Historical data is becoming a key tool for decision-making at enterprises of all levels.
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.
Our Data Analytics Testing Process has:
We understand the risks that your analytics reports can face. With the know-how of both proprietary and open-source tools, our Data Testing services include,
Production Data Validation: It’s essential that your data warehouse is of maximum competitive advantage to your business. Our ETL Testing and Validation techniques ensure Production reconciliation
Application Upgrade Testing: Technology changes every day. And your Data warehouse must adapt to the changes to be compliant, embrace new security & performance upgrades. Considerable effort in pre and post upgrade can be saved with our systematic testing approach towards migrating previous data into the new repository and the like.
Validating Source-to-Target: Data correctness is a vital check to be performed during and post the data transformation process. Our automated testing procedure not only validates the end-to-end data but also outlines remediation to prevent future data corruption
Testing for Data Completeness: Once the data is validated with the help of Validation testing, we verify that all the data is accurately loaded into the data warehouse by comparing validation counts, aggregates, and Spot checks between random Actual and Target data on a timely basis.
Metadata Testing: Metadata protects the Data quality. Our automated metadata testing procedure includes close check on Data type, Data length, Index /Constraint, etc.
Data Transformation Testing: Data transformation testing can get complicated at times since multiple SQL queries may need to be run to verify all transformation rules conform to the business rules. Our techniques ensure saving time on this tedious task.
Quality Testing/Reference & Syntax tests: Our data quality testing services help prevent syntax issues (dirty data), and errors caused due to incorrect reference types (data model). Data quality is affected due to bad data (Ex- invalid characters, invalid patterns, etc.) or Data model (Ex- data types, precision, Null, etc.)
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
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.