Breaking the Silo in Big Data Analytics
In my position as Chief Data Officer for Ness Software Engineering Services, I talk with many companies about their Big Data challenges and needs. Some of these companies have chosen to analyze their Big Data to enable use cases such as online personalization or creating a 360-degree view of their users. Others have been forced into this area by one of their competitors, who announced that, thanks to their successful Big Data initiative, they now possess a competitive advantage over all their competitors; e.g., their products require significantly fewer service calls because they can monitor the performance of the equipment remotely.
If there is one common thread that links 95% of these customers, it is FUD – fear, uncertainty, doubt. The field is full of competing or overlapping products, each of which claims to be the right solution for Big Data. The Big Boys (Oracle, IBM, SAP, Teradata, etc.) all want to steer you towards their (sometimes costly) offering. The upstarts (Cloudera, HortonWorks, DataBricks, DataStax, etc.) all talk about use cases with dramatic savings, but you may not be familiar with the use cases in which their product isn’t designed to work as well. And, depending on your vertical, there are dozens of one stop shops who offer to take your data and come back with vertical...Read more
If there is one common thread that links 95% of these customers, it is FUD – fear, uncertainty, doubt. The field is full of competing or overlapping products, each of which claims to be the right solution for Big Data. The Big Boys (Oracle, IBM, SAP, Teradata, etc.) all want to steer you towards their (sometimes costly) offering. The upstarts (Cloudera, HortonWorks, DataBricks, DataStax, etc.) all talk about use cases with dramatic savings, but you may not be familiar with the use cases in which their product isn’t designed to work as well. And, depending on your vertical, there are dozens of one stop shops who offer to take your data and come back with vertical...Read more
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