{"id":1840,"date":"2016-08-18T09:14:25","date_gmt":"2016-08-18T03:44:25","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=1840"},"modified":"2016-10-06T18:58:06","modified_gmt":"2016-10-06T13:28:06","slug":"big-data-errors-that-enterprises-make","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/big-data-errors-that-enterprises-make\/","title":{"rendered":"Big Data: Errors that enterprises make"},"content":{"rendered":"

\"errors-enterprices-make\"<\/a><\/p>\n

<\/h6>\n

Big Data implementation can help organizations to incorporate additional data sets into their existing data infrastructure, allowing them to question anything from their data sets. This seems very practical given the advancement in technology and commoditization of infrastructure, but there are many errors that organizations need to avoid.<\/p>\n

\"Big<\/a><\/p>\n

Mistakes in handling Big Data<\/em><\/p>\n

Absence of a Business case<\/h1>\n

The complexity of Big Data is widely known and it becomes very difficult to understand when there is no business case associated with the value that organizations can derive when they incorporate it into their decision support platform. There should be clearly developed requirement for gaps in an appropriate business case.<\/p>\n

Reducing data relevance<\/h1>\n

There are various shapes and sizes that Big Data can assume, and it is all around us. The key to succeed in Big Data initiatives for a business is to understand the relevance of these data sets with respects to the needs of the business.<\/p>\n

Basically, three types of Big Data exist today:<\/p>\n