Research on Big Data

Topics: Business intelligence, Data management, Data warehouse Pages: 23 (6418 words) Published: October 29, 2012
w h i t e

p a p e r

big Data analytics:

Future architectures, Skills and roadmaps for the CiO

September 2011

by philip Carter

Sponsored by

w h i t e pa p e r

big Data analytiCS:

Future architectures, Skills and roadmaps for the CiO

Brave New world oF Big data
the ‘big Data era’ has arrived — multi-petabyte data warehouses, social media interactions, real-time sensory data feeds, geospatial information and other new data sources are presenting organisations with a range of challenges, but also significant opportunities. IDC believes that as CIOs start to adopt the new class of technologies required to process, discover and analyse these massive data sets that cannot be dealt with using traditional databases and architectures, it will become clear that the real value will be derived from the high-end analytics that can be performed on the increasing volumes, velocity and variety of data that organisations are generating – or Big Data analytics. One of the key differences between analytics in the traditional mode, and what we are dealing with in terms of the big Data era is that we are gathering data that we may or may not need – and from the perspective of analysis, this means ‘we don’t know what we don’t know’ – hence, the variables and models are likely to be entirely new, requiring a different infrastructure strategy and perhaps most importantly, new skill sets. the objective of this white paper is to explore the initial impact that big Data is having on organisations, particularly the it departments – which is being forced to re-assess architectures, delivery models and future roadmaps. It will explore the following areas in more detail:

DefInIng BIg Data. this is not in the context of the quantity or threshold that actually quantifies Big Data (as this is changing all the time, and will be applied differently, depending on the vertical and market segment), but more in terms of a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-speed capture, discovery and/or analysis.

haDOOp, mapreDuCe, Key Value StOre? there is a lot of hype around the new technologies that are being used by the market to deal with the Big Data phenomenon. We will highlight some of these and their relative importance. the Value Of BIg Data… In analytICs. the bottom line here is that it is getting more complicated to process and analyse these 1

big Data analytiCS:

Future architectures, Skills and roadmaps for the CiO

large and growing data sets – and it essentially requires a re-assessment of the broader information management strategies for the majority of organisations that have started their business analytics journey. Why BIg Data analytICs Is ImpOrtant (anD DIfferent). many have asked the question – what is new with this trend? this section will highlight the traditional use of business analytics in the old ‘pre-big Data’ world, versus big Data analytics in the ‘new World’. this will also look at the various use cases that iDC expects to see being most commonly used across a variety of industries. the SKill FaCtOr – the riSe OF the Data sCIentIst. With the raft of new technologies and organisational structures

that need to be put in place as the big Data phenomenon becomes a reality, there will be increasing demand for ‘data scientists’ – the next-generation analytical professionals who are able to extract information from large data sets and then present value-added content of business value to non-data experts – who also have the unique skill of understanding the new models that need to be put in place. mapping Out the big Data analytiCS JOurney. the big Data analytics journey will be an iterative one – it is therefore important to map this out in the context of a broader framework. this section aims to do exactly that, and also provide some recommendations to CiOs as they embark on this...
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