7 deadly sins of testing – Not Understanding Your Data

It doesn’t take long working in a data field for you to come across data being used in ways other than what it was intended for. George Canning once correctly quipped, “I can prove anything by statistics except the truth.” One of the hardest struggles for anyone trying to make sense of all the various data sources is an understanding of the data that you are dealing with, what is it really telling you, what is it not telling you, and how should you act. We have all this rich interesting information, but what is the right tool for the job? What is the right way to think about or leverage that data? One of the ways that testing programs lose value over time is when they stop evaluating their data with a critical eye and focus on what is it really telling you. They so want to find meaning in things that they convince themselves and others of answers that the data could not ever provide. Understand your data, understand the amazing power that it can provide, and understand the things it cannot tell you.

Every tool has its own use, and we get the most value when we use tools in the correct manner. Just having a tool does not mean it is the right fit for all jobs. When you come from an analytics background, you naturally look to solve problems with your preferred analytics solutions. When you come from a testing background, you naturally look for testing as the answer to all problems. The same is true for any background, as the reality is when we are not sure, you are wired to turn back to what you are comfortable with. The reality is that you get more value when you leverage each tool correctly, and the fastest way to do that is to understand what the data does and does not tell you from each tool.

Analytics is the world of correlative patterns, with a single data stream that you can parse and look backwards at. You can find interesting anomalies, compare rates of action, and build models based on large data sets. It is a passive data acquisition that allows you to see where you have been. When used correctly, it can tell you what is not working and help you find things that you should explore. What you can not do is tell the value of any action directly, nor can it tell you what the right way to change things is.

Testing is the world of comparative analysis, with only a single data point available to identify patterns. It is not just a random tool to throw one option versus another to settle an internal argument, but instead a valuable resource for active acquisition of knowledge. You can change part of a user experience and you can see its impact on an end goal. What you can not do is answer “why?” with a single data point, nor can you attribute correlated events to your change to each other. You can add discipline and rigor to both to add more insight, but at its core all testing is really telling you is the value of a specific change. It is beholden on you for the quality of the input, just as your optimization program is beholden on the discipline used in designed and prioritizing opportunities.

Yet without fail people look at one tool and claim it can do the other, or that the data tells them more then it really does. Whether it is the difference in rate and value, or it is believing that a single data point can tell you the relationship between two separate metrics. Where we make mistakes is in thinking that the information itself tells you the direction of the relationship of that information, or the cost of interacting with it. This is vital information for optimization, yet so often groups pretend they have this information and make suboptimal decisions.

We also fail to keep perspective on what the data actually represents. We get tunnel vision on what the impact is to a specific segment or group that we lose the view on what the impact to the whole is. To make this even worse, you will find groups targeting or isolating traffic, such as only new users, to their tests and extrapolating the impact to the site as a whole. It does not matter what our ability to target to a specific group is unless that change will create a positive outcome for the site. The first rule of any statistics is that your data must be representative. Another of my favorite quotes is, “Before look at what the statistics are telling you, you must first look at what it is not telling you.”.

Tools do not understand the quality of the inputs, it is up to the user to know when they have biased results or they do not. Always remember the truth about any piece of information, “Data does not speak for itself – it needs context, and it needs skeptical evaluation”. Failure to do so invalidates the data’s ability to make a the best decision. Data in the online world has specific challenges that just sampling random people in the physical world does not have to account for. Our industry is littered with reports of results or of best practices that ignore these fundamental truths about tools. It is so much easier to think you have a result and manipulate data to meet your expectations then it is to have discipline and to act in as unbiased a way as possible. When you get this tunnel vision, both in what you analyze or in the population you leverage, you are violating these rules and leaving the results highly questionable. Not understanding the context of your data is just as bad or worse then not understanding the nature of your data.

The best way to think about analytics is as a doctor uses data. You come for a visit, he talks to you, you give him a pattern of events (my shoulder hurts, I feel sick, etc..). He then uses that information to reduce what he won’t do (if your shoulder hurts, he is not going to x-ray your knee or give you cough medicine). He then starts looking for ways to test that pattern. Really good doctors use those same tests to leave open the possibility that something else is the root cause (maybe a shoulder exam shows that you have back problems). Poor doctors just give you a pain pill and never look deeper into the issue. Knowing what data cannot tell you greatly increases the efficiency of the actions you can take, just as knowing how to actively acquire the information need for the right answers, and how to act on that data, improves your ability to find the root cause of your problems.

A deep understanding of your data gives the ability to act. You may not always know why something happens, but you can act decisively if you have clear rules of action and you have an understanding of how data interacts with the larger world. It is so easy to want to have more data, or to want to create a story that makes it easier for others to understand something. It is not that these are wrong, only that the data presented in no way actually validates that story nor could provide the answers that you are telling others that it does. In its worse, you are distracting from the real issue, at its best, it is just additional cost and overhead to action.

The education of others and the self on the value and uses of data is vital for long term growth of any program. If you do not understand the real nature of your data, then you are subject to biases which remove its ability to be valuable. There are thousands of misguided uses of data, all of which are easy to miss unless you are more interested in the usage of data then the presentation and gathering of data. Do not think that just knowing how to implement a tool, or knowing how to read a report, tells you anything about the real information that is present in it. Take the time to really evaluate what the information is really representing, and to understand the pros and cons of any manipulation you do with that data. Just reading a blog or hearing someone speak at a conference does not give you enough information to understand the real nature of tools at your disposal. Dive deep into the world of data and the disciplines of it, choose the right tools for the job, and then make sure that others are as comfortable with that information as you are. It can be difficult to get to those levels of conversations or to convince others that they might be looking at data incorrectly, but those moments when you succeed can be the greatest moments for your program.


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