What is wrong with Business Intelligence? Do we need Enterprise Data Lakes?

Everybody knows that you have to perform following vital tasks in order to solve business intelligence problems: * You have to involve your IT. * Your IT has to model your data (build a data mart by specifying how your data are to be aggregated). * Your IT might have to harmonize your master data. * Your IT has to cleanse your source data. * Your IT has to populate your data mart with a batch run. * Your IT has to train you how to use your data mart. In essence you need to know what you want to know before your are getting your answers. There is something wrong with this picture. What if you could start the other way round? What if you don’t know the answers before asking your questions? Would a data lake method be the better approach? What is a data lake? “A massive, easily accessible data repository built on (relatively) inexpensive computer hardware for storing “big data”. Unlike data marts, which are optimized for data analysis by storing only some attributes and dropping data below the level aggregation, a data lake is designed to retain all attributes, especially so when you do not yet know what the scope of data or its use will be.” (http://en.wiktionary.org/wiki/data_lake) An enterprise data lake could shift the upfront IT effort till later or sometimes forfeiting it at all. You could experiment with your data right away. And determine later where and when your IT gets involved. Sounds like a pipe dream. Or? Talk to us if you are curious to learn how our customers are leveraging...