To round off our latest article series on the challenges of data quality and the risks associated with unreliable data, here is a summary of the 28 quality criteria essential to improve marketing performance, campaign success and strategic decision-making. 

1 – Certified measurement  

Choose a digital analytics partner whose measurement is certified by independent multimedia measurement organisations such as ACPM (France), OJD Spain, TÜV Saarland (Germany), ABC (United Kingdom), KIA-INDEX (Sweden) and ENED (Greece). 

2 – Identified (and excluded) robot traffic 

Your analytics supplier can identify these robots using an official IAB exclusion list that is regularly updated.  

3 – An independent analysis tool 

An analytics solution provider should not be both judge and jury. There must be no reason or motivation to overestimate or manipulate the reported data, nor any conflict of interest in your activity. 

4 – A transparent algorithm on its calculations 

Your digital analytics supplier must be able to explain to you, in complete transparency, how the indicator calculations are carried out. In other words, to help you understand what is included in the algorithm. Beware of black boxes that do not offer any possibility of human interaction when calculating your data. 

5 – Data control procedures  

As an integral part of your good data governance, regular procedures (automatic tests for example) allow you to check the presence of all tags. 

6 – Complete tagging audits 

They must be carried out in particular in the event of a very important modification redesign of your site and/or applications. 

7 – No sampled data 

They can highlight general trends, but the smaller their size, the less representative they are of reality. Sampling can be risky, especially on financial data.  

8 – Service contracts (SLAs) 

Your Web Analytics provider is contractually committed to guaranteeing you a data collection rate close to 100%.

9 – A first domain measurement 

You recover as much of the traffic blocked by ad blockers or ITP systems as possible via a collection solution on your own domain name.
Important note: the CDDC method available from AT Internet is fully compliant with the GDPR and is not exempt from the collection of required consent for all websites. 

10 – Accessible and well formatted data  

Test how your data is retrieved and presented. Is it being collected as planned? Are the values displayed correctly on your interface? 

11 – Fair and verified values 

Check that no technical (collection) elements could have altered the accuracy of the figures in your analysis. 

12 – Tools for customising tagging  

By defining customised data processing rules, you are able to correct errors due to tagging problems, enrich collected data and exclude unwanted traffic, all without any intervention from technical teams. 

13 – Automated quality control 

Consider using tools that will allow your analyst to update the data independently, without relying on technical teams to modify the code. 

14 – Real time data  

Verify that your digital analytics solution can provide you with real-time KPIs, i.e. that the data availability does not exceed 5 minutes on your analyses and dashboards. 

15 – A reusable data 

Ensure that your analytics data is reusable for customer re-engagement, push notifications, re-targeting and e-mail campaigns.  

16 – An exportable data  

You must be able to extract millions of events every hour to feed your IS or data lake (or other Data Science project) with highly granular and up-to-date analytical data. 

17 – Data that can be understood immediately 

Use the customer support experts at your analytics supplier (if any) to decrypt your data quickly.  

18 – Increased data 

Use the Machine Learning features of your analytics solution to anticipate or predict possible urgent and critical events. 

19 – Governance in place  

The collection, processing and use of data is part of a governance strategy initiated by the company management and driven by a CDO or data expert. 

20 – Comparable indicators  

Determine which analytical solution serves as a reference for the entire company. Then, more precisely define and calculate the indicators to be used as benchmarks in the different services. 

21 – Common analysis tools 

Use dashboards and analytical reports that can be adapted to all types of data users in the company: everyone can base their analyses and decisions on data collected, calculated and processed in a consistent way. 

22 – A cross-device vision  

Always keep an overview when analysing your performance. Avoid approaches that are too platform or device specific, as they may miss important insights. 

23 – Exact data  

The law requires that the data be accurate to be compliant. You must take reasonable steps to avoid processing incorrect data (imagine, for example, the consequences of data errors in a medical record). 

24 – A collection of lawful consent 

It is also a condition of compliance. Some Analytics solutions such as AT Internet benefit from an exemption from data collection, which makes measurement easier and more reliable. 

25 – A limited cookie lifetime 

If the Internet user has given his or her consent, your Web Analytics solution must offer functionalities to ensure that the lifetime of cookies is respected. 

26 – Complete and transparent information 

It is a duty to the Internet user. These principles concern the processing of data and in particular: duration and place of storage and type of data collected. 

27 – A clear and precise definition of personal data 

This is one of the obligations of the GDPR (section 14). At AT Internet, all types of analytical data are considered as personal data. 

28 – Data accessibility 

You and your subcontractor must have the capacity to respond to the rights of the persons concerned. AT Internet is organised on this point.  

AT Internet offers a wide range of tools for quality control of analytical data. Fewer errors are therefore likely to alter your data and influence your decisions. 

If you are interested in the subject of data quality, download our latest guide:

Data Quality in Digital Analytics updated in 2019

Photo credits: Glenn Carstens

Author

Editorial Manager. Bernard was responsible for the content strategy of the AT Internet brand. He has almost 10 years’ experience in technical and marketing writing in the Software industry. A word specialist, Bernard’s job sees him working on many different mediums including blogs, white papers, interviews, business cases, press, infographics, videos etc. His specialist fields? Marketing and digital analytics content of course!

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