In today’s increasingly online world, it’s no surprise that the collection and analysis of data is becoming vital to the business decision-making process. However, in order to turn data into actionable insights for your organization, it’s important that proper planning takes place, that the right teams are involved, and that implementation, testing, and analysis are all achieved in order to assure quality data and decisions. Without confidence in your ability to extract and refine the major resource that is big data, your analytics and strategy teams will fall short in their ability to make insightful business observations and recommendations. This presents the question: how confident are you in your analytics?
Know what you’re tracking
When it comes to your data, start by asking the “who, what, where, and why.” Who are you trying to track? What are you collecting? Where are you collecting from? Why are you collecting the data that you are? Start broad and ask yourself – “What are the key performance indicators (KPIs) and what data can I use to create actionable insights that impact key business decisions?” Then, narrow it down to the important details – “What do I consider a full conversion, versus a step along the path to conversion?” These fundamental questions will ensure that you are always collecting the right data by segmenting your traffic with the right users and behaviors, to tie the data back to your business objectives.
As with all endeavors, getting it right the from the start is key. In the analytics world, this means proper planning and implementation. Begin all analytics projects with clear communication amongst teams, making sure all tracking efforts are inclusive of your analytics and marketing teams to ensure focused, relevant insights. While it would be ideal to get all analytics implementations correct off the bat, most data quality issues are detected once data collection is already in motion. You can’t plan for everything, but you can save yourself a lot of valuable time by starting on the right foot.
Uncover any issues
Once you are aware of what you’re looking for and what normal is for your current data points, you can start looking for the oddities and misrepresentations that you may find.
When you have an inclination that your data is not being collected properly, it is important to conduct an audit on your analytics implementation, segmentation, and tagging efforts. Conduct a step-by-step approach to troubleshooting, working from the outside-in. Start with the most specific items, such as the triggers you set or the tag associated with your concern. Once you have confirmed they are correct, move inwards to the implementation – for example, are tags only working sometimes? Is there something else that could be firing this tag elsewhere? This includes going into your tag manager, whether it be DTM, GTM, hard-coded tags, etc., and reviewing them. Your review should go as deep as your potential problem.
Dive deep
I recommend going back to your initial implementation and core site code to further troubleshoot your data quality issues. When approaching an analytics audit, think of repairing a bike – if the wheel does not rotate, you start investigating the pedals, then the gears, then the shifting mechanism. You will need to diagnose down and if you have to change your core, you need to make sure that the core is truly the issue.
It’s important for you to have a full understanding of your data, the goals you have set for it, and a working knowledge of how you are collecting it. Once you have these items, you can start looking for concerns, troubleshooting issues, and begin feeling confident that your analyses are correct and complete. Look out for another post on this topic, where we delve further into common mistakes that occur when repairing data, and how to correctly fix issues concerning data quality.
