Most of the time we work with aggregated data. What is it? It is transformed chunks of information composed from thousands of pieces of data. Usually, we use it to build charts and graphs, reports and dashboards. Aggregated data helps managers make decisions and draw conclusions about their businesses’ performance. We often see aggregated data in services like Google Analytics, where various pieces of information come together to build full pictures of trends.
But what should we do with raw data, meaning unprocessed content of hits? Turns out, raw data is just as useful as visual diagrams. Why? We’ll tell you in a second.
To eliminate inaccuracy caused by sampling
Sampling is a method, where to build a report, we use a small amount of data (which is “surprisingly” called a sample), proportional to the overall population. The results, in this case, are shown as if all of the data was taken into consideration when building dashboards. Sampling is widely exploited by services like Google Analytics and is used for processing larger amounts of data. Sampling could be useful in lots of different situations, however, it sometimes stands between users and objective conclusions. Here we have to understand that Google Analytics only illustrates trends and patterns — result of the analysis of a sample.
We certainly can avoid sampling by using the paid version of Google Analytics, but its price can go as high as 100.000$/year which is only affordable for large businesses. The same raw data could be obtained through much cheaper ETL- services.
To set up targeted ads
The composition of narrowed-down user lists and the formation of cohorts could only be done with raw data. For example, you want to highlight those who have been loyal to your brand in the past year and have purchased within your special offers periods. What do we have here? Different activities, different times, same clients. These people are hardly detachable from the rest of the customers, and that’s why you will need the retrospective information on the user activity to form needed segments for your targeted ads.
And what if you’re collecting audience for retargeting? Raw data is there for you to connect all the advertising networks and channels for your customers to come back to buy more or to leave a positive review of your service.
To understand why data doesn’t match
As we’ve already mentioned, Google Analytics is a tool that only evaluates trends, therefore, we can’t expect it to be 100% precise. Sometimes the information about spendings and clicks can differ: let’s say you’ve set up data transferring from one advertising channel via two different instruments. In this case, manual matching of data can reveal up to 30% discrepancy, which, of course, will be reflected in the final efficiency estimation.
You can be sure of the precision of the information only with raw data. It will indicate what went wrong, and you will have an opportunity to quickly react to any problems related to doubling or absent data.
For custom analytics
Using raw data, analysts can go into more depth, and thus optimization of internal mechanisms of the company have higher chances for success. Plus, initial data could be connected to data from other sources and you can draw conclusions based on them. If you need to analyze business-processes or evaluate the impact of advertising on offline sales, raw data will assist in using any of the data cuts and easily adapt it to your system.
Only raw data reveals where and when suspicious activities, such as too many daily registrations or awkward time frames between similar actions, took place on your website. Moreover, information about hits assists in catching bots and spam-filtering.
To change providers and instruments
While you store only aggregated data, you’re tied to certain contractors and systems. Right as you start collecting raw data, you’re free to switch from one system to another and from one partner to a different one.
What if you’re receiving data from your tracking provider in real-time, and the connection is suddenly lost? Raw data! It’s always available for downloading and doesn’t depend on the stable connection.
For unlimited amounts of data
Raw data can be collected in any volume without restrictions on the number of lines. Thus, when you need to “dig deeper” and see separate hits, raw data will come in handy.
For special cases
Before uniting data and make decisions based on it, we often have to get rid of some of the incorrect outcomes. For that, we need to access raw data, as with aggregated data, filtration is reduced. Typically, this “cleansing” is required when assessing the results of sociological research or polls.
Collecting raw data is easier than you may think — you just need one instrument for automized streaming, which will get the job done. Raw data can be helpful in all states of your company’s work process, that’s why you better start doing it now and use this information at any needed time.
Hopefully, the number of times we’ve mentioned “raw data” in this article has convinced you to go and collect it, and your businesses’ optimization will soon start showing desired results.