Integrated data management and analytics to optimally service business processes and end users
Data quality management to prepare data for use in valuation and risk models and machine learning
Complete data lineage to trace data back to its original sources, show data derivations and explain model results

Data Management & Analytics

An integrated approach

Data management processes typically involve data sourcing, cross-referencing, and ironing out any discrepancies via reconciliations and data cleansing. Data analytics processes generally are carried out afterward in various desk-level tools and libraries, close to the business users and typically operating on separately stored data subsets.

Full Transparency

Improve business user productivity

Alveo provides both an easy to use environment for business users to access validated data and create analytics, as well as a range analytics created within Alveo. Our cloud-native, bitemporal data warehouse combines full transparency on sourcing, data preparation, data lineage and quality metrics with external libraries and native R and Python integration. Data can be accessed via our user interface and REST API as well.

Take Data into your own hands

A self-service model

Alveo empowers business users to define custom rules for derivation and validation and onboard new data without the need for IT. Alveo manages price consolidation and construction of derived data such as curves and allows business users to access market data directly using open-source technology using Apache Spark and notebooks.

Our unique structured approach to securing high-quality market data and validated price information has proven to be highly effective for many of the world’s most notable financial services firms.

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Integrated data management and analytics – Why it’s the future for financial services

New research commissioned by Alveo, polling data scientists in financial services firms across the UK, US, and Asia, highlights the scale of the challenge. In the Alveo survey, just 35% of data scientist respondents said the data management systems and processes they use today are ‘very effective’ at supporting business and operational decision-making.

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