November 04, 2022
Data approximation is commonly used to make certain data understandable and readable. But in the marketing field, this approximation is no longer desirable.
Data Approximation is the process of using mathematically sound methods to calculate approximate outcomes from available data. While the methods of calculation are sound, the outcomes cannot be certain when dealing with approximations, and yet, without realizing it, approximation is everywhere, even in the most serious fields.
The intention behind this process is to make complex data understandable to as many people as possible. While in certain contexts and for certain uses, simplifying data is fully justified (for example the way countries often approximate their yearly GDP), it can be problematic for businesses in its marketing use. Especially since the technology for generating data has developed considerably and exploits increasingly colossal volumes. What’s new is the more complex ways of processing data, the increase of sources from which data is sourced, the increasingly sophisticated algorithms the data is subjected to, or data architectures becoming more and more labyrinthine.
When it comes to addressing prospects, the consequences of approximate use of data can be minimal, especially in the case of proposals or advertisements that do not require a high level of commitment. It is then possible to reach part of your objectives with approximate data. But you lose effectiveness. On the other hand, when it comes to addressing people who are already customers, the consequences can be much more serious. The reading contract with the customer of a brand or service must be handled with a rigor that only the most accurate data can provide.
However, outside of data quality professionals, this subject seems to be of little concern in most businesses. You only need to look at the programs of webinars and data events to be convinced. Speakers who talk about end-to-end data quality are rare. Everything seems to implicitly indicate that the data used is reliable and of good quality. However, this is far from being the case. The topics discussed during these events exclusively concern data collection, RGPD-related aspects, and rectified data. The rest of the data journey is still a subject to which the dedicated teams still do not devote enough importance.
If data quality does not seem to be everyone's priority in France, in Europe, it is a different story. Some Swiss companies have become masters of "end-to-end" quality management and have set up dedicated teams, tools and processes to guarantee a more than convincing result. Too often in France, this aspect of data is treated, when it is, in an overly traditional manner and is only managed when the team has enough time to do so.
However, there are algorithms that can be used to monitor the quality of data, and specialized editors can provide adapted solutions. There are also teams of experts on this issue. Of course, this requires financial and human resources. Large companies and groups deal with such large volumes of data that only algorithms can sort out the good and bad data. This titanic task cannot be done by data scientists alone. It is therefore key for these companies to seize this subject as soon as possible and to put an end to approximation.
At Valtech, we don’t use approximate data when deploying our Data Science services for clients. Our end-to-end quality management focuses on every aspect of data use and analysis, and we make it a point to only make recommendations based on all the most accurate data available. Call us today to learn more about what our data services teams can do for your brand.
