Innovative and efficient search procedure ‘Saxion-entrepreneurs’
Bison code: L.28237
At the moment we have an Excel-database of approximately 1.200 (former) students and (former) employees of Saxion who are an entrepreneur ánd registrered by the Chamber of Commerce. Currently, we use an inefficient and ineffective search procedure via social media (mainly LinkedIn). For example, we have an Excel-database of over 90.000 Saxion-students who once stated that they own a company. Given <name person> we then search via social media for their company name. With the combination <name person> and <company name>, we subsequently search – case by case (inefficiently!) – in the REACH database whether it is indeed a Chamber of Commerce registered company. In addition, we copy some listed company data, for example annual turnover and employment, in our database.
Why is an automated search procedure necessary? Filling and updating our database is a time-consuming activity. Moreover, we wonder whether we do not miss relevant cases. The database is incomplete. Our objective is that we have a database which is up-to-date in an efficient and effective way. Therefore, an new searching procedure is needed.
To be sure: the objective of this project is NOT that the project group updates / completes the database. The objective is that the project group designs an automated procedure searching public and private databases for relevant cases (in our case ‘Saxion-entrepreneurs’ but it might be that the solution will be more broad applicable).
The main question is: what effective and efficient searching procedures ensures relevant cases (in this case: Saxion-entrepreneurs)?
It is beforehand fair to say that it is unknown whether our problem can be solved at all; various experts in digital intelligence stated that they are not sure that a solution exists. Of course, in that case we are happy with the ‘second best solution’.
Central words: digital technologies, big data analysis, algorithms; application of designed solution on Saxion entrepreneurship.