What challenges do you face when testing data and how can you overcome them?

I would say data quality & validation. One must never trust the source 100% , and it's better to put some checks in place. Remember, garbage data leads to garbage results.

There are a number of challenges that can be faced when testing data, including: Data volume and complexity: Data sets can be very large and complex, making it difficult to test all of the data exhaustively. Data quality: Data can be inaccurate, incomplete, or inconsistent, which can lead to errors in machine learning models. Data privacy and security: It is important to protect sensitive data during testing. Test automation: Test automation can be challenging for data testing, due to the complexity and variability of data sets.

You can enhance data quality in two ways: - Data Quality Prevention: This involves establishing a development process that prioritizes data quality. Developers should have a clear understanding of how code affects data when deploying new code. - Data Quality Detection: This approach involves defining a data contract or standard data format that you expect from a data source. This enables you to quickly identify data errors and act accordingly

As technology continues to advance, we are seeing more widespread use of AI, AR, and interconnected devices in both mobile and web applications. This is bringing data from various sources and at rapid pace. So, verocity of Data is huge. To add to this, Every web app and every software has its own level of complexity. Data Profiling and Data Security is very important as far as data complexity is concerned.

Data complexity and diversity are paramount challenges. Managing data from various sources, such as sales, supply chain, and consumer insights, can be intricate. The diversity in data formats, structures, and quality requires meticulous validation. To tackle this, we implement robust data profiling to understand the nuances of each data source. Establishing data standards and validation rules ensures consistency. Automation plays a pivotal role; we employ testing frameworks and tools for scalability. Moreover, collaboration between data engineers, domain experts, and data scientists is crucial to interpret complex data correctly.

Maintaining data consistency and validity is paramount in FMCG data testing. With vast data volumes and diverse sources, ensuring that data aligns with business rules is a challenge. We tackle this by establishing clear data validation rules and checks at each stage of the data pipeline. Automation plays a pivotal role; we employ data validation frameworks and scripts to catch inconsistencies early. Collaboration is vital too; close communication with domain experts helps define what "valid" data means in various contexts. Data governance practices and documentation ensure ongoing consistency. In the dynamic FMCG landscape, upholding data integrity is non-negotiable for sound decision-making.

Start with a drawing board and create a visual diagram representing the flow of data within your business. Brainstorm with your team. Examine each stage of the data pipeline and identify where data validation should occur. Usually, validation steps take place after data is loaded or transformed in a system. Write test cases to automate data quality checks. These are highly dependent on the nature of your data. If you have a table regarding revenue, writing a revenue test case would be wise. Start to implement appropriate alerts and monitoring strategies. Validation Dashboards can be a good option to verify the health of your daily after each run.

Automating and scaling data testing in the FMCG industry is a formidable yet essential task. With vast datasets and evolving requirements, manual testing is impractical. To overcome this, we employ robust data testing frameworks and automation tools tailored to our specific data landscape. This not only ensures repeatability but also accelerates testing processes. Scalability is addressed by designing testing frameworks that can handle increasing data volumes and sources. Cloud-based solutions provide flexibility in resource allocation. Continuous monitoring and periodic reviews are integral. As an Agile Delivery Data Manager, ensuring that our testing methods adapt to the ever-changing data environment is a top priority.

Take it step by step, instead of building the most correct case first, start with a simple model and enhance it then step by step.

Data Testing is one of the most crucial and important part of a development process. It can be performed by running sql queries, running automated test scripts, visualisation, reconciliation etc. In my personal experience I have faced many challenges when doing data testing in real time mode than in batch mode. Real time data testing is a difficult process. We can do certain sanity checks but verifying a ETL pipeline working in real time is doing its desired job is very tricky. During such scenario one approach which I have tried and tested and gives correct output is taking a snapshot of the both source and target table at the same time and doing further analysis. One another approach which I have seen multiple

One additional aspect to consider in data testing is "Data Privacy and Security." Ensuring sensitive information remains protected during testing is crucial, especially with evolving data regulations. Implementing data anonymization techniques and securing test environments is vital for compliance and safeguarding customer data.

One of the most overlooked aspects of testing data is ensuring data privacy and security. Neglecting this aspect can lead to significant risks, such as data breaches, or regulatory violations- it could even damage an organization's reputation. Another overlooked aspect is potential biases in the data. We often end up focusing so much in the results we want that we could end up disregarding or downplaying data that contradicts them. Both data privacy/security and addressing data bias should be integral parts of an organization's data governance and management practices.