Data Transformation

Data migration to the cloud is merely the first step, while being a substantial effort for many. The possibility to create a trustworthy data platform.


Leveraging Data Transformation for Improved Data Quality

Data transformation is a key component of data quality management, and it is essential for organizations to leverage this process to improve the accuracy and reliability of their data. Data transformation is the process of converting data from one form to another, in order to make it more useful or to provide a better understanding of the data.  This process can be used to make data more structured and organized, to reduce redundancy, identify and eliminate outliers, and increase the accuracy of the data. Data transformation can be a highly beneficial process for organizations, as it can help to improve data quality by ensuring data is accurate, consistent, and reliable. By leveraging data transformation, organizations can reduce errors in data, such as duplicate entries, incorrect values, and incorrect formats. This can improve the accuracy and usefulness of the data, resulting in more informed decisions. Additionally, data transformation can help to reduce the time spent manually entering data, and reduce the potential for errors.



Our Service Offerings

1. Data migration

Maximise the value of your data and reduce data management costs by removing any data landscape constraints. With our professional services, you can experience dramatic improvements in the value of data, freeing up more resources for strategic initiatives.

2. Data platform modernization

Establish a trustworthy and re-usable set of data products that enable quicker time to new insights, even without advanced AI. With our professional and reliable data products, you will be able to unlock value and get ahead of the competition.

3. Data center Modernization with Hybrid Cloud

Establish trust and sustainability with our proven Managed Platform for Adaptive Cloud. We provide faster time to new insights, committed reduction, and optimal distribution of workloads across both Private and Public Clouds— even without advanced AI. Our platform offers superior value with streamlined processes and reliable data products.

Analyzing Data Quality Before and After Transformation

Data quality plays an essential role in any business process, especially when it comes to analysis. In order to ensure that the data being analyzed is accurate and reliable, it is important to go through a rigorous process of data quality analysis both before and after the transformation process.  By taking the time to analyze data quality both before and after transformation, businesses can ensure that the data they are working with is of the highest quality and can be trusted to lead to the right decisions.


Data Transformation Blogs

Read what our bloggers are saying about the Market insights, trends, consumer forces and predictions for the banking industry

DevSecOps – The Most Sought-after Secured Integration Practice

DevSecOps is the process of incorporating security procedures across the whole software development lifecycle. Instead of waiting until the end of the process or after deployment, the aim is to identify and reduce security issues as early as feasible in the development cycle. This may be done by including security testing and analysis tools into DevSecOps – The Most Sought-after Secured Integration Practice


How to Design an Effective Data Transformation Strategy

Designing an effective data transformation strategy is essential for businesses to stay ahead of the competition in the digital age. With the rise of big data, companies need to be able to quickly and accurately process large amounts of data in order to gain insights and make decisions. Data transformation is a process of converting data from one format to another in order to make it more useful. It involves restructuring, cleaning, and aggregating data so that it is easier to analyze.


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