Quality Assurance for Analytics: 4 Steps to Avoid Big Headaches
Accurate business intelligence on how your brands are perceived and what drives your customer base is critical to your business’s success. That’s where Analytics comes in. Analytics is the process of discovering and interpreting meaningful patterns in data monitored from an ongoing process. It uses statistics, computer-automated analysis, and operational research to quantify the performance of that process.
But the data gathered about commerce in your products and services is only as good as the software that gathers and analyzes it, and the quality of the collected data itself.
Like any other software system, analytical tools and their implementations need the continuous attention of a software quality process. You need to look at the quality of the tools and processes that monitor the quality of your system. Whether you’re implementing one of the industry-standard analytics solutions like Omniture or Bluekai or building a custom tracking solution tailored to your specific needs, you will need to plan out and qualify your analytics system as carefully as you did your product sale and delivery system.
Software Quality Assurance for analytics will guide your implementation and find the bugs that could turn your carefully gathered business intelligence into a chaotic mass of unusable data.
Big data testing for analytics requires experience in the nuances of not only what can go wrong with collecting the data but also how data can be incorrectly interpreted into a wholly misleading assessment. Quality engineers must engage directly with your development teams and operate seamlessly in your agile sprints to provide the analytics output that helps get your product to market on time.
Here’s how that works:
1. QA Staffing
Assemble a QA team to verify that the data your analytics system generates is accurate and provides the metrics you need. Getting qualified people on board quickly poses a challenge, especially if you’re located in an area with a competitive job market.
Hiring a respected QA service team to work with your developers gives you the flexibility to staff up for crunch times. And if you choose a test partner who doesn’t penalize you for flexibility, you can save money on salaries in slower cycles.
2. A Systematic Approach
To ensure testing is thorough and consistent, your QA service team should create test plans and test cases. These provide definition and structure to the QA process and ensure consistency and continuity.
3. Efficiency through Automation of Data Validation Tasks
Data validation is a real chore that adds time and costs to the analysis. If your QA service team can design custom data validators, that takes the heavy lifting out of the data validation task and improves the accuracy of the data.
Some devices, like the Roku set top box, can’t be routed to typical transparent proxies for data analysis, so they require a custom data capture solution. A strong QA partner will be able to develop this.
4. Testing with Real Devices
Simulations of smartphones, tablets, and other devices will only take you so far. To get real data, you need real devices. Be sure your QA partner has a large available library of devices with a broad range of OSs to capture a wide variety of data.
Check out our case study, Analytics and Telemetry, to see how smooth a successful QA process can be.
Let us know if you are looking for big data testing services.