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Data Discovery
In the realm of data management, one crucial aspect is Data Discovery. Data Discovery is the process of systematic exploration and understanding of data within an organization. This involves aspects like discovering various entities within the data, their relationships with each other, and understanding master data vs transactional data. It also involves understanding sources of data, their formats and change rates. Understanding these sources and the frequency of change is essential for maintaining data accuracy and relevance. In parallel, we have Data Quantity Estimation which deals with assessing the volume of data, both historically and as it accumulates incrementally. Accurate estimation of data quantity aids in capacity planning and resource allocation, ensuring that the data infrastructure can effectively handle the load. Together, these components under Data Discovery are fundamental in guiding data management strategies within an organization.
We have helped multiple organizations across different industries build and optimize their data architecture to solve their most critical business requirements.
Data Architecture
Multiple factors need to be considered while designing a Data Architecture. These include - Business Goals and Objectives
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Data Requirements - what are the input data formats & sources. What are the data SLAs - based on this we can decide if we need stream processing or batch processing for the data.
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Data Governance & Data Security to ensure data quality, security, compliance, and privacy. Define roles and responsibilities for data stewardship (access control). Measures to protect data from unauthorized access, breaches, and data leaks.
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Data Integration - how data will flow through the organization. Consider data integration tools, data pipelines, and ETL (Extract, Transform, Load) processes
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Scalability - Ensure that the architecture can scale as data volumes and processing needs grow,
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Data Quality - Establish data quality standards and data cleansing processes to maintain accurate and reliable data. Address data anomalies, inconsistencies, and duplicates.
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Data Storage - Select appropriate data storage technologies and databases (relational, NoSQL, data lakes, etc.) based on the specific data requirements and use cases,
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Data Access & Data Performance - Define how users and applications will access data. Consider APIs, query languages, and data access patterns to optimize performance and usability. Optimize data retrieval and processing performance. This includes indexing, caching, and query optimization
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Data Lifecycle Management - Plan for data retention, archival, and deletion.
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Cost Considerations: Evaluate the cost of implementing and maintaining the data architecture, considering factors like hardware, software, licensing, and operational expenses.
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Data Lake, Data Warehouse & Data Mart Design - Based on all above considerations a design for Data Lake, Warehouse and Data Mart is finalized. It is ensured that data lineage is maintained throughout the data architecture so that any data can be traced back to its original source and there is no loss of information during the data transformation process.
Case Studies
Analytics SaaS Platform for the Hospitality Industry
JashDS developed a scalable, multi-tenant SaaS analytics platform for a hospitality client, consolidating data from disparate management systems and reducing data processing time by 75%. The solution incorporated advanced ETL pipelines, a secure data warehouse, and interactive dashboards, enabling rapid, data-driven decision-making across multiple hotel properties.
