Why Preserving Data Quality is Important for Data Integration?

Posted By: administrator
Posted On: 23 Nov, 2020
Last Updated On: 09 Dec, 2020

There have been tremendous advancements in information technology, but very few organizations realize the real value of their enterprise data. The increasing dominance of automated business processes and relentless competition are driving companies to focus on their business data. They can use data integration processes to enhance customer service, comply with government regulations and simplify global operations through quality data.

Why Do You Need Data Integration?

Enterprise data should be easily accessible and reusable to fulfill business goals. Businesses need to look for ways of integrating data from multiple source systems into more productive data applications. These data should be cleaned and modified for better data management and preserving the data quality while using in different applications.  

Organizations are using more data in new digital transformation processes such as collecting data and reusing it in other applications. But, often it is observed that the data collected for operational systems are not suitable for different applications like business intelligence, customer relationship management or reporting. The presence of hybrid IT systems can also lead to data duplication, lack of compliance, and other inconsistencies if data are not synchronized.

How are Data Quality & Data Integration Interdependent?

Data integration technologies have emerged to support organizations with fragmented data and enhance quality. Technologies should have a symbiotic relationship among them, i.e. they should work together seamlessly.  

Analyzing and profiling the data before data integration are necessary steps to speed up the development of integration workflows. This initial profiling helps companies to identify their source data so that it can be exported to multiple destinations such as data warehouse, customer relationship management system, or business analytics applications. 

Data quality declines over time, so preserving data quality is not a one-time task, but an ongoing process. It is not only identifying defective or inaccurate data but also bringing comprehensive, consistent and relevant data to the business.  

Data quality and Data integration are related but are two different processes. Data integration tasks use quality data; similarly, Data quality processes require data integration technology. As data quality initiatives evolve and become more embedded in operational systems, they need a data integration platform to deliver performance and scale. This also helps organizations recognize the true value of their enterprise data.

Are You Using the Right Tools for the Right Role?

Data quality and data integration share a strong correlation, yet the two disciplines demand different skill sets and tools. The data integration developer focuses on transferring data from multiple sources to a destination quickly, while the data quality professionals ensure to maintain the quality of the raw data content. A unified platform for data quality and data integration provides organizations with all the right tools to access, analyze, cleanse, and deliver various data types on-premises, in the cloud, even in a hybrid environment.

How do We Connect Data Quality and Data Integration?

We, support businesses of all sizes to access data they can trust throughout their organization based on our data quality and data integration solutions. Our Data Quality service provides profiling, cleansing and monitoring of data especially from Google Analytics which is the most widely used tool to capture data. We run automated testing and set up anomaly alerts so that you get alerted when there is an unusual activity happening on the site. Daton is our automated data pipeline that would seamlessly extract all the relevant Data from popular data sources then consolidate and store it in the cloud data warehouse of your choice for more effective analysis.

The products and services offered by us are synchronized at different stages of the data integration and data quality process rendering powerful and quality data from a single integrated environment.

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