Data Management & Analytics

Manage, Analyze, Visualize, Understand and Adapt data for Transformative Growth



Data Architecture

Modern Data integration Strategies, Secure, simple and elastic architectures, Improved workflows.


Data Modernization

Data architecture reviews, Expert planning, design, execution techniques to modernize data, Reduce risks in data migrations.


Master Data Management

Shared data repositories, reliable data to support modern development approaches


Business Intelligence

Interpret past data, understand historical performance of your business, develop Descriptive Analytics


Advanced Analytics

BI solutions using Machine Learning, AI and Data Science. Analyze Patterns and make future predictions, Predictive and Prescriptive Analytics


Data Visualizations

Data platform development, reports and dashboards development and optimization


Cloud Migration

Migration strategy using CI/CD pipeline, Automated workflows, BI infrastructure for Data Analytics


BI at fingertips

Real-time analytics, rich visualization and exploration, Timeliness, Result-driven collaboration, Data insights, Descriptive Analytics

Advanced Analytics

Customer behavior prediction, machine-learning based image analysis, Sales effectiveness through opportunity scoring, personalized customer experience through collaborative, content-based filtering techniques.

Efficient data warehouses

Cloud based Data lakes, data marts, Metadata storge for Analytics infrastructure, Data Integration, data quality, data backups, ETL using multiple data sources.

Meaningful Data Analysis

Accurate, reliable information, OLAP processing, Hetrogeneous data handling

Effective data integration and Management

Effective Data models, Master data management, Data transformations, unified Access to variety of data through multiple data sources, formats and structures;

Secured data

Data encryption, data with user authentication and authorization, row and column level access controls, report and workspace level security.

Interactive data visualizations

Dashboarding, collaborative visualizations, Mobile reporting, Scheduled and adhoc reporting, Customized visual elements

Cloud Migration with Customer Focus

DBAs need to make a comprehensive backup plan for databases for which they are accountable. The backup plan should include all types of RDBMS within the enterprise and should decide what needs to be backed up, appropriate backup type to use for the data, where to store backups and backup retention policy. For Oracle databases, we put tablespaces in backup mode and backup the associated data files using OS copy command or RMAN. It is important to review the RMAN compatibility matrix for the database. the DBA can reduce the backup window for VLDBs by allocating multiple channels and fine-tuning backups, can save disk space by using compressed backups, and can block tracking with incremental backup techniques with the latest versions. The DBA must review the version and edition of the database to confirm availability of this option. Alternatively, the DBA can consider setting up split mirror backups. For SQL Server, the DBA can partition the database among multiple files and use the file or filegroup backup strategy. Using multiple backup devices in SQL Server allows backups to be written to all devices in parallel. It is good practice to select a backup window at a point when the lowest amount of activity affects the database so that the backup does not reduce available database server resources and slow down the database user’s activity. The DBA can tune the backup window by parallelizing backups using multiple channels however, the DBA must review the version and edition of the database to confirm availability of this option. In most of the cases, it is best to set up a weekly backup cycle starting with full backups and then incremental/differential backups. Archive/transaction log backups can be scheduled for every few hours, depending on the volatility of the database.

Hybrid approach for a cost-efficient IT

By combining dedicated hardware with cloud-based services, hybrid gives you the flexibility to take advantage of on-demand cloud resources while simultaneously satisfying even the most complex security and compliance requirements. For example, if you need to ensure GDPR compliance, you can retain control over your most sensitive data in your own on-premises data center while running the application’s front end in the public cloud. This allows them to conduct business and transact payments online all within one seamless, agile and secure environment. Other gains can come from changes in operations and maintenance. A reduction in on-premises servers can translate to reduced energy consumption, as there is less infrastructure to power and cool. And less on-premises hardware to maintain leads to more effective use of human resources. Using inexpensive cloud storage, data and application segmentation and taking advantage of infrastructure elasticity are also ways to reduce costs.

Bringing Big data into Enterprise Data for Health Analytics

Associating enterprise application data with Big Data will bring the most benefits to an organization. We establish new capabilities and leverage our client’s prior investments in infrastructure, platform, business intelligence and data warehouses. Investing in integration capabilities enables us to correlate different types and sources of data, to make associations, and to make meaningful discoveries. This is especially true in Health Industry. It allows access to both health-related and non-health data to support forecasts of future healthcare needs for any given demographics. Storing the vast range and magnitude of health data — medical history, biomedical data, diagnostics data, and so on using Big Data facilitates data analysis for prompt healthcare services and innovations.