Garbage in garbage out is a phrase that is common in the context of data and analysis. Ensure data hygiene, build trust in your data infrastructure, and free up time to focus on growth by taking some essential steps.
Google Analytics Data Correction
Google Analytics is one of the most prominent tools in the market for capturing user behavior data on a web application. Unfortunately, for a product as prevalent as it is, it is surprising to see 9 out of 10 implementations have critical gaps and flaws. For a digital business, these gaps could mean that decisions are being taken based on incomplete, inaccurate, and inconsistent data.
Once we audit your systems, we fix the data issues, setup the right tagging in place, and ensure that the quality of data is accurate before rolling out the changes to your production application.
Automated Testing and Alerts
Instilling trust in data is vital for the success of any tagging, analysis, data warehousing, or reporting initiative. However, performing manual testing is a slow way to build and maintain that trust.
We take a software engineering approach to testing by leveraging tools and techniques used to ensure the quality of products and extend those techniques to testing for the quality of data after the implementation of tagging and build-out of a data set for reporting or analysis.
Automation ensures that tests run more frequently on a schedule than when performed manually. Relevant teams are then alerted of the issue immediately as the issue occurs so that they can take remedial measures before the issue causes a much more significant downstream impact.
How often do you see your traffic spiked significantly, but you were only made aware of it after a few days? How do you track this activity if you are overseeing multiple sites? We set up anomaly alerts so that you get alerted when there is an unusual activity happening on the site.
Report and Dashboard QA
- Do you have data quality issues with your existing reports and dashboard?
- Are you unable to pin down the root cause of these issues?
- Are you constrained for time to debug these issues yourself?
- Do you not trust that the data pipeline is pulling accurate data?
Do you rather have someone do the root cause analysis and fixes while you focus on more pressing issues or important aspects like generating insights for your business?