Until the mid-1970s, most systems were built using functional systems. Object-oriented systems were introduced with a flurry of promises in the early ‘80s, many of which actually proved to be true, for once.
More recently, people have been talking about object-based systems, object stores and object-based file systems. In this article, I’d like to clarify the characteristics of each type of technology. Truth in advertising—there’s a lot of overlap, so I’ll try to smooth out the bumps in the ride.
These days, most large organizations have a plan for big data integration (see Figure 1), that is, to collect and analyze their big data assets from many sources: For instance, e-commerce businesses have the tools to sort through CRM databases for order logs, customer correspondence, and delivery information, and can pair that data with historical weather records to assess how the temperature impacts when customers are most likely to order certain products, or how changes in weather have historically impacted delivery schedules.
Almost any popular, fast-growing market experiences at least a bit of confusion around terminology. Multiple firms are frantically competing to insert their own “marketectures,” branding, and colloquialisms into the conversation with the hope their verbiage will come out on top.
Add in the inherent complexity at the intersection of Business Intelligence and Big Data, and it’s easy to understand how difficult it is to discern one competitive claim from another. Everyone and their strategic partner is focused on “leveraging data to glean actionable insights that will improve your business.” Unfortunately, the process involved in achieving this goal is complex, multi-layered, and very different from application to application depending on the type of data involved.