Driving Business Transformation with Big Data

An introduction to big data

Big data analytics is a technology-enabled strategy for gaining richer, deeper, and more accurate insights into customers, partners, and business operations. This collection of tools, techniques, and technologies ultimately provide competitive advantage and increased agility by deriving insight from complex, large data sets. By processing a steady stream of real-time or static data, organizations can make time-sensitive decisions faster, monitor emerging trends, course-correct rapidly, and jump on new business opportunities.

For companies in fields as diverse as travel, genomics, e- commerce, and space exploration, the opportunity of delivering business transformation through these data sets is being driven by on-demand availability of clusters of computers.

Significant technological advances have recently coincided to unlock the value of data for organizations.

Data has long held the opportunity to provide insight into business and society, but two significant technological advances have recently coincided to unlock the value of data for organizations. Fueled by increasing data volumes and throughput, they have co-evolved to empower developers, architects, and data scientists to navigate vast data collections and discover new business models or customer segments and move quickly toward emerging opportunities.

 

The first component is the introduction of software frameworks, such as the Apache Hadoop* framework, which allow data to be easily stored and retrieved at scale by distributing it across a number of different computers and disks, along with providing convenient methods to ask questions of that data. Such tasks would have taken many skilled computer scientists months to concoct only a few years ago; today, getting up and running with a distributed data platform is quick and easy with the Hadoop framework, since the software handles most of the complexity automatically.

 

However, the success of analytics software and the growth of the Hadoop ecosystem would have been delayed had it not been for the dramatic increase in availability of infrastructure to store and query that data, in the form of cloud computing. The second factor driving innovation in this space is the fast provisioning of scalable, secure infrastructure capable of managing modern data, available to anyone at a fraction of the traditional cost. Companies such as Amazon Web Services offers elastic computing services, where infrastructure is delivered as a utility that can be dialed up when needed, and reduced later on. Coupled with the Hadoop framework, the AWS cloud significantly reduces the barrier of entry to large–scale data analytics, while the growth in processing power from Intel Xeon processors has provided the enhanced computation needed to work with increasingly complex data sets today, and the larger sets of tomorrow.

Big data technologies help organizations collect, store, compute, and collaborate around data sets of all sizes, from including the managed Hadoop service, Elastic MapReduce used for scalable analytics.

 

Who is using big data?

Big data techniques are already widely used at a wide variety of businesses, including financial services, social and mobile gaming, online businesses, e-commerce, life sciences, advertising, and media. Smaller start-ups and larger enterprises are finding the value in putting their data, and the data that is available online, to drive greater levels of innovation.

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