Big data is a broad term
for data sets so large or complex that traditional data processing applications
are inadequate. Challenges include analysis, capture, data curation, search,
sharing, storage, transfer, visualization, and information privacy. The term
often refers simply to the use of predictive analytics or other certain
advanced methods to extract value from data, and seldom to a particular size of
data set. Accuracy in big data may lead to more confident decision making. And
better decisions can mean greater operational efficiency, cost reduction and
reduced risk.
What is big data analytics?
Big
data analytics is the process of examining big data to uncover hidden patterns,
unknown correlations and other useful information that can be used to make
better decisions. With big data analytics, data scientists and others can
analyze huge volumes of data that conventional analytics and business
intelligence solutions can't touch. Consider that your organization could
accumulate (if it hasn't already) billions of rows of data with hundreds of
millions of data combinations in multiple data stores and abundant formats.
High-performance analytics is necessary to process that much data in order to
figure out what's important and what isn't. Enter big data analytics.
Why
collect and store terabytes of data if you can't analyze it in full context? Or
if you have to wait hours or days to get results? With new advances in
computing technology, there's no need to avoid tackling even the most
challenging business problems. For simpler and faster processing of only
relevant data, you can use high-performance analytics. Using high-performance
data mining, predictive analytics, text mining, forecasting and optimization on
big data enables you to continuously drive innovation and make the best
possible decisions. In addition, organizations are discovering that the unique
properties of machine learning are ideally suited to addressing their
fast-paced big data needs in new ways.
Why is big data analytics
important?
For years SAS customers
have evolved their analytics methods from a reactive view into a proactive
approach using predictive and prescriptive analytics. Both reactive and
proactive approaches are used by organizations, but let's look closely at what
is best for your organization and task at hand.
Reactive vs. proactive approaches
There are four approaches
to analytics, and each falls within the reactive or proactive category:
Reactive –
business intelligence. In the reactive category, business intelligence (BI)
provides standard business reports, ad hoc reports, OLAP and even alerts and
notifications based on analytics. This ad hoc analysis looks at the static
past, which has its purpose in a limited number of situations.
Reactive –
big data BI. When reporting pulls from huge data sets, we can say this is
performing big data BI. But decisions based on these two methods are still
reactionary.
Proactive –
big analytics. Making forward-looking, proactive decisions requires proactive
big analytics like optimization, predictive modeling, text mining, forecasting
and statistical analysis. They allow you to identify trends, spot weaknesses or
determine conditions for making decisions about the future. But although it's
proactive, big analytics cannot be performed on big data because traditional
storage environments and processing times cannot keep up.
Proactive –
big data analytics. By using big data analytics you can extract only the
relevant information from terabytes, petabytes and exabytes, and analyze it to
transform your business decisions for the future. Becoming proactive with big
data analytics isn't a one-time endeavor; it is more of a culture change – a
new way of gaining ground by freeing your analysts and decision makers to meet
the future with sound knowledge and insight.
The Challenges of Big Data
Analytics:
For most organizations,
big data analysis is a challenge. Consider the sheer volume of data and the
different formats of the data (both structured and unstructured data) that is
collected across the entire organization and the many different ways different
types of data can be combined, contrasted and analyzed to find patterns and
other useful business information.
The first challenge is in
breaking down data silos to access all data an organization stores in different
places and often in different systems. A second big data challenge is in
creating platforms that can pull in unstructured data as easily as structured
data. This massive volume of data is typically so large that it's difficult to
process using traditional database and software methods.
Big Data Requires
High-Performance Analytics
To analyze such a large
volume of data, big data analytics is typically performed using specialized
software tools and applications for predictive analytics, data mining, text
mining, forecasting and data optimization. Collectively these processes are
separate but highly integrated functions of high-performance analytics. Using
big data tools and software enables an organization to process extremely large
volumes of data that a business has collected to determine which data is
relevant and can be analyzed to drive better business decisions in the future.
Examples of How Big Data
Analytics is Used Today
As the technology that
helps an organization to break down data silos and analyze data improves,
business can be transformed in all sorts of ways. According to Datamation,
today's advances in analyzing Big Data allow researchers to decode human DNA in
minutes, predict where terrorists plan to attack, determine which gene is
mostly likely to be responsible for certain diseases and, of course, which ads
you are most likely to respond to on Facebook.
The business cases for
leveraging Big Data are compelling. For instance, Netflix mined its subscriber
data to put the essential ingredients together for its recent hit House of
Cards, and subscriber data also prompted the company to bring Arrested
Development back from the dead.
Another example comes from
one of the biggest mobile carriers in the world. France's Orange launched its
Data for Development project by releasing subscriber data for customers in the
Ivory Coast. The 2.5 billion records, which were made anonymous, included
details on calls and text messages exchanged between 5 million users.
Researchers accessed the data and sent Orange proposals for how the data could
serve as the foundation for development projects to improve public health and
safety. Proposed projects included one that showed how to improve public safety
by tracking cell phone data to map where people went after emergencies; another
showed how to use cellular data for disease containment.
Benefits of Big Data
Analytics:
Enterprises are
increasingly looking to find actionable insights into their data. Many big data
projects originate from the need to answer specific business questions. With
the right big data analytics platforms in place, an enterprise can boost sales,
increase efficiency, and improve operations, customer service and risk
management.
Webopedia parent company,
QuinStreet, surveyed 540 enterprise decision-makers involved in big data
purchases to learn which business areas companies plan to use Big Data
analytics to improve operations. About half of all respondents said they were
applying big data analytics to improve customer retention, help with product
development and gain a competitive advantage.
Notably, the business area
getting the most attention relates to increasing efficiencies and optimizing
operations. Specifically, 62 percent of respondents said that they use big data
analytics to improve speed and reduce complexity.
