In my previous post, I have outlined what Data Elicitation was about. I have introduced the three areas required for a proper eliciting process, e.g. Data Patterning, Data Enrichment, and Data Analytics. This post will deal with the first of these areas, “patterning“.
First, it is worth explaining why I chose this word. I have used a typical concept from the textile industry (or, to be a bit more ambitious from the “Haute Couture” world). In this area, a pattern is the intermediate stage between the designer’s sketches and the item production, a formalized plan enabling industrial planning. On one hand, it still is a concept, like the sketches, as it is purely paper. But on the other hand, it already is production, as it includes all the necessary information for implementing a full production process. This is what patterning is all about, allowing people’s ideas to become physical shapes.
Patterning data is an essential step for data management,as it allows to take stakeholder wishes and technical constraints into account, and prepare an optimal project and development planning.. No database is suitable, if not driven by clients’ needs and requests. No data analysis is relevant if not aligned with the previously agreed pattern.
A few key questions in this respect:
- What are my data made of and, even more important, made for? → as one finds its way better when the ultimate goal is known…
- How can data sets be best organized? → the proper content ought to be in the proper place
- What content do I need to store to get the best out of my data? → since not every piece of information may be worth keeping
- How may my data relations be best optimized? → data are more useful when they are properly linked and aligned
I have summarized a typical process in the table below, in three columns; a fashion analogy, some project management steps and a basic (softened) example inspired by one of my previous experiences in the mobile world:
The parallel with the creative flow in the Fashion industry is strong, as it shows that the first half of the process (steps #1 to #4) is the true added-value to the whole content, the second half being more execution. It is clear that botching the patterning phase will impede the proper completion of the project. In the case above, the proposed solution could be summarized in a small chart, as the information laid in two fields of the provided log files.
The two attributes (fields) that were present in the log files are marked here in blue.
The TAC (the first part of the IMEI code) may be directly used, as it relates to one device model; the master database is maintained by the GSMA, and is delivered to its members (including Telecom Operators).
The User Agent is more complicated, as it includes entangled information; parsing the User Agent will allow namely to identify browser, OS and type of connection that have been used. Still, it does not require additional information, only a good content analysis and a solid set of coding rules.
The combination of these four items creates a unique identifier, which is not specifically related to given users, but creates homogeneous groups, sharing similar technical conditions (hardware, software, network). Each group will then receive contents adapted to their specific conditions, thereby optimizing their browsing experience and consequently increasing engagement.
As this blog is aiming at a large public, I chose to keep a rather simple example. Of course, should the matter be more intricate, the skills I have built up over my years of experience in Data Management will even be more valuable. Feel free to ask more about patterning or other fields of Data Elicitation, I shall be glad to elaborate customized solutions for your business.