Data integration involves combining data residing in different sources and providing users with a unified view of data. This process becomes significant in a variety of situations, which include both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories) domains. Data integration has increased as the volume and the need to…
Products: Ready To Go!
- Ready to Go – Zero Capex and Low Opex model
- End-to-end product Solutions that uses scalable computing, distributed computing and parallel processing (Big Data)
- Adaptable products which complement existing solutions
Solutions: Results Delivered!
- Industry Focus: Solutions focused for banking industry
- Unique business model that utilizes cost effective technology and human resources to deliver results
- Fees based on results delivery
Converging & Unifying Data from Multiple Sources
Unique solution based on Hadoop framework for integrating, consolidating, aggregating and managing structured or unstructured data from disparate sources.
- Data profiling
- XBRL reporting
- Data normalization
- Data consolidation
and utilize Big Data to
comply with AML
- Real-time screening
- Client on boarding
- Reduce false positives
- Single view of customer
- Behavior Drivers Analytics
- Real-time Deduplication
- Internal Audit
(mortgage, credit etc)
Sanitize data to get
one view of customer
- Data Quality
- Data Integration
- Data Profiling
- Data Mining
HEXANIKA POC – Large European Bank
NAME: Global Banking & Financial Services Company
SIZE: 100,000 employees in 70 countries
INDUSTRY: Financial Services
- Problems in reconciliation of same transaction being passed over and presented in multiple data streams
- Analysts were manually identifying duplicate and inconsistent entries in excel spreadsheets of security sales data.
- Data lacked standard naming conventions and other attributes like client names, status, unique ID
Situation: Manual Reconciliation.
Help client reconcile data from various different data streams and identify outliers which cannot be identified. Two data streams with similar transaction information but data is presented differently and there is no unique identifier.
Solution: DRaaS + SmartJoin
Multilevel semantic join based on Hadoop i.e. two hoops of jump and then get joins from there. The schema is for ‘MultiJoin’. Configurable rules engine for validation of incoming and outgoing data and create rules for project
Approximately 25% resource savings identified as currently 75+ people are doing this exercise manually
VC FinTech Demo Day - HEXANIKA
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