Click through for five ways data silos make data more difficult to manage and analyze, as identified by ZL Tech.
Every organization happily daydreams of the perfect Big Data analytics strategy, but the reality of messy IT environments typically renders the situation as more of a nightmare than fantasy. Time and time again, business teams fail to see proportional returns on their analytics investments, despite implementing best-of-breed tools and algorithms. Why? It's not necessarily the tools themselves; it's the data they're being fed.
The biggest perpetuator of data mismanagement is the ubiquitous data "silo." Harboring content in isolation, the prototypical data silo is established with the best of intentions – to solve a specific problem or increase control of a particular data type – but paradoxically makes data harder to globally access and govern over time. With different data types scattered amongst different locations being used for different purposes in different business units, there's no wonder that it's hard to take inventory of the entire enterprise data corpus… let alone leverage it proactively.
Big Data analytics demands a big-picture approach to information governance practices, and silos impede the way forward. Data manipulation is futile without the ongoing effort to cleanse, pool, and maintain resources over time; but silos, by nature, segregate and disperse material. No matter their original intention, a data silos is guilty of several "sins" that must be eliminated. In this slideshow, ZL Tech has identified five big problems with using the silo.
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