Enabling enterprises to manage data efficiently to power up business decision-making
Managing complex data to produce powerful insights
Data management a key aspect that sets businesses up for success in a highly competitive market. Organizations continuously collect large amounts of customer, product, market, financial and social data to understand their market better and drive personalized experiences. In this context, it becomes essential for enterprises to get actionable insights out of it. However, most enterprises suffer from challenges like inaccurate data, data quality issues, and insufficient data.
Nuvento’s deep expertise in data management data helps organizations place systems and practices to sort and manage data so that it makes sense to the user. We are data management vendors who support enterprise-wide implementation as well as provide consulting services to help organizations transform their data into powerful insights.
Our core offerings
Why choose Nuvento?
12 years in Data Analytics and Data Science
Over 10+ years’ experience in DWH services, designing and BI implementations
8 years of big data consulting and implementation services
Deep data analytics expertise in delivering agile solutions of different complexity levels
On-time delivery of data projects
Expertise in working with business-critical data elements
How to use data and analytics?
Build your data foundation
Understand your data, sort it meaningfully and secure it thoroughly
Put advanced analytics to work
Apply advanced analytics on mined and extrapolated data and predict future trends
Improve performance of your business
Use analytical insights to let your business be more efficient and effective
Explore opportunities for innovations
Understand new trends with data and discover tomorrow’s opportunities
Featured Customer Success stories
DWH optimization solution for an insurance company
A multi-line business insurance company aimed to implement a data warehouse solution for managing its claims operations. They used MS SQL server, and the source systems were a popular multi-line claims system, ERP systems, home-grown applications, AS400, and mainframe systems. They also had multiple feeds coming in from business sources and digitized forms. They had around 150 TB of data, and the ETL processes that extracted data from these sources took over 30-36 hours, as opposed to an overnight processing requirement.
The extended data load time impacted operational dashboards and reports, impacting timely operational decision-making. The ETL processes were not designed well for exception handling and they lacked an efficient recovery process. This resulted in data quality and integrity issues.
Data load reduced from 36 hours to 6 hours
ETL process was redesigned and optimized
Data process was remodeled and re-sequenced
Corrected errors in the historical data
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