Technology looses appreciation, as it becomes obsolete. Well in reality this behavior is not just limited to technology and is a relatively common trend of the modern world! I still remember when I was a teen in the late 90’s, I got some money saved off my allowance and borrowed some from my mother to buy my first personal computer (PC). The end of the PC era is perhaps a topic for another discussion! Nevertheless, it cost me almost a fortune back then to get this great looking piece of equipment, with latest 1 GHz Pentium III processor, 1 GB RAM and a whooping 100 GB IDE hard drive. Well I am sure no one would jump off his or her seat looking at these specs today. However, I was really excited to brag about the “BIG” hard drive capacity my PC had back then and how it could save dozens of my all time favorite movies! In today’s world a tiny pocket smart phone or a handy tablet could hold double the capacity of my good old PC and that too in almost half the price I paid back then! So, if the definition of BIG is relative and what used to be BIG isn’t big anymore! Then what is BIG?
Well the answer to this tricky question is actually quiet simple. The new BIG is the big-data floating in this vast computing universe. Big-data is the term given to large volumes of variety of business, services and consumer data received at high velocities, variable intervals and complexities. Processing of such large quantities of data to perform predictive and trend analysis is more commonly termed as big-data analytics. As per Gartner’s ‘Hype Cycle for Emerging Technologies, 2014’ big-data analytics has started its decent into the trough of disillusionment, which evidently show the steady maturity of this technology in the past few years. Big-data analytic is the most fast growing technology of the modern world. Traditionally powered by search engines, social networks and the cloud it is slowing making its presence felt in every industry and business. It is just a matter of time when big-data, predictive analytics and machine learning algorithms will become integral part of our daily life. Unknowingly, big-data has already started influencing our day-to-day decision-making. How many times have you used your android phone suggestions around estimated time of arrival based on your preferred mode of transport and built-in realtime traffic analytics? How many times have you seen and used the Google Now cards to view your favorite game scores, share market data, nearby places to visit etc? Well it may sound unbelievable but all this is linked to the BIG-data!
These are just some of the more common applications of big-data analytics that we see in the smart phone industry. However, have you ever thought how big-data could solve the biggest problems of the world? Think about its predictive analytics applications in the field of medicine, epidemic & disease control, controlling terrorism, controlling economic uncertainties, efficient traffic, power, water management, predicting natural disasters, population, pollution and food-supply regulation etc. The applications of big-data are endless, yet this technology is relatively new and have its own set of adoption and implementation issues. As per some recent market research, rate of big-data adoption is found to be very intermittent across the different regions and industries.
Lack of knowledge and hype: Big-data analytics is still considered as the technology of the public clouds, Internet, social networks and search engines. On the contrary enterprises have always traditionally invested in complex and expensive decision support, business intelligence systems which in some sense have provide a rather basic and less efficient business / predictive analytics over years.
Perception of huge investment and low returns: Often a perception of huge investment is associated with introduction of a new and emerging technology. Open source big-data systems like Apache Hadoop, MongoDB etc. provide a very low total cost of ownership and could be run over inexpensive commodity hardware to provide a low risk solution for small and medium sized enterprises.
Size of the organization: It is usually seen that big (large) organizations have a better adoption rate than their smaller counterparts, however in reality there is no direct co-relation between the size of the organization and the adoption of big-data. It is essentially the understanding of its true business value that often cause slow adoption in small and budding organizations.
Lack of proper strategies and information silos: Most organizations have yet to identify a well-defined strategy to implement big-data analytics in their short, mid and or long term business plans. Most times it is the lack of communication, collaboration and innovation between the business functions that hinders these strategic decisions.
Data security and privacy issues: If we look at the industry specific issues and adoption rates, slowest adoption would perhaps be in the healthcare industry. Most countries have strict regulations around patient data security and privacy inhibiting the adoption of big-data analytics in healthcare, pharmaceuticals and biotechnology sectors.
Lack of consumer willingness to share data: Often consumer awareness and willingness to share non-confidential data slow the adoption of big-data in many industries like consumer goods, financial and professional services.
Lack of softwares and skills: The least significant of all these issues would be the lack of softwares and or in-house skills to support big-data analytics, as these issues could be easily resolved through cloud based softwares and services.
“Big-data analytics is without a doubt, the most valuable and innovative piece of technology of the past decade, however there are still a lot of issues preventing exposure of its true capabilities. Applications of big-data are endless and there is definitely a lot of expectations and optimism in the industry. Despite its slow adoption, there are industries and businesses, which evidently understand the value of, big-data in improving customer satisfaction, business processes, efficiency and agility. Lack of communication and collaboration between business functions, information silos, unwillingness to share data and executive sponsorship are some of the issues that must be resolved in order to truly adopt big-data analytics in the enterprise sector. Whatever be the issues, adoption of big-data is the future of business intelligence, trend predictions and ultimate consumer experience! And it is just a matter of time before we start to see mass implementation of this technology in almost every industry and business we know of!”
About the Author
Vishal Srivastava is a technology enthusiast based in Singapore and has over 12 years of experience in systems virtualization; cloud computing, systems architecture, software research and development. He works for a leading virtualization and cloud computing company as an engineering manager and has articulated, started and delivered many open-source and commercial projects around cloud computing and server virtualization domains. Vishal is a passionate blogger and owner of www.Cloud-Kin.com, his personal blog around cloud computing and its related technologies