to make your ML/AI work.

Standardised end-to-end data interpretation to automate operations and carry your data over to desired granularity and interconnectedness.

 

PROBLEM:
High volumes of granular data are generated by internal systems of financial organisations. Regulators increasingly demand quality granular data inputs or seamless links to regulatory, aggregated reports.

SOLUTION:
With the straight-through processing (STP) approach internal data is streamlined into aggregated or regulatory data structures and requirements. Standardised data definitions applied consistently to various data sets ultimately lead to lean, simplified, end-to-end automated processes.

VALUE:
A high STP ratio for internal data simplifies and accelerates internal processes and improves the quality of data. It boosts productivity as well as establishes new capacities to use and reuse data for value creating tasks. It allows increasing profitability by taking resources off manual and time-consuming data preparation workarounds.

Single Data Dictionary for financial institutions.

 

PROBLEM:
Abundance of financial definitions and concepts lead to ambiguous interpretation and poor-quality data.

SOLUTION:
ATOME: Matter is a data management platform to map complex regulations (like CRD IV, Solvency II, BoE NST, CBI NST) into precise, easily navigable, traceable and clear data concepts, fostering the establishment of a shared and robust single base dictionary.

VALUE:
Unequivocal interpretation of definitions, common language, business-friendly definitions to improve data quality and lower the costs of data processing.

Model management

Creating or extending the imported data model.

COMMENT SYSTEM

Exchanging opinions between users by prioritised messages.

DICTIONARY BUILDING

Creating new dictionary elements and modifying already-existing concepts.

REPORTING REQUIREMENTS MANAGEMENT

Creating and modifying reporting requirements.

Technical interoperability

Exporting data models to international standards i.e. XBRL, JSON, CSV, SQL and spreadsheets or accessing via API. Importing of SQL database via API.

DEFINING BUSINESS RULES

Specifying business rules for the correctness of data.

Partnership

We are open to various level of cooperation.

User

Get the solution that supports data management process in your organisation.