The client is a US-based management and consulting firm that supports various industry leaders in effectively managing their business data. Their AI-driven platform transforms raw data into actionable insights. Business owners can use the platform’s sophisticated analytics engine to process and analyze large volumes of data and identify patterns, trends, and correlations that inform decision-making.
As the client operates across diverse domains, including finance, healthcare, and management consulting, it led to the inevitable challenge of managing volumes of unstructured data. Additionally, within each domain, data was distributed across multiple siloed systems, making it tougher to achieve a unified view of operations and systems.
Upon auditing the existing data infrastructure, we identified the following issues:
To get a centralized solution that optimized data ingestion, retrieval, and analysis, they decided to seek professional data management assistance.
After analyzing their existing data infrastructure, we recommended a comprehensive data processing solution. This solution would compile a centralized repository of structured data (legal & corporate documents, client agreements, etc), integrated with a tailored BI solution for advanced visualization and reporting.
We conducted a thorough assessment to identify all the sources & formats of unstructured data (projects, meetings, inspection data, etc.) and integration points.
Our data experts then developed a detailed data strategy outlining the architecture, technologies, and workflows for processing this data and ultimately aggregating it to form a centralized data lake.
Once we had access to the client’s unstructured data, our data processing experts:
We designed a scalable and flexible data lake architecture using AWS services, including Amazon S3 for storage, AWS Glue for data cataloging and ETL (Extract, Transform, Load), and Amazon Redshift for data warehousing.
Once the data lake was developed, our experts did the following:
As the client needed smooth access to structured data, preferably in visually comprehensible forms, we also integrated a tailored BI solution that generated informative and interactive dashboards. This enabled real-time data visualization and reporting, allowing the end users to get valuable insights into risk management, project status, working schedules, downtimes, etc.
We also integrated Amazon SageMaker to develop and deploy predictive ML models directly on the data lake. By repeatedly analyzing business data (operational & consumer data and trends), these models enabled the end users to forecast business performance, identify potential operational issues, and uncover new business opportunities.
To address the inevitable risks of handling and processing large volumes of sensitive, multi-format data, we implemented robust security measures, such as IAM (Identity and Access Management), encryption, and data masking. This security-driven approach helped us win their trust, leading them to sign a long-term data servicing contract.
After implementing the BI-integrated, centralized data lake solution, the client experienced:
50,000+ data fields (per day) managed automatically
30% reduced delivery time for the
end users
40% improved analytics engine performance, with increasing data accuracy rates (from 72% to 95%)
After evaluating their initial work, we were confident that we had found a reliable data partner. Their expertise in data management, BI, and machine learning integration allowed us to get and deliver deeper insights using the same business data.
- Client
We implemented a robust data lake architecture, integrated advanced BI solutions, and employed a humans-in-the-loop approach to ensure our client gets precise and actionable insights even from unstructured data. Learn how you can also benefit from our comprehensive data management capabilities and real-time data visualization and reporting services.
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