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𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐔𝐬𝐞𝐝 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Certain methodologies can be adapted based on project requirements and the specific context of the data analytics application being tested. Let's check out a few of the methodologies used for #DataAnalyticsTesting in details. 𝐀𝐠𝐢𝐥𝐞 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Overview: #Agiletesting integrates testing into the Agile development cycle, allowing for continuous feedback and iterative improvements. Application: Test cases are created and executed in short sprints, focusing on quick iterations to adapt to changing requirements. 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Overview: #DataDrivenTesting involves creating test scripts that use multiple data sets to validate the system’s functionality. Application: It allows testers to run the same tests with different input data, ensuring comprehensive coverage of scenarios. 𝐄𝐓𝐋 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦, 𝐋𝐨𝐚𝐝) Overview: Focused specifically on data pipelines, #ETLtesting verifies the accuracy and completeness of data as it moves from source to target systems. Application: It includes validating data transformation logic, ensuring that data is correctly extracted, transformed, and loaded into data warehouses. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Overview: #Performancetesting assesses the speed, scalability, and stability of analytics applications under load. Application: It includes #stresstesting, #loadtesting, and benchmarking to identify how the application behaves under various conditions. 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 Overview: #Regressiontesting ensures that new code changes do not adversely affect existing functionality in analytics applications. Application: After updates or changes, this testing re-evaluates previous test cases to verify that the outcomes remain consistent.

Are You Using the Full Potential of Your Data? In today's fast-paced business environment, ensuring the quality and accuracy of your data is crucial. Discover how BiG EVAL Data Quality Automation software can revolutionize your enterprise by providing comprehensive test cases and data validation rules. Say goodbye to manual errors and inefficiencies, and embrace a new era of data excellence. 🧪Data Warehouse & ETL Testing - Utilize BiG EVAL's automated regression testing algorithms within a data warehouse project to ensure implementation quality during the full release-cycle. 🧪Data Vault Testing - BiG EVAL provides many capabilities to apply automated quality assurance mechanisms to your data vault project and operations. 🧪API Testing - Utilize BiG EVAL's flexible connectors to validate data provided from an API or to use API data as a test reference. 🧪Data Migration Q/A - To assure the quality of a data migration project, BiG EVAL's automated data reconciliation algorithms bring huge benefits. 🧪CI/CD Test Automation - Automatically testing code and artifacts within a continuous integration and deployment process is made possible with BiG EVAL's test automation features. 🧪Collaboration - BiG EVAL is able to communicate with several collaboration solutions to inform teams about data validation issues. 🧪Data Quality Management - BiG EVAL's automation features allow to continuously monitor data quality rules during the whole life-cycle of your data. Harness the power of BiG EVAL to automate and enhance your data testing and validation processes. Make your data work harder and smarter with BiG EVAL—your partner in achieving unparalleled data accuracy and reliability!

I am thrilled to share that I have recently designed a Quadruple Source Data Refreshable Report that promises to be a game-changer. It’s truly a proud moment when the results of your hard work are unveiled. 🌟🚀 And guess what??? You can do it too! Let’s explore new horizons and create impactful solutions together. Here’s a step-by-step guide for all the architects behind the Quadruple Source Data Report: Research and System Selection: Begin by researching the systems available within your company. Identify platforms or tools that can integrate multiple reports seamlessly. Consider factors like data compatibility, scalability, and ease of use. Impact Proposal: Create a comprehensive proposal that outlines the impact of automating your report. This proposal should be persuasive and data-driven. Include visual insights, summary of process improvement, revenue generation, growth opportunities, and the cost saving benefits it will produce by reducing hours of labor. Project Overview Deck: Prepare a high-level presentation for developers and stakeholders. This deck should convey the big picture of your project. Key elements to include: project scope, data sources, workflow, and benefits. Design Logic for Each Event: Break down the report logic for each event or data source: triggers, event timestamps, data aggregation, data transformation and data integration. Thorough Testing: Rigorously test the automation over an extended period (months, if possible). Consider the following: edge cases, data consistency, performance, and error handling. Accuracy and Bug Confirmation: Once the testing phase is complete, run the automated report in a production-like environment. Verify the accuracy of the generated reports against manual calculations. Address any bugs or discrepancies promptly. Archiving Old Reports: As you transition to the automated process, archive your old reports systematically. Ensure that historical data remains accessible either in your automation or saved for reference for compliance purposes.

Are You Using the Full Potential of Your Data? Making sure your data is accurate and of high quality is essential in the fast-paced business world we live in today. Discover how the comprehensive test cases and data validation rules provided by BiG EVAL Data Quality Automation software can transform your business. Say farewell to manual mistakes and inefficiencies and welcome an age of superior data. 🧪Data Warehouse & ETL Testing - Utilize BiG EVAL's automated regression testing algorithms within a data warehouse project to ensure implementation quality during the full release-cycle. 🧪Data Vault Testing - BiG EVAL provides many capabilities to apply automated quality assurance mechanisms to your data vault project and operations. 🧪API Testing - Utilize BiG EVAL's flexible connectors to validate data provided from an API or to use API data as a test reference. 🧪Data Migration Q/A - To assure the quality of a data migration project, BiG EVAL's automated data reconciliation algorithms bring huge benefits. 🧪CI/CD Test Automation - Automatically testing code and artifacts within a continuous integration and deployment process is made possible with BiG EVAL's test automation features. 🧪Collaboration - BiG EVAL is able to communicate with several collaboration solutions to inform teams about data validation issues. 🧪Data Quality Management - BiG EVAL's automation features allow to continuously monitor data quality rules during the whole life-cycle of your data. Use BiG EVAL's capabilities to improve and automate your data testing and validation procedures. Join forces with BiG EVAL, your partner in attaining unmatched data accuracy and dependability, to make your data work harder and smarter! #Data #BusinessIntelligence #DataTesting

Data is the glue that sticks systems together. Many organizations struggle to maintain a comprehensive understanding of data structures, data relationships, and data flow between disparate systems. This lack of visibility creates a "black box" scenario, obscuring critical details necessary for effective testing and test data management. Some teams opt to circumvent this challenge by relying solely on user-based validation of the system, which can lead to various problems, including inadequate test coverage and an inability to identify the data requirements for thorough testing. Visualizing data structures, relationships, and flows is a powerful approach to overcome these challenges and enhance the effectiveness of testing efforts by clearly delineating system boundaries and data dependencies. By visualizing data structures, relationships, and flows, organizations can: 1. Gain a comprehensive understanding of how data moves through interconnected systems. 2. Identify potential bottlenecks, redundancies, or inefficiencies in data processing. 3. Facilitate collaboration between development, testing, and business teams by providing a shared visual representation of data dependencies. 4. Improve test case design by accounting for all relevant data scenarios and edge cases. 5. Streamline test data management by pinpointing the required data sources and transformations. 6. Enhance test coverage by ensuring that all critical data flows are adequately tested. By embracing data visualization as a core practice, organizations can demystify the "black box" of data integrations, enabling more effective testing strategies, better test coverage, and ultimately, higher-quality software systems. Curiosity Software #TestAutomation #SoftwareTesting #ModelBasedTesting #AgileTesting #QALife #TestingTips #TestingStrategy #TestAutomationFramework #SoftwareQuality #QAEngineering #TestingTransformation #TestingInnovation #ModernTesting #Infosec2024 #InfoSec

Are you part of an IT/Data team working on digitizing business processes? If yes, then you know that precision in specifying and developing incoming and produced information is crucial for each business process task. Effective communication between business and IT is essential for this to happen. While developing our Business Logic Platform, EICORE, we've identified five vital aspects of information that business users possess, which are crucial for developers to understand in detail: 1) Semantic Model, 2) Technical Information Structure, 3) Information Entry Guardrails, 4) Information Content, and 5) Rulesets and Data Quality Rules. We're keen to learn about any other aspects of Business Information that you think are relevant for IT/Data Teams in developing digital solutions. Share your insights with us! Semantic Model:  A collection of Business Terms from a Glossary defines  content for a Business Process Task. For a file with measurement data, Business Terms would represent each parameter. Technical Information Structure:  Specifying the schema aligning with  technical names in a file format. The Schema is further divided into Blocks and Attributes which may be linked with Business Terms from the Semantic  Model for detailed insights. Information Entry Guardrails:  Aiding users in accurate content creation,  guardrails may include descriptive prompts, list-limited entry, and  dedicated forms for various data types. Information Content:  Users mainly add content supporting Information  specifications. Guardrails aid entry, while storage aligns with the specified  Technical Information Structure. Rulesets and Data Quality Rules:  Users assign rules for data quality or to specify how a Business Process Tasks shall interpret the information. These rules, organized into Rulesets, can range from conceptual to more complex, including variables for user input. An example could be data quality rules  such as "value >/</= [user input]." The screenshot below demonstrates how a Business user can incorporate various information aspects into a single information object utilized by a Business Process Task. #DigitalTransformation #EICORE #BusinessLogic #ITCollaboration #metadatamanagement #datagovernance #digitaltransformation #datamanagement #datastrategy #dataops #masterdatamanagement #datamesh #datafabric #bpmn #businessprocess #datacontract #dataquality

Here’s my latest article on Process Mapping and its critical role in understanding and enhancing business processes. Check it out and let me know your thoughts! #ProcessMapping #WorkflowOptimization #BusinessProcessManagement #DataScience #DataAnalysis

As most industries and governmental organisations embrace digital transformation and strive for operational excellence, the need for streamlined processes, efficient decision-making, and cohesive case management becomes increasingly apparent. In order to achieve the goals and objectives linked with operational excellence ever more organisations have started using business modeling and simulation techniques to analyse, document, implement and automate their case, process and decision management. The fourth pillar to support this journey is shared data management. #sdmn #bpmn #dmn #cmmn #bpm+#operationalexcellence #customeroperations #processmodeling #casemodeling #decisionmodeling #datamodeling #corporatedatamodeling #corporatedataplatform #businessprocesses #decisionaware #decisioncentric #orchestration #digitaltransformation #businesstransformation #insurtech #fintech