Databases Reference
In-Depth Information
The company, in its transformation journey, is now positioned to realize four key
benefits from its big data and analytics strategy:
•
Delivers consistent information faster and more inexpensively.
•
Summarizes and distributes information more effectively across
the business to better understand performance and opportunities
to leverage the global organization.
•
Develops repeatable and defined BI and analytics instead of every
group reinventing the wheel to answer similar questions.
•
Generates value-creating insights yet to be discovered through
advanced analytics.
End Points
The massiveness of data and the complex algorithms it requires is an important issue;
but it isn't the most important one. To manage big data you don't have to set up a massive
scale of hardware infrastructures anymore; cloud services have given us the capability
to run very large server clusters at a low startup cost. Open-source projects from Google
and Yahoo have created big data platforms such as the Hadoop ecosystem, enabling
processing of massive amounts of data in a distributed data-processing paradigm.
These technology evolutions have accelerated a new class of data-driven startups, it has
reduced both marketing costs and the time it takes for these startups to flourish. And it
has allowed startups that were not necessarily data driven to become more analytical as
they evolved, such as Facebook, LinkedIn, Twitter, and many others.
Data issues can happen with even less than a terabyte of data. It is not uncommon to
see teams of database administrators employed to manage the scalability and performance
issues of EDW systems, which are not even on a big data scale as we discussed earlier. The
big issue is not that everyone will suddenly operate at petabyte scale; a lot of companies
do not have that much data. The more important topics are the specifics of the storage and
processing infrastructure and what approaches best suit each problem. How much data do
you have, and what are you trying to do with it? Do you need to do offline batch processing
of huge amounts of data to compute statistics? Do you need all your data available online to
serve queries from a web application or a service API? What is your enterprise information
management strategy and how does it co-exist with the big data realm?
References
Snapshot of data activities in an internet minute:
Go-Globe.com
MAD Skills: New Analysis Practices for Big Data: VLDB '09, August 24-28, 2009,
Lyon, France
The next frontier of innovation, competition and productivity:
Mckinsey.com
Bringing Big Data to the Enterprise, IBM, 2012
A Comprehensive List of Big Data Statistics, Wikibon Blog, 1 August 2012
eBay Study: How to Build Trust and Improve the Shopping Experience, KnowIT
Information Systems, 8 May 2012
