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.
5 Big Problems with Data Silos
Click through for five ways data silos make data more difficult to manage and analyze, as identified by ZL Tech.
Duplicate Data Copies
Multiple data silos mean multiple copies of the same content. A single email attachment sent out to multiple users may be saved several times – in email archives, file shares, compliance tools, etc. – thus skewing data sampling efforts, eating up storage space, and causing complications in finding the original or most up-to-date version.
Different data silos have different search capacities, with diverse algorithms and accuracy. The same search on the same dataset can provide wildly different results depending on the data silo used, not to mention that silos make it impossibly inefficient to conduct a true enterprise-wide search of content.
Every silo has different capabilities for retention capabilities, not to mention that different silos employed in different business units may be employing completely different retention policies to the exact piece of data. With silos, there is no easy way to know where or if data exists; a piece of data eliminated permanently in one system may still linger in another.
Each new silo spawns a distinct view of data that is usually unique to a particular business function’s needs, with each department favoring their own silos – despite the need to control vastly overlapping data. Dashboards to federate these views often do little to help, simply putting a glossy control panel over data that is still tangled, dirty, interconnected, and slow to retrieve.
Disparate Code Base
Different business applications speak different languages, and the attempt to interconnect silos without a common underlying code base will never be efficient or successful. Cobbled systems may have connectors and APIs, but with different underlying data schemas they will never be able to communicate with the exact same metadata, classifications, or policies.