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Data Governance and Engineering

Our Methodology

innovation

Data Security

Protect the data, in transit and at rest, with encryption, managed keys, and folder-level access control lists (ACLs). Establish access control with object-level security, multi-factor authentication, and network security. Implement a set of control and audit procedures to ensure consistent compliance. Reducing operational complexity and meeting privacy regulations.

innovation

Data Lineage

Understand, record, and visualize the data as it flows from the initial to the consumption stage. Identify the data origin and visualize the source-to-end user data flow. Augment the data value by mapping the assets and their interrelationship across the data ecosystem. Make better decisions by managing data input from the cloud.

innovation

Metadata Management

Align the metadata management discipline with the data map, and establish data lineage. Using data definitions for all the entities and attributes, navigate the architectural relationships between objects. Build the data catalog and document the entire set of data rules and procedures. Streamline data consistency, access insights faster, and reduce costs.

innovation

Data Engineering

Govern the data collection, storage, integration, and usage by defining models, policies, rules, and standards for the organizational data ecosystem. Analyze data requirements that support business processes in tune with the information systems. Build data pipelines that ensure end-to-end data processing.

Blogs

Blog 1 Empowering Smarter Business Decisions with Cloud-Based Data Intelligence
Blog 2 The Convergence of Cloud Computing, Data Analytics, and AI in Enterprise IT
Blog 3 Cracking the Code of Cloud Analytics: Risks We Solved, Value We Unlocked

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