Big data

Growth of and digitization of global information-storage capacity[1]

Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, finance, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]

Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]

Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[12]

Definition[edit]

Visualization of daily Wikipedia edits created by IBM. At multiple terabytes in size, the text and images of Wikipedia are an example of big data.

The term has been in use since the 1990s, with some giving credit to John Mashey for coining or at least making it popular.[13][14] Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.[15] Big Data philosophy encompasses unstructured, semi-structured and structured data, however the main focus is on unstructured data.[16] Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data.[17] Big data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale.[18]

In a 2001 research report[19] and related lectures, META Group (now Gartner) defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data.[20] In 2012, Gartner updated its definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." Gartner's definition of the 3Vs is still widely used, and in agreement with a consensual definition that states that "Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value".[21] Additionally, a new V "Veracity" is added by some organizations to describe it,[22] revisionism challenged by some industry authorities.[23] The 3Vs have been expanded to other complementary characteristics of big data:[24][25]

  • Volume: big data doesn't sample; it just observes and tracks what happens
  • Velocity: big data is often available in real-time
  • Variety: big data draws from text, images, audio, video; plus it completes missing pieces through data fusion
  • Machine learning: big data often doesn't ask why and simply detects patterns[26]
  • Digital footprint: big data is often a cost-free byproduct of digital interaction[25][27]

The growing maturity of the concept more starkly delineates the difference between big data and Business Intelligence:[28]

  • Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends, etc..
  • Big data uses inductive statistics and concepts from nonlinear system identification[29] to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density[30] to reveal relationships and dependencies, or to perform predictions of outcomes and behaviors.[29][31]

Characteristics[edit]

Big data can be described by the following characteristics:[24][25]

Volume
The quantity of generated and stored data. The size of the data determines the value and potential insight- and whether it can actually be considered big data or not.
Variety
The type and nature of the data. This helps people who analyze it to effectively use the resulting insight.
Velocity
In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
Variability
Inconsistency of the data set can hamper processes to handle and manage it.
Veracity
The quality of captured data can vary greatly, affecting accurate analysis.

Factory work and Cyber-physical systems may have a 6C system:

  • Connection (sensor and networks)
  • Cloud (computing and data on demand)[32][33]
  • Cyber (model and memory)
  • Content/context (meaning and correlation)
  • Community (sharing and collaboration)
  • Customization (personalization and value)

Data must be processed with advanced tools (analytics and algorithms) to reveal meaningful information. For example, to manage a factory one must consider both visible and invisible issues with various components. Information generation algorithms must detect and address invisible issues such as machine degradation, component wear, etc. on the factory floor.[34][35]

Architecture[edit]

In 2000, Seisint Inc. (now LexisNexis Group) developed a C++-based distributed file-sharing framework for data storage and query. The system stores and distributes structured, semi-structured, and unstructured data across multiple servers. Users can build queries in a C++ dialect called ECL. ECL uses an "apply schema on read" method to infer the structure of stored data when it is queried, instead of when it is stored. In 2004, LexisNexis acquired Seisint Inc.[36] and in 2008 acquired ChoicePoint, Inc.[37] and their high-speed parallel processing platform. The two platforms were merged into HPCC (or High-Performance Computing Cluster) Systems and in 2011, HPCC was open-sourced under the Apache v2.0 License. Quantcast File System was available about the same time.[38]

In 2004, Google published a paper on a process called MapReduce that uses a similar architecture. The MapReduce concept provides a parallel processing model, and an associated implementation was released to process huge amounts of data. With MapReduce, queries are split and distributed across parallel nodes and processed in parallel (the Map step). The results are then gathered and delivered (the Reduce step). The framework was very successful,[39] so others wanted to replicate the algorithm. Therefore, an implementation of the MapReduce framework was adopted by an Apache open-source project named Hadoop.[40]

MIKE2.0 is an open approach to information management that acknowledges the need for revisions due to big data implications identified in an article titled "Big Data Solution Offering".[41] The methodology addresses handling big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty in deleting (or modifying) individual records.[42]

2012 studies showed that a multiple-layer architecture is one option to address the issues that big data presents. A distributed parallel architecture distributes data across multiple servers; these parallel execution environments can dramatically improve data processing speeds. This type of architecture inserts data into a parallel DBMS, which implements the use of MapReduce and Hadoop frameworks. This type of framework looks to make the processing power transparent to the end user by using a front-end application server.[43]

Big data analytics for manufacturing applications is marketed as a 5C architecture (connection, conversion, cyber, cognition, and configuration).[44]

The data lake allows an organization to shift its focus from centralized control to a shared model to respond to the changing dynamics of information management. This enables quick segregation of data into the data lake, thereby reducing the overhead time.[45][46]

Technologies[edit]

A 2011 McKinsey Global Institute report characterizes the main components and ecosystem of big data as follows:[47]

Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation,[48] such as multilinear subspace learning.[49] Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining,[50] distributed file systems, distributed databases, cloud-based infrastructure (applications, storage and computing resources) and the Internet.[citation needed]

Some but not all MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS.[51]

DARPA's Topological Data Analysis program seeks the fundamental structure of massive data sets and in 2008 the technology went public with the launch of a company called Ayasdi.[52]

The practitioners of big data analytics processes are generally hostile to slower shared storage,[53] preferring direct-attached storage (DAS) in its various forms from solid state drive (Ssd) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures--Storage area network (SAN) and Network-attached storage (NAS) —is that they are relatively slow, complex, and expensive. These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost.

Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible. Data in memory is good—data on spinning disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques.

There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners as of 2011 did not favour it.[54]

Applications[edit]

Bus wrapped with SAP Big data parked outside IDF13.

Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell have 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.[3]

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.[3] Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn lead 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[8] and predictions put the amount of internet traffic at 667 exabytes annually by 2014.[3] According to one estimate, one third of the globally stored information is in the form of alphanumeric text and still image data,[55] 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.[56]

Government[edit]

The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation,[57] 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. Below are some examples of initiatives the governmental big data space.

United States of America[edit]

India[edit]

United Kingdom[edit]

Examples of uses of big data in public services:

  • Data on prescription drugs: by connecting origin, location and the time of each prescription, a research unit was able to exemplify the considerable delay between the release of any given drug, and a UK-wide adaptation of the National Institute for Health and Care Excellence guidelines. This suggests that new or most up-to-date drugs take some time to filter through to the general patient.[66]
  • Joining up data: a local authority blended data about services, such as road gritting rotas, with services for people at risk, such as 'meals on wheels'. The connection of data allowed the local authority to avoid any weather-related delay.[67]

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.[68][69] 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 disaster and resource management.[70][71][72] Additionally, user-generated data offers new opportunities to give the unheard a voice.[73] 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.[70]

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.[74] 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 Prognostics and Health Management (PHM).[75][76]

Cyber-physical models[edit]

Current PHM implementations mostly use data during the actual usage while analytical algorithms can perform more accurately when more information throughout the machine's lifecycle, such as system configuration, physical knowledge and working principles, are included. There is a need to systematically integrate, manage and analyze machinery or process data during different stages of machine life cycle to handle data/information more efficiently and further achieve better transparency of machine health condition for manufacturing industry.

With such motivation a cyber-physical (coupled) model scheme has been developed. The coupled model is a digital twin of the real machine that operates in the cloud platform and simulates the health condition with an integrated knowledge from both data driven analytical algorithms as well as other available physical knowledge. It can also be described as a 5S systematic approach consisting of sensing, storage, synchronization, synthesis and service. The coupled model first constructs a digital image from the early design stage. System information and physical knowledge are logged during product design, based on which a simulation model is built as a reference for future analysis. Initial parameters may be statistically generalized and they can be tuned using data from testing or the manufacturing process using parameter estimation. After that step, the simulation model can be considered a mirrored image of the real machine—able to continuously record and track machine condition during the later utilization stage. Finally, with the increased connectivity offered by cloud computing technology, the coupled model also provides better accessibility of machine condition for factory managers in cases where physical access to actual equipment or machine data is limited.[35]

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.[77] 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.[78] "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.[79] While extensive information in healthcare is now electronic, it fits under the big data umbrella as most is unstructured and difficult to use.[80]

Education[edit]

A McKinsey Global Institute study found a shortage of 1.5 million highly trained data professionals and managers[47] and a number of universities[81] 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.[82]

Media[edit]

To understand how the media utilises big data, it is first necessary to provide some context into the mechanism used for media process. It has been suggested by Nick Couldry and Joseph Turow that practitioners in Media and Advertising approach big data as many actionable points of information about millions of individuals. The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead taps into consumers with technologies that reach targeted people at optimal times in optimal locations. The ultimate aim is to serve, or convey, a message or content that is (statistically speaking) in line with the consumer's mindset. For example, publishing environments are increasingly tailoring messages (advertisements) and content (articles) to appeal to consumers that have been exclusively gleaned through various data-mining activities.[83]

  • Targeting of consumers (for advertising by marketers)
  • Data-capture
  • Data journalism: publishers and journalists use big data tools to provide unique and innovative insights and infographics.

Channel 4, the British public-service television broadcaster, is a leader in the field of big data and data analysis.[84]

Internet of Things (IoT)[edit]

Main article: Internet of Things

Big data and the IoT work in conjunction. Data extracted from IoT devices provides a mapping of device inter-connectivity. Such mappings have been used by the media industry, companies and governments to more accurately target their audience and increase media efficiency. IoT is also increasingly adopted as a means of gathering sensory data, and this sensory data has been used in medical [85] and manufacturing [86] contexts.

Technology[edit]

  • eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising.[87]
  • Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers. The core technology that keeps Amazon running is Linux-based and as of 2005 they had the world's three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.[88]
  • Facebook handles 50 billion photos from its user base.[89]
  • Google was handling roughly 100 billion searches per month as of August 2012.[90]
  • Oracle NoSQL Database has been tested to past the 1M ops/sec mark with 8 shards and proceeded to hit 1.2M ops/sec with 10 shards.[91]

Information Technology[edit]

Especially since 2015, big data has come to prominence within Business Operations as a tool to help employees work more efficiently and streamline the collection and distribution of Information Technology (IT). The use of big data to resolve IT and data collection issues within an enterprise is called IT Operations Analytics (ITOA).[92] By applying big data principles into the concepts of machine intelligence and deep computing, IT departments can predict potential issues and move to provide solutions before the problems even happen.[92] In this time, ITOA businesses were also beginning to play a major role in systems management by offering platforms that brought individual data silos together and generated insights from the whole of the system rather than from isolated pockets of data.

Retail[edit]

  • Walmart handles more than 1 million customer transactions every hour, which are imported into databases estimated to contain more than 2.5 petabytes (2560 terabytes) of data—the equivalent of 167 times the information contained in all the books in the US Library of Congress.[3]

Retail banking[edit]

  • FICO Card Detection System protects accounts worldwide.[93]
  • The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates.[94][95]

Real estate[edit]

  • Windermere Real Estate uses anonymous GPS signals from nearly 100 million drivers to help new home buyers determine their typical drive times to and from work throughout various times of the day.[96]

Science[edit]

The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.99995%[97] of these streams, there are 100 collisions of interest per second.[98][99][100]

  • As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication.
  • If all sensor data were recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×1020) bytes per day, almost 200 times more than all the other sources combined in the world.

The Square Kilometre Array is a radio telescope built of thousands of antennas. It is expected to be operational by 2024. Collectively, these antennas are expected to gather 14 exabytes and store one petabyte per day.[101][102] It is considered one of the most ambitious scientific projects ever undertaken.[103]