Data Analytics Testing

Testing the Single Version of Truth

Qapitol QA > Data Analytics Testing

Testing the
Single Version of Truth

Data analytics testing gets you answers to questions related to the veracity of data preparation, model production, and helps uncover inherent biases. Tests are conducted to verify whether the visualization graphs are displaying data correctly, whether the visualizations can be bettered, and whether the models are performing without latency.

QA Needs of
Producers & Consumers

online lending

Qapitol QA provides testing support to companies developing Analytics solutions including code based tools and GUI based applications. Our team with deep analytics domain experience helps with data validations, defect investigations, test automation and QA for continuous integration.

Data Analytics
Testing Approaches

Pre-ETL Validations: Format, Consistency, Completeness
Post-ETL Tests: Meta-data, Data transformation, Data quality checks, Business validations
Validate Models: Implementation, Computation
Validate Aggregation: Data Hierarchy, Data Scope, Summarized Values
Validate Visualization: Information Representation, Data Format, Result Intuitiveness

Data Science Expertise &
Data Smart Teams

Our team comprising of passionate and creative data science professionals aim to bring measurable business value to all projects. We leverage our decades of experience working in SAS R&D which is a hot-bed for creation of world-class analytics solutions.

Data Analytics Development &
Testing Solutions

Take advantage of our Analytics Advisory team that provides solutions for predictive modeling, demand forecasting, customer segmentation, sentiment analysis etc. We audit current data analytics maturity, suggest the right tools and techniques and eventually help build your Analytics Centre of Excellence.

Data Scientist (n.):  A person who is better at statistics than any software engineer and better at software engineering than any statistician. “

Josh Wills


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