Do you know what’s common between Salesforce’s Einstein AI, Google Analytics, and Bloomberg Terminal? They are all data products. What sets data products apart from other technology products is that they use data as the primary asset to achieve a goal.
Data products embed intelligence into business operations and leverage data to solve an internal or external business challenge.
Salesforce Einstein AI, for instance, is a predictive customer analytics solution that provides marketing and sales teams with powerful and up-to-date views of customers and sales prospects. This helps drive better decisions to influence better sales outcomes. On the other hand, Google Analytics is used by marketers around the world to track and measure the many intricacies of their web traffic.
As companies become more data-driven, creating data products has become essential. In fact, it would not be wrong to say that it has become essential for more or less all products to become data products. Only these products can empower enterprises to improve their business processes and cater to their customers more effectively.
However, building data products is not easy. Apart from the issues related to architecture, embedding analytics capabilities, and addressing reporting challenges, for these data products to function well, testing is super-important.
The scope for testing these products is immense. Apart from testing the functionalities, usability, and performance, a focus on data security and accuracy is critical given that these products tend to become repositories of sensitive data. Robust testing strategies will ensure that the product is secure and meets the customer’s requirements.
Here are some test strategies that companies can implement while testing modern data product.
What Are The Right Test Strategies For Testing A Data Product?
Test the MVP
Emily Glassberg Sands, in an HBR article, suggests that companies start with a focus on testing the minimum viable product (MVP). It’s accepted wisdom now that this will help the product team to launch products quickly without overspending or compromising on future-focused R&D efforts. Once the MVP is tested, companies can launch the product and find out the feedback of customers. Starting on a small scale simplifies the testing process and gives the product team an idea about what features are acceptable or unacceptable to the customers. It will provide the team with accurate data on whether the product is viable in the market and if it will solve the customer’s pain point.
Conduct iterative testing
Data products can be relatively more complex than other enterprise or consumer products. They need continuous iterations as they are based on data that is driven by changing customer and business conditions. This is the core value promise of the agile model and is particularly well-suited to the evolution of data products. Of course, product teams may have to build and test new models of the data product till they find the right product-market fit. Iterative testing helps the product team test the product continuously. It helps them to make small changes to the product based on the previous test results. Iterative testing is easier to manage as it is done incrementally. It is easier to identify bugs and vulnerabilities. This enables companies to deliver a better-quality data product that is constantly improving. Also, considering that the results are real-world evidence-based, it’s easier for companies to gain support from stakeholders.
Don’t just test the code
The code is essential. However, the relationship with data is the core of a data product. Without a deep understanding of data, the product team will not be able to build a good data product. To safeguard the sanctity of data, the product team must not limit the testing to the code alone. They must test the reliability and accuracy of the data that’s carried through the code. The product team must perform data-focused unit tests to determine if the data carried through the code is as expected. They must do comprehensive integration tests to ensure that the code that’s carrying the data works seamlessly as data moves from one system to another. Integration testing ensures that nothing breaks in the transfer. Most importantly, the product team must perform data tests. Data tests ensure that the data in the target system is precisely the same as the data in the source. The product team may also test the previous data with the current data to check if it has met the expected outcome.
Automate the testing process
This is the age of fast go-to-market. Pace is valuable in development and testing too. This is why it’s important to do automated testing while dealing with an enormous amount of data. The world of analytics talks about the volume, velocity, and variety of data in the context of how this stresses the solutions that analyze them. Data sets are dynamic. As the team develops the product further, new parameters, new criteria will have to be included. The data sets will also be updated regularly. It will be nearly impossible for the testing team to test manually. The scope for errors in manual testing is high and this is time-consuming too. Automated testing reduces the time taken to test such large and dynamic data sets and maintains accuracy in testing. Automated testing will also enable the product team to launch the data product quickly.
Data products are necessary to help customers make data-driven business decisions. Hence, it’s crucial to build good quality data products. The only way to uphold quality is through continuous testing. With the right testing strategy, tools and processes, and the advice and support of the right experts, it will be possible to build world-class products.