• I think it is getting every possible piece of information from transactional systems instead of just getting data specific for reporting. This gives you more potential for analysis but at the same time, it also creates sizing issues and information overload.

    This is especially bad with those end users who request that they see all the data so they can analyze it when in fact they just want to see all the data because they have no idea what is in it and want to aggregate and slice it from every possible angle.

    The upside is that you can produce better forecasting models and such using data of this grain but it often takes a while for the business to feel comfortable with the data. So you end up storing a lot more data than what you really need. And usually once you have started to store it, it becomes critical that you keep all of it even if it has never actually been accessed.

    So to me, big data means retaining all of the data instead of just what is deemed as required. It is essentially the opposite of what data professionals have been trying to do for years by narrowing scope during the requirements gathering process of ETL development.