Birlasoft helps a client to establish an Enterprise Data Lake and Cloud Migration, resulting in improved data operations by reducing errors and reworking by 40%.
The Challenge
Data accessibility presents organizations with significant challenges on account of the volume, variety, and velocity of data. To ensure that data is available and accessible to authorized users when they need it, organizations must eliminate issues such as data silos, lack of standardized formats, limited data integration capabilities, and inadequate data governance practices. Only then can they unlock timely decision-making and maximizes the value derived from data assets.
Quality data that ensures accuracy, completeness, consistency, and relevance is crucial for reliable business insights and effective decision-making. Challenges arise due to data entry errors, outdated information, inconsistent formats, and duplicate or conflicting data. Inadequate data validation processes, lack of data governance frameworks, and poor data integration practices complicate issues further. Organizations must invest in data quality management strategies – including data cleansing, standardization, and validation techniques – to address these challenges.
The convergence of data swamp, data management, and access management presents a challenge for organizations. Proper data management practices – including data governance, data cataloging, and data quality management –are critical to address this challenge. Additionally, appropriate access management is vital to protect sensitive data and maintain data privacy and security. Balancing data accessibility with data security requires robust access control mechanisms, user authentication, role-based permissions, and monitoring capabilities.
The Solution
The proposed solution encompasses several components aimed at enhancing data management and analytics capabilities. First, establishing an enterprise-level Central Data Repository enables data consolidation from various sources, facilitates multifunction analytics, and provides a unified view of organizational data. Second, using Data as a Service (DAAS) to embed data and analytics into core processes and decision-making, enabling stakeholders to leverage real-time insights. Furthermore, legacy data and data processing systems to the AWS cloud Data Lake to improve scalability, agility, and cost-efficiency. Finally, the solution focuses on ensuring data consistency and accuracy throughout the organization.
As part of the solution, a new data lake was established utilizing AWS S3 as the storage infrastructure. This served as the upstream source for the Snowflake data warehouse hosted on AWS. Access management to the Data Lake was ensured by leveraging AWS IAM users and roles, providing secure and controlled access. Code versioning was implemented using Bitbucket, ensuring efficient tracking and management of code changes. This solution effectively addressed the challenge of data swaps by eliminating redundancy and improving data governance. The centralized repository enabled streamlined data management, enhanced data quality, and facilitated efficient data analysis and decision-making.
The Impact
The solution transformed data operations by reducing errors and reworking by 40%. It also standardized and modernized technologies, equipping the organization to handle vast volumes of unstructured data. This opened up better access to diverse data sources, fostering partnerships, breaking down silos, and seeding cross-functional integration. The solution also introduced cost efficiencies, performance improvements, automated error handling, and a robust failover mechanism to unlock the full potential for business growth.
The solution transformed the organization's ecosystem, introducing platform stability and data consistency and ensuring reliable and accurate insights for decision-making. It also unlocked the true value of data to drive actionable outcomes. Low latency Extract, Transform, Load (ETL) processes using EMR guaranteed near real-time reporting and comprehensive insights. Data ingestion as a service accelerated the data onboarding time by 60%, equipping the organization with a foundation for sustainable growth.
The solution reduced platform rollout time by 40%, enabling faster time-to-market. By adopting standardized deployment patterns for analytics apps and automation, the organization has unlocked over 15% more team bandwidth, allowing teams to focus on driving innovation. Data governance, data quality, and accuracy are significantly higher, with automated data reconciliation promoting data integrity and consistency. Overall, the solution has established robust data governance practices and primed the organization for success.
