We use measurements in many different situations.
The cook in the kitchen with measuring cups and spoons to make a cake.
The carpenter with tape and t-square to build a house.
The crew of the fishing boat looking for fish in the ocean with sonar and fluorometry.
For the cook and the carpenter the measurements must be accurate, else the cake and the house will fall. And they must be precise, or they could not reliably make cake after cake and build house after house.
The fishing crew measures with a different purpose.
They cannot see and measure the fish directly. They don’t need to know exactly how many fish are down In the depths.
They just want to know with some precision, as they move across the surface, going first in one direction and then another, are there more fish or fewer fish?
In a business, some are like carpenters, others like the fishing crew
Accounting and inventory management and sales forecasts are more like baking cakes and building houses. We expect the measurements to be accurate. People base their expectations and decisions on the results.
Our first instincts are to expect the same from our online marketing activities. Since we can now collect lots of marketing data, we want web analytics accuracy before we make decisions.
Clifton and Kaushik advise that these efforts are misplaced.
Why? Because we are more like fishermen than cooks or carpenters.
We don’t need super-accurate data in online marketing to make decisions. We need to know whether our actions bring more people to our site or fewer, whether they produce more conversions or less.
The graphic above from the Wikipedia article illustrates the difference between these two.
This highlights that repeatable and reproducible results are what marketers, webmasters and web site owners require i.e. Precision. As long as your data reports have this, then your trends will be accurate and your decisions based on solid foundations. I emphasize the word trends above as that is the most important aspect of using web analytics – placing your data in context.
“Move Fast, Think Smart”
Kaushik points out two obstacles in the way of data quality in online marketing:
- the high volume of data we collect
- the imperfections in the data collection system
He cautions against the “futile quest for clean data.”
Instead, Kaushik recommends a six-step process for getting just enough data to find actionable insights and implement them quickly:
You may be surprised to find that if you simply follow best practices, collect only as much data as you need to make a decision, and move quickly, your aim for precision and speed will also lead to web analytics accuracy.