Data are and have always been what feeds information systems as well as corporations. In the last years, the ability to collect, store and generate data have increased exponentially. It is therefore critical to identify, within this ocean of data, the ones that are critical and those that are superfluous. Although those non-useful data are no longer a technical problem thanks to increased storage and Big Data technologies, they make it harder to manage the business.
Indeed, after they are collected, those useless data are easy to identify but they can require a lot of space unless there are controls in place to handle them. It is therefore necessary to continuously check your methods of acquiring data via data quality checks.
What is the point of Data quality?
In the last few years, « Digital Transformation » and « Big Data » have become important issues for corporations. Those concepts are now key to the omnichannel strategy of all corporations. However, this tactical shift has nos happened smoothly. All information that populate your databases cannot be exploited and some may not even be accurate ! Unfortunately, those incorrect data are a major issue. According to IBM, in 2016, the cost generated annually by data quality issues was US$ 3,1 Bn, for the United States only.
What about online advertising ? This particular sector is no exception. Most campaigns you track cannot be understood on the basis of the data you receive. Most must be analyzed and the data reworked first if you are not to generate absurd results.
It is now commonplace to track the Web performances of your company using existing technology.
However, people really drill into the quality of the data they generate. A few years ago, the amount of data collected was small enough that is was possible to make corrections in real-time. Today, the opposite is true. The amount of data available no longer lets you see « with a bird’s eye » if the result of your campaigns makes sense or if you need to adjust the data
So what if you cannot trust your data?
If your business is mostly online, being able to trust your data is a must.
Useless data can impact your omnichannel strategy in several different ways. The first impact has to do with volume. Even if your IT system can store all this data, the related cost of maintenance and storage if far from marginal. Indeed, if you collect information on a visitor (visit to your Web site, conversions, etc …..) but this information is not accurate or is not deduplicated, this can have severe negative consequences on your analyses
What is at stake here is important. Data analysis is the engine of all corporate strategies. Indeed, according to a recent OMC survey, using web analytics in decision-making has increased from 30 % to 42 % in the past five years. More than half of all corporations use data from the Web and think it is a positive.
However, not all players control these informations. According to Halobi, irrespective of whether a corporation is big or small, anywhere between 20% to 40% of the data being used are not correct.
If you are using data from the Web, have you ever asked yourself the following question: do my data reflect reality ? If the answer is no, do not worry, below is some advice as to how to optimize the exploitation of your data.
Improving data control for more accuracy
Despite being careful, it is hard to have only high-quality data. But this does not mean that no action is possible to optimize data quality. The advice below will help you reduce the share of inaccurate data to a non-material level.
Track what you need, but track it well!
Why talk about tracking here ? Because it is the first collection process to take into account data quality.
The goal here is not to offer a guide to tracking your campaigns well.
Before you launch any campaign and set-up any tracking plan, you need to make sure that all elements are consistent. Whether it is the name of your campaigns, the UTMs you use or the activation period, it is critical to make sure everything is right. If you have made a mistake and you have not added the right tracking code for your campaign or you have not input the right information, numerous issues might arise.
Deduplication of data, non-collection of informations from a campaign etc… are common problems that you may run into if you don’t pay enough attention to this set-up phase.
Jolt down precisely the information of each of your campaigns (name, activation period, UTMs used, the partners involved in your campaign. All this information to make sure you are not forgetting something and you won’t have to rework those parameters at some point in time.
Well-tracked campaigns, is this all there is regarding data quality?
Unfortunately, this is not the end of it. Making sure you have high-quality data is not a one-time thing. It has to be monitored continuously.
A concrete example : you are part of a corporation specialized in e-commerce. To understand the conversion paths and the interactions of your users, you have decided to measure the performance of your website and your advertisement campaigns. You have set-up your tracking plan and your campaigns including the analytics tracking UTMs
You then wait until your database fills up. In theory, your data are properly stored and classified. And you can access them whenever you need to.
However, in real life, your data storage is quite messy : your data are scattered in every direction and some of the information is missing. As a result, you may find what you are looking for but this will require much more time than anticipated and you may not be sure to have all the data you wanted.
One of the recurring problems in web data analysis is thus structuring your database and making sure nothing is missing.
And this method is time-consuming and costly. You will spend more time cleaning up your data than analyzing them to generate insights for your strategy.
Wizaly’s advice : check once a month (or more if you can) the main KPIs of your business in comparison to your last period of activity. If there is an unexpected fall in traffic from one month to the next, you can take correcting action. The keyword here is consistency!
Having high-quality data matters for your company.
This will let you :
- Provide more accurate and more precise marketing
- Make the right decisions for the future of your company thanks to reliable information
- Increase customer satisfaction and lower opportunity costs
It is true that setting up quality data collection or checking your data can be time-consuming. But all in all, you will spend less money and cleaning up your data will generate increased revenues (since your analyses will accurately reflect reality).
If you follow this quick piece of advice, you won’t hit major issues. The hardest part remains : maintain this data quality process as time goes. Let’s end this article with a comparison : data collection is not a sprint but a long haul race ! Be consistent with your data quality process to exploit your data in the best possible way.
Do not forget : only after making sure you have high-quality data will you be able to use it, not before !