Big data is data that is too large to process using traditional methods.
In its May 2011 report on big data, the McKinsey Global
Institute projected a 40 percent annual growth in global data—a doubling of
data volumes every two years. Much of this data is complex, unstructured data that
doesn’t submit readily to analysis. In fact, big data refers to the volumes of
mostly complex, unstructured data whose analysis could yield significant
dividends in terms of competitive business advantage, but cannot be analyzed by
the existing structured data oriented infrastructure & framework.
Therefore it lies the challenge of big data, at Three WHYs for Big Data, we explored
why Big Data is Big Matter, we also further articulate Three Big WHATs to identify Big Data Challenges, and now
continue the series on how to overcome the barriers to tame the Big Data?
While experts agree that Big Data is big, exactly how big is
a matter of debate. IDC forecasts a roughly 50 percent annual growth rate for
what it calls the world’s “digital universe,” more than 70 percent of which IDC
estimates is generated by consumers and over 20 percent by enterprises. Between
2009 and 2020, the digital universe will swell by a factor of 44 to 35
zettabytes, or 35 million petabytes, IDC predicts
Big data is data that is too large to process using
traditional methods. It originated with Web search companies who had the
problem of querying very large distributed aggregations of loosely-structured
data
If you’re creating large data sets, which most of businesses
do today, you have no choice but to
embrace big data management. First Steps to Managing Big Data can be
overwhelming and full of confusion, especially if the organization has a lot of
existing data and data continue to grow and flow exponentially.
A) Start with
business Strategy/goal: As we put in the Three Big Whys for Big Data, Big
Data is the means to end, not the end. Start with end: the purpose, the
business goals, the strategy;
B) And then develop a
Big Data Strategy/map in which you classify:
The types of data that are important to the organization.
Various aspects of your data, such as whether it changes a
lot (complexity & variety) and how
fast it’s growing (velocity).
Is much of your data critical information
Prioritize the business problems Big Data can help solve
C) The right Tools and Architecture: using
traditional data warehouse solution to search through such vast amounts of
often unstructured data can takes weeks, if not months. Hadoop, an open source ecosystem of data management
tools and technologies that can help organizations tackle the broad problem of
big data analytics, here is another nice article to recommend 7 top tools for taming Big Data.
D) Data Security & Governance:
However, IT and business managers should keep cautious when it comes to
implement Hadoop, which requires a fair amount of practices and serious
attention & solutions to the security of massive amounts of data/
information stored in distributed clusters and potentially in public clouds
Extracting business value and wisdom from big data remains
elusive for many organizations. For most companies today, data are abundant and
readily available, but not well used. Nearly one in four survey respondents
says the vast majority of its company’s data are untapped. Another 53% say they
only use about half of their valuable data. Yet 73% say that data collection in
their organization has increased over the last year
Big-data analytics must be business led with the right
mindset, time to value, and with effective data mining tools, as not all projects
will be successful at finding the needle in the haystack. The organizations
that can do it successfully will out beat the competitors and become the
intelligent business in 21st century.
Twitter: @Pearl_Zhu
5 comments:
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Global Institute Of Technology & Management
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