Big Data defined:
Big Data Analytics:
Big Data
analytics is the process of examining large amounts of data of a variety of
types to uncover hidden patterns, unknown correlations and other useful
information. Such information can provide competitive advantages over rival
organizations and result in business benefits, such as more effective marketing
and increased revenue.
The primary goal of big data analytics is to
help companies make better business decisions by enabling data
scientists and other users to analyze huge volumes of transaction data as
well as other data sources that may be left untapped by conventional business
intelligence programs. These other data sources may include Web server logs and
Internet clickstream data, social media activity reports, mobile-phone call
detail records and information captured by sensors.
Big Data Management:
Big data
management is the organization, administration and governance of large volumes
of both structured and unstructured datakpsko. The goal of big data
management is to ensure a high level of data quality and accessibility for
business intelligence and big data analytics applications. Corporations,
government agencies and other organizations employ big data management
strategies to help them contend with fast-growing pools of data, typically
involving many terabytes or even petabytes of information saved in a variety of
file formats. Effective big data management helps companies locate valuable
information in large sets of unstructured data and semi-structured datakpsko
from a variety of sources, including call detail records, system logs and social
media sites. Tools used for Big Data Management are: Hadoop, MapReduce, NoSQL,
Cassandra and Hive.
Important
business questions will be:
- Why are our customers leaving
us?
- What is the value of a 'tweet'
or a 'like'?
- What products are our customers
most likely to buy?
- What is the best way to
communicate with our customers?
- Are our investments in customer
service paying off?
- What is the optimal price for my
product right now?
Big Data as a Service (BDaaS):
Big data
as a service (BDaaS) is the delivery of statistical analysis tools or
information by an outside provider that helps organizations understand and use
insights gained from large information sets in order to gain a competitive
advantage. Given the immense amount of unstructured data generated on a regular
basis, big data as a service is intended to free up organizational resources by
taking advantage of the predictive analytics skills of an outside provider to
manage and assess large data sets, rather than hiring in-house staff for those
functions.
Big data as a service can take the form of
software that assists with data processing or a contract for the services of a
team of data scientists. BDaaS is a form of managed services, similar to
Software as a Service or Infrastructure as a Service. Big data as a service
often relies upon cloud storage to preserve continual data access for the
organization that owns the information as well as the provider working with it.
The Challenges:
Many
organizations are concerned that the amount of amassed data is becoming so
large that it is difficult to find the most valuable pieces of information.
- What if your data volume gets so
large and varied you don't know how to deal with it?
- Do you store all your data?
- Do you analyze it all?
- How can you find out which data
points are really important?
- How can you use it to your best
advantage?
Until
recently, organizations have been limited to using subsets of their data, or
they were constrained to simplistic analyses because the sheer volumes of data
overwhelmed their processing platforms. But, what is the point of collecting
and storing terabytes of data if you can't analyze it in full context, or if
you have to wait hours or days to get results? On the other hand, not all business
questions are better answered by bigger data. You now have two choices:
Incorporate massive data volumes in analysis or determine upfront which data is
relevant.
Final words:
Big data
transforms the data management landscape by changing fundamental notions of
data governance and IT delivery. Though big data is still at its early stage,
the advantages of big data will feed the development of new capabilities in
sensing, understanding, and playing an active role in the world for the next 20
years and will change all walks of life. However, the underlying analytics and
interpretations of results will still require human cognition to connect the
dots and see the big picture.
An
organization needs a strategic plan to adopt the big data technologies. The
ability to collect and analyze massive amounts of data will be a key
competitive advantage across all industries, including government. Such
analytics projects can be complicated, idiosyncratic, and disruptive—thus they
require a strategic plan to be successful.
It takes
time to change the culture of depending only on traditional data analytics.
There will be occasions of unethical, abuse or misuse of big data applications
as big-data analytics and technologies are implemented. Therefore, it is better
to be cautious and start small and simple.
It takes the whole society to implement big datakpsko
technologies. Since big data will affect all of us in our life and
collaboration and partnership are essential to make big data successful, we are
all responsible to work together in dealing with the issues raised along with
application of the technologies on this journey for the next decade or two.
"Now you can run hundreds and thousands of
models at the product level - at the SKU level - because you have the big data
and analytics to support those models at that level."
References :
[1]
http://www.thoughtworks.com/big-data-analytics
[2]
http://www.sas.com/en_us/insights/big-data/what-is-big-data.html
[3]http://searchbusinessanalytics.techtarget.com/essentialguide/Structuring-a-big-data-strategy
[4]http://www.meritalk.com/
[5]http://searchcio.techtarget.com/
[6]
http://en.wikipedia.org/wiki/Big_data
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