Imagine you are a teacher and want to store your student information in a simple spreadsheet. You have all the usual columns of information:
Student ID | First Name | Middle Initial | Last Name | Age | Gender | Grade
But you want to add relevant contact information. Now organizing the information gets a little trickier.
You want to add Mother and Father contact information. No problem…except half of your students have step-parents. You need to add a few more columns for Step-Mother and Step-Father. However you would like to have the primary contracts where the student usually lives. So you need another column to store that information and perhaps several phone numbers – work, mobile, home.
Primary Contact | Mother | Contact home1 | Contact mobile1 | Contact work1 | Father | Contact home2 | Contact mobile2 | Contact work2 | StepMother | etc.
Hopefully, you now see the challenge of complexity in just this simple example. It gets messy quickly. Not all real world information fits nicely into rows and columns. Yes, if you plan ahead you could build a complex spreadsheet or relational database to account for possible combinations and permutations, but in corporate settings the data spans many databases and many departments. And rarely do the sources and structure remain static.
The AllegroGraph semantic graph database is structured to easily and simply represent complex, real world structures of data. It is also built to make it easy to add new types of information, on the fly, and integrate that new information without changing the database structure. AllegroGraph works particularly well when the data is located across multiple data sources, such as a CRM database, a social network contacts database, website traffic logs, financial payment systems, public data sources, etc. Lastly with over 80% of all data unstructured, the future is only going to get more complex, not less.