Big Data Technologies Go Back

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.

"Big Data technologies describe 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 velocity capture, discovery and/or analysis."

Big data is a dynamic that seemed to appear from almost nowhere. But in reality, Big Data is not new – and it is moving into mainstream and getting a lot more attention. the growth of Big Data is being enabled by inexpensive storage, a proliferation of sensor and data capture technology, increasing connections to information via the cloud and virtualised storage infrastructure, as well as innovative software and analysis tools. It is no surprise then that business analytics as a technology area is rising on the radars of CIOs and line-of-business (LOB) executive.

New Big DataSources : Industry Digitization

New data sources for Big Data include industries that just recently began to digitize their content.In virtually all of these cases, data growth rates in the past five years have been near infinite,since in most cases it started from zero.

industries include:

The media/entertainment industry moved to digital recording, production,and delivery in the past five years and is now collecting large amounts of rich content and user viewing behaviors.

The healthcare industry is quickly movingto electronic medical records and images,which it wants to use for short-term public health monitoring and long-term epidemiological research programs.

Life sciences:
Low-cost gene sequencing can generate tens of terabytes of information that must be analyzed to look for genetic variations and potential treatment effectiveness.

Video surveillance:
Video surveillance is still transitioning from CCTV to IPTV cameras and recording systems that organizations want to analyze for behavioral patterns (security and service enhancement).

Transportation, logistics, retail, utilities, and telecommunications:Sensor data is being generated at an accelerating rate from fleet GPS transceivers, RFID tag readers, smart meters,and cell phones (call data records[CDRs]); that data is used to optimize operations and drive operational business intelligence (BI) to realize immediate business opportunities.

The BI market is mature, many terms have been around for a long time and have either become obsolete or have been redefined over the years.

The term 'analytics' is relatively new and its meaning is often unclear — it refer to advanced analytics including predictive analytics, optimisation and forecasting, or analytic applications. In some submarkets, such as Web Analytics, the term 'analytics' simply means a dashboard on top of some data.

Business analytics is a combination of the above and also includes data warehousing technologies.

The CIO and the IT department need to leverage a broader set of business analytics capabilities to create a new information management strategy that deals with the emerging big Data dynamic as well as delivering improved decision-making capabilities to the business stakeholders across the organisation.

The business environment today is more dynamic than ever, with mergers and acquisitions, consolidation and regulatory changes. To succeed, an enterprise like yours needs to develop an ability to sense these changes, and thus respond to them quickly and smartly.

'To know the technology is one thing, but to apply it in the right environment is something entirely different'. The new technology needs to be tied back to business requirements as much as possible – not just examining the technology for the sake of it having said that, most It executives are not aware of the technologies and trends developing in this area – and where they are aware of it, Their strategy is to put a couple of people in their enterprise architecture team to experiment with the new technologies (i.e. in memory, hadoop, map reduce, Key Value Stores etc) that are being used to deal with the 'Big Data' phenomenon