DATA MODERNIZATION

Moving
from legacy
to modern

The cornerstone of a digital enterprise

Gain a competitive edge in today's fast-paced world of innovation, and meet business objectives effectively through advanced data platforms. We help businesses tackle issues related to data fragmentation, silos, scalability, and inefficient warehousing. With our data modernization solutions, organizations can unify, mobilize, govern, understand their data, and create a single source of truth.

Our Offerings

Shift to new-age data platforms

Experience the data value through our data modernization services.

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Current state and gap analysis

Evaluate current data platform and identify gaps for improvement,
Data platform strategy

Roadmap for design, implementation, and execution strategies for new data platform.
Data sourcing

Ingest data from different sources such as on-premise and cloud, using various connectors.
Centralized data storage

Establish a cost-effective method to retain historical data while ensuring intraday data updates.
Data transformations & business rules

Maintain data consistency and quality to establish a reliable source of information for all business users.
Industry-standard data warehouse model

Industry-standard models that are readily available to fulfill both operational and analytical requirements
Self-service analytical capabilities and BI reporting

Self-service business intelligence capabilities that empower business users to implement analytics use cases with minimal reliance on data engineers.
Data science use cases

Enable data scientists to use AI/ML algorithms for predictive analytics and uncovering hidden patterns in data.
Data managed services

Ensure that operations run smoothly, and that data is reliable and available in a timely manner.

Key components of data modernization

Data Virtualization

Utilize advanced integration capabilities to access data without replication. Analyze unified data in real-time, and feed data to downstream applications and BI reporting tools.

Data Quality

Establish data quality metrics, criteria and assessment rules for data completeness, reliability and consistency. Automate testing based on user-defined rules

Data Governance

Create robust governance policies that encompass people, processes, and technologies to guarantee data reliability, completeness, accountability, and compliance.

Data Fabric

Leverage the combination of ML and knowledge graphs to integrate enterprise data and maximize business value. Easy access to quality data for analytics with intelligent integration and data catalogs, engineering, and governance.

Data Mesh

Implement a distributed data architecture and decentralized data management that transfers data responsibility to respective domains. Enabling secure, easy, and quick data access.

Data Lakehouse

Merge data lakes and data warehouses, providing inexpensive data storage, minimized data transfer, and access updated data, as well as unified data management and governance.

Metadata Management

Consolidate all metadata in one location. Enhance data quality, boost operational efficiency by reducing duplication, and leverage data to make informed decisions.