Intelligible solutions deliver by best QA practices

As there is new booming topic of Data Science in market, everyone is making serious amount of time and thinking of creating Data related benefits. Companies are developing their Data Science departments to have gain over new arrivals in competitive domains. However, we all know that every industrial project needs thorough evaluation and timely Verification before the product delivery as well as project development by technology experts. QA activities are also required in Data handling as well as in Project’s Lifecycle. It demands Data Verification, Data Cleaning, Data Integration, Data Aggregation and Data Loading. Verification and Cleaning requires understanding of data sources and the sense of domain knowledge. After proper verification and cleaning of data and data sources, we load this data on our Databases and warehouse.
Many companies shifting towards Manual to Automated testing whereas both of these have their own pace of work according to case scenarios.

Advantages of implementing QA

• Verification and Validation of Business requirements
Identify the cause of the data issues (discrepancies, missing values, different formats etc.) and provide the solution to fix the problem. Synchronize multiple data sources, eliminate data anomalies to clarify conflicts in data. This process leads towards more robust and to the point working of Business scenarios.

• Reduces project risks and costs
Report and track defects, assist developers in defect resolution, and compile test results for reporting. Accurate QA practices obtain informative methodologies to meet Business Requirements to effectively fulfill client’s demand in a more risk-free manner.

• Build Quality systems
Write and maintain automated scripts, test plans to insure quality of the data. QA ensures end-to-end quality of systems (from typographical mistakes to approve project builds). Meanwhile we can say that it is a sink and source data analysis where the data get customized and reports are created to integrate BI functionality.

• Makes system cases easy to use and reliable
Review product requirements, functional specs, and design specifications to create comprehensive test plans, scenarios, cases, and scripts.

• Testing makes long-term savings for project
People are adopting new technologies but not necessarily for their data sources. Data volumes are increasing day by day so, all we need is to reinvent the wheel by automating and simplifying the testing tasks for both cost and time saving.

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