Outsourcing / Data Processing

Data Processing

Business Process Outsourcing is a sector pioneered and dominated by Indian IT players. After its phenomenal rise in the past few years, it is currently in the midst of a consolidation phase. With a vast pool of talent trained with a special focus on the BPO industry, India has emerged as the most preferred destination for Business Process Outsourcing. The biggest incentive for a company to outsource its operations to India has been the cost savings. However, there have also been concerns about issues like security and data theft.

That precisely why the choice of the right outsourcing partner is so critical to the success of any firm. Unlike other conventional BPO companies, we have gone beyond the usual contact center and calling operations and charted out new avenues within the BPO domain. Our services are based upon the promises of cost-effectiveness and promptness.

Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an organization. This includes data processing, data research and a host of other solutions.

Data management in clinical research relates to the processes of gathering, recording, monitoring, analysing and reporting on data.

Topics in Data Management include:
•Data modelling.
•Database administration.
•Data warehousing.
•Data movement.
•Data mining.
•Data quality assurance.
•Data security.
•Meta-data management (data repositories, and their management).
•Data architecture.

Data management becomes even more challenging when there are fusions and acquisitions and multiple data sets need to be consolidated.

There several challenges are positioned like poor data quality, incompatibilities between data sets, duplication of data and outflows in multiple systems.

Thus, second phase of IT evolution is directed at simplifying the information landscape of the enterprise.

Master data management is a tactic to standardise data comprising tools and technologies for classifying, normalising, consolidating and aggregating data across the enterprise to provide a consistent view.

The process of data management should be on-going and should begin at the early stages of protocol development, and end only when statistical analysis is complete.