Data management is an administrative process that includes acquiring, validating, storing, protecting, and processing required data to ensure the accessibility, reliability, and timeliness of the data for its users.
Proper data handling and management is crucial to the success and reproducibility of a statistical analysis. Selection of the appropriate tools and efficient use of these tools can save the researcher numerous hours, and allow other researchers to leverage the products of their work. In addition, as the size of databases in transportation continue to grow, it is becoming increasingly important to invest resources into the management of these data. There are a number of ancillary steps that need to be performed both before and after statistical analysis of data. For example, a database composed of different data streams needs to be matched and integrated into a single database for analysis. In addition, in some cases data must be transformed into the preferred electronic format for a variety of statistical packages. Sometimes, data obtained from “the field” must be cleaned and debugged for input and measurement errors, and reformatted.
MS Office Solution’s Data Management Services enables organisations to integrate, transform and improve data through advanced data integration and master data management with ample governance and control.
Our all solutions comes with AI enbaled chat bot using which clients/customers can write questions and get answers immediatley we uses NLP based tehnology in backend
We understand data and the need for source of truth. Whether it is cleaning data, duplication or planning data warehouse architecture, we do it all. Companies need to put forth control over the daily volumes of data that aggregate in paper and electronic form.
We help our customers with Data Management solutions encompassing definition and enhancement of data governance policies, data planning for transformations, operating model definitions, data cleansing activity, and automated & manual validation processes of cleansed data to eventually ensure data stewardship, reduce overall cost and time to market for enterprise data and also help improve efficiencies in the entire data lifecycle.