Find out about the democratisation of data analysis, as users catch on to the value of analytics and move to take on more of the work themselves
Organisations are drowning in data. The age of big data has brought with it enormous opportunities for organisations that are able to extract insight and value from all that data, but doing so requires an entirely new approach to thinking about data management and analysis, and an entirely new set of tools.
The true value of big data lies in the use of analytics tools, which range from basic business intelligence reporting to predictive analytics and machine learning. View your copy to find out about:
New approach to data management
See how organisations are rethinking the hardware infrastructure that supports big-data analytics. Analysing big-data platforms are based on ‘scale-out’ infrastructure, whereas traditional data warehouses and business intelligence systems are ‘scale-up.’
Big data driving forces and trends
Dig into the 3 principal reasons organisations deploy big data: improving operational efficiency, creating new revenue opportunities, and driving transformational change. Gain insight into how big data adoption is maturing, via analytics tools and collaborations on common data sets. View the shifts between 2016 and 2017, spanning data centres, colocation, managing services providers and public cloud environments.
The popularity – and challenges – of deploying big data in the cloud
Learn more about current trends, where enterprises are moving from traditional storage platforms (SAN, NAS) to all-flash, hyperconverged infrastructure and other storage and technologies. Explore how software vendors are solving the complexity of Hadoop, and why cloud is expected to become the primary integration point for big data projects.
Discover how workers can create advanced analytics and business intelligence models – without necessarily being a data scientist or analyst.
3 required aspects of transformation
Get best practice strategies for managing and leveraging the growing data volumes. Areas covered include transformation of processes, infrastructure and information.
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