ResearchPool

DMS-large

Data Management System

DMS

Designed to empower organizations to automate and streamline data acquisition and management, while helping their users to make better sense of the available data for better investment decisions. Our solution powered by Microsoft Azure Data Services facilitates the aggregation, storage, organization, and maintenance of both structured and unstructured data. It provides a scalable, reliable, efficient, and secure workflow across the data lifecycle.

KEY FEATURES

  • Collection: Seamless and multi-channel data aggregation via sFTP, APIs, bulk upload, web scrapping, etc…from internal and external sources (multi-vendor) into a single database
  • Storage: Secured and scalable storage of different types of data structured and semi structured and unstructured
  • Quality Control: Semi-automated techniques to ensure data accuracy and consistency
  • Transformation: Capacity to transform stored data into a usable format for analysis
  • Visualization and Reporting: User friendly and flexible interfaces with customizable tiles and tables to match needs and requirements
  • Enrichment: By adding detailed field metadata and combining static and dynamic data
  • Access control: Encryption and authentication to protect sensitive data from unauthorized access
  • Datasets: Comprehensive and rich Referential, Financial and ESG datasets, easily integrable via our Applications, API, sFTP and emailing.

Delivered Benefits

Easy data retrieval and access to accurate, consistent, and reliable data in one single place, fostering productivity and collaboration

Better decision-making by having enriched and organized data in a structured and unified manner that enables users to easily make sense of it

Protection of sensitive information, by controlling access to permissioned users only

Cost Efficiency by optimizing data storage, identifying, and reducing redundancies, and streamlining processes

Compliance with data regulations and standards by managing data in accordance with legal requirements

Removed Pain-Points

data dispersed

Data dispersed across systems and groups hindering collaboration

pour data quality

Poor data quality reducing user trust and induce flawed decisions

inefficient data

Inefficient data retrieval which time can be time consuming

lack data

Lack of data insights by not presenting the data in a way that makes sense to users

Access and Delivery

Desktop

Desktop

iFrame

iFrame

Questions? Let's discuss!