Data Democratization on AWS

Global enterprises are struggling to find and utilize valuable data due to fragmented storage and limited discoverability. Traditional data management approaches create bottlenecks, thereby slowing down innovation.

Through this offer, Sigmoid enables data democratization, making quality data available to all relevant stakeholders in an organization, with self serve analytics capabilities, ensuring data driven decision making, fostering innovation and causing overall organizational growth. Sigmoid’s approach is focused on building data products and enabling self service through a data marketplace.

The decentralized data architecture approach of data mesh facilitates the creation of curated, easily accessible, domain oriented as well as cross domain data products that ensure interoperability, enabling downstream analytics and AI/ML initiatives. This is facilitated by creating an internal data marketplace, which acts as a centralized platform within an organization for data discovery, sharing, and governance of data assets across various departments and teams. Built on the guidelines of the FAIR principles of making the data more Findable, Accessible, Interoperable and Reusable, it operates much like a commercial data marketplace but is tailored for internal use, enabling employees to access and leverage data efficiently.

Sigmoid's Internal Data Marketplace offers the following key features:

1. ETL/ELT Integrations
2. Data Quality Governance and Cataloguing
3. Role Based Access Control
4. Self Serve Analytics Dashboards

Sigmoid has custom-built data connectors, developed using a Low-Code No-Code methodology to streamline the integration of multiple existing data sources with streaming data sources. Sigmoid also assesses and analyses the precursors needed for a successful implementation:

1. Stage of data maturity: The processes should be standardized and documented
2. Identified Data Challenges and Needs
3. Strategic Alignment of the Organization’s Goals
4. Should have necessary technological infrastructure and integration capabilities
5. Cultural and organizational readiness for successful adoption

The steps involved for a successful implementation:

1. Assessment, Scope Alignment & Planning - Stakeholder Identification; Understanding of existing data sets, organizational needs; Goal setting and feasibility analysis
2. Developing the Architectural Frameworks - Data Governance policies and procedures; Architectural considerations - Data Storage, cataloguing and access control; UI design elements
3. Building & Deploying the Marketplace - Integration of data sources into marketplace; deploying data quality tools
4. Testing, Monitoring and Continuous Improvement - Monitoring the performance of the marketplace, including usage metrics, data quality, and user satisfaction
5. Scaling Strategy and Publisher Sign-off - Developing a scaling strategy to accommodate more users, data sources, and advanced analytics capabilities

Key AWS workloads configured and optimized while building this solution:

1. AWS Glue: Orchestrating data integration pipelines to integrate data from diverse sources.
2. Amazon S3: Serving as the underlying storage layer
3. AWS Lake Formation: Unified data governance
4. Amazon SageMaker for Machine Learning
5. Amazon QuickSight for Business Intelligence

Benefits ensured by the successful implementation:

1. Agility - Faster development and iteration of data products.
2. Quality - Higher data quality due to domain expertise and ownership.
3. Efficiency - Reduction of bottlenecks associated with centralized data management
4. Innovation - Has empowered teams to experiment and innovate