The Structure of the Data Management Platform
Methodology
Description of information objects, data management rules and procedures.
- Data business
model - Norms, Rules, Instructions
Technical Platform
Automation of rules and procedures to ensure data quality.
- Data validation
- Meta data
- Information security
- Normative and reference documentation
Transaction
data
Structured data warehouses
Unstructured data and partially structured data
The Platform Capabilities
- Data visualization in dashboards for selected business metrics
- Tracking unreliable data and defining their source without contacting technical support
- Choosing a proper data source for calculating specific metrics
- Analytics of selected metrics with tracking the dynamics of the company business growth
- Prediction of adverse business situations and their timely prevention
Implementation advantages
- The graphic interface boosts the performance of data engineers, data scientists, and business analysts
- A wide range of tools for data handling and AI
- Secure container platform based on RedHat OpenShift
- Full life cycle for AI data
Get a preliminary calculation and estimate for the project
Implemented projects
Domain area: turnover balance sheet and Form 101 submitted to the Central bank of RF
The product has the following modules:
- Business glossary, data management policy and rules
- Automated business processes for data management
- Relationship between business terms and physical data sources
- Tools for testing data quality, monitoring reports
- Transition of the Bank to risks calculation by the IRB approach
- Adherence to data quality requirements set by the Central Bank of the Russian Federation
Domain area: risk estimation and management
The developed data management tool includes:
- Business glossary, data management policy and rules
- Automated business processes for data management
- Relationship between business terms and physical data sources
- Tools for testing data quality and monitoring reports
- Adherence to the Bank’s methodology
- Geographically distributed points of data generation and processing
- Necessity of stock-taking and setting rules for data processing
- Bonus program data management
- Adherence to GDPR
- Better processing of transactions for accrual and write-off of miles due to regular monitoring and error prevention
- Lower overheads due to automatic identification and deleting of personal data
- Data monetization due to targeted advertising and using the knowledge about passengers
Our skills
Proprietary methodology of data management implemented in TOP-40 companies
Hands-on experience of implementation in finance and transportation industries and B2C companies
5 years of implementing Enterprise and Open Source products
Implementing turnkey projects, from the need and task identification to implementation and subsequent support of the solution
Certified Data Architect - Big Data specialists in the field of data management (Integration, Quality & Governance)
