Are you able to enrich your data using external and internal sources?

Most firms across all major industries today are grappling with the issues around their ability to integrate data from their proprietary systems, third party data providers and public sources in near real time to keep their analytics and data-driven insights relevant for the evolving needs of the business.

Using EXF's ML Framework and partnering with various external data providers across industries, we provide rich, actionable insights into market data by identifying anomalies. In this process, we also enrich the data provided by third party data providers.

EXF's ML Framework includes a library of Machine Learning and AI algorithms that are applied to reference data before the data is ingested into the Data Warehouse/Data Management platform. As a part of this offering, we enbale our customers to identify hidden rules, patterns, and duplicates that improve data integrity while adhering to DQ thresholds.

EXF Customer 360 - Sentiment Analysis:

EXF provides a platform to understand the emotional sentiments from comments posted by various users across social media platforms such as Twitter and Facebook. For any topic (Identified by hashtag), our application aggregates comments posted by the users under this topic. The built-in algorithms analyze whether the comments reflect positive, negative or neutral opinion and provide a summary of statistics.

EXF Provider MDM - NPI (National Provider Identifier) Alert:

Our NPI alert mechanism is used to track and record the updates of a Healthcare Provider based on unique NPI number. Once there is any change in provider details, geographic location, affiliations or services, our application will issue an alert and show attributes that are updated for the provider. We leverage Spark for large data volume processing as ETL which serves as a core for providing updates to our Healthcare MDM Platform solution.

EXF Insights - ML has driven data enrichment for capital markets:

We have developed a library of ML and AI algorithms that are applied to reference data and market data across Securities, Entities, Accounts, Instruments, Trades, Positions, Pricing, Corporate Actions etc. to provide insights before it is pushed into Data warehouse or Data management platforms.

We enable the sell-side and the buy-side players to identify hidden rules/patterns/duplicates/anomalies thus increase the confidence in the data and its adherence to data quality thresholds. Some use cases solved are around:
I. Build drop and drag analytical data models for risk and profitability calculations
II. Provide analytics for on-the-spot decision making by front-line personnel
III. Serve as a foundation for operational reporting, self-service analytics

Related

Are you able to enrich your data using external and internal sources?

Most firms across all major industries today are grappling with the issues around their ability to integrate data from their proprietary systems, third party data providers.

Are you able to free up your bandwidth from cost-saving projects to allocate more time for data innovation projects?

Firms of the future need to focus more on their core, mission critical processes than deploy armies of people on rudimentary, ‘Run the Business’ operations.

Are you able to keep up the health of your data under governance?

Setting up and implementing a Robust data governance framework is crucial to manage the ongoing cultural, procedural and structural impacts on the MDM initiatives.