Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dellhave spent more than $15 billion on software firms specializing in data management and analytics. In 2010, this industry was worth more than $100 billion and was growing at almost 10 percent a year: about twice as fast as the software business as a whole.[7]
Developed economies increasingly use data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide, and between 1 billion and 2 billion people accessing the internet.[7] Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn led to information growth. The world’s effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007[12] and predictions put the amount of internet traffic at 667 exabytes annually by 2014.[7]According to one estimate, one-third of the globally stored information is in the form of alphanumeric text and still image data,[67] which is the format most useful for most big data applications. This also shows the potential of yet unused data (i.e. in the form of video and audio content).
While many vendors offer off-the-shelf solutions for big data, experts recommend the development of in-house solutions custom-tailored to solve the company’s problem at hand if the company has sufficient technical capabilities.[68]
Government[edit]
The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation,[69] but does not come without its flaws. Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to deliver the desired outcome.
CRVS (Civil Registration and Vital Statistics) collects all certificates status from birth to death. CRVS is a source of big data for governments.
International development[edit]
Research on the effective usage of information and communication technologies for development (also known as ICT4D) suggests that big data technology can make important contributions but also present unique challenges to International development.[70][71] Advancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, and natural disasterand resource management.[72][73][74] Additionally, user-generated data offers new opportunities to give the unheard a voice.[75] However, longstanding challenges for developing regions such as inadequate technological infrastructure and economic and human resource scarcity exacerbate existing concerns with big data such as privacy, imperfect methodology, and interoperability issues.[72]
Manufacturing[edit]
Based on TCS 2013 Global Trend Study, improvements in supply planning and product quality provide the greatest benefit of big data for manufacturing. Big data provides an infrastructure for transparency in manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information.[76] A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data. Vast amount of sensory data in addition to historical data construct the big data in manufacturing. The generated big data acts as the input into predictive tools and preventive strategies such as Prognosticsand Health Management (PHM).[77][78]
Healthcare[edit]
Big data analytics has helped healthcare improve by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.[79][80][81][82] Some areas of improvement are more aspirational than actually implemented. The level of data generated within healthcare systems is not trivial. With the added adoption of mHealth, eHealth and wearable technologies the volume of data will continue to increase. This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data. There is now an even greater need for such environments to pay greater attention to data and information quality.[83] “Big data very often means ‘dirty data‘ and the fraction of data inaccuracies increases with data volume growth.” Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed.[84] While extensive information in healthcare is now electronic, it fits under the big data umbrella as most is unstructured and difficult to use.[85] The use of big data in healthcare has raised significant ethical challenges ranging from risks for individual rights, privacy and autonomy, to transparency and trust.[86]
Education[edit]
A McKinsey Global Institute study found a shortage of 1.5 million highly trained data professionals and managers[57] and a number of universities[87][better source needed] including University of Tennessee and UC Berkeley, have created masters programs to meet this demand. Private bootcamps have also developed programs to meet that demand, including free programs like The Data Incubator or paid programs like General Assembly.[88] In the specific field of marketing, one of the problems stressed by Wedel and Kannan [89] is that marketing has several subdomains (e.g., advertising, promotions, product development, branding) that all use different types of data. Because one-size-fits-all analytical solutions are not desirable, business schools should prepare marketing managers to have wide knowledge on all the different techniques used in these subdomains to get a big picture and work effectively with analysts
Big Data and Its Applications 