Big data

 ( Data Management ) 

Relational database management systems and desktop statistical software packages used to visualize data often have difficulty processing and analyzing big data. The processing and analysis of big data may require "massively parallel software running on tens, hundreds, or even thousands of servers". What qualifies as "big data" varies depending on the capabilities of those analyzing it and their tools. Furthermore, expanding capabilities make big data a moving target. "For some organizations, facing hundreds of 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."

"Volume", "variety", "velocity", and various other "Vs" are added by some organizations to describe it, a revision challenged by some industry authorities. The Vs of big data were often referred to as the "three Vs", "four Vs", and "five Vs". They represented the qualities of big data in volume, variety, velocity, veracity, and value. Variability is often included as an additional quality of big data.

A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd's relational model."

There are three significant factors in the use of big data in marketing:

1. Big data provides customer behavior pattern spotting for marketers, since all human actions are being quantified into readable numbers for marketers to analyze and use for their research.De Luca, Luigi M.; Herhausen, Dennis; Troilo, Gabriele; Rossi, Andrea (2021-07-01). "How and when do big data investments pay off? The role of marketing affordances and service innovation". Journal of the Academy of Marketing Science. 49 (4): 790–810.In addition, big data can also be seen as a customized product recommendation tool. Specifically, since big data is effective in analyzing customers’ purchase behaviors and browsing patterns, this technology can assist companies in promoting specific personalized products to specific customers.
2. Real-time market responsiveness is important for marketers because of the ability to shift marketing efforts and correct to current trends, which is helpful in maintaining relevance to consumers. This can supply corporations with the information necessary to predict the wants and needs of consumers in advance.De Luca, Luigi M.; Herhausen, Dennis; Troilo, Gabriele; Rossi, Andrea (2021-07-01). "How and when do big data investments pay off? The role of marketing affordances and service innovation". Journal of the Academy of Marketing Science. 49 (4): 790–810.
3. Data-driven market ambidexterity are being highly fueled by big data.De Luca, Luigi M.; Herhausen, Dennis; Troilo, Gabriele; Rossi, Andrea (2021-07-01). "How and when do big data investments pay off? The role of marketing affordances and service innovation". Journal of the Academy of Marketing Science. 49 (4): 790–810. New models and algorithms are being developed to make significant predictions about certain economic and social situations.Grybauskas, Andrius; Pilinkienė, Vaida; Stundžienė, Alina (2021-08-03). "Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic". Journal of Big Data. 8(1): 105. . . PMC 8329615. PMID 34367876.

• The Integrated Joint Operations Platform (IJOP, 一体化联合作战平台) is used by the government to monitor the population, particularly Uyghurs. Biometrics, including DNA samples, are gathered through a program of free physicals.
• By 2020, China plans to give all its citizens a personal "social credit" score based on how they behave. The Social Credit System, now being piloted in a number of Chinese cities, is considered a form of mass surveillance which uses big data analysis technology.

• Big data analysis was tried out for the BJP to win the 2014 Indian General Election.
• The Indian government uses numerous techniques to ascertain how the Indian electorate is responding to government action, as well as ideas for policy augmentation.

• Personalized diabetic treatments can be created through GlucoMe's big data solution.

• Data on prescription drugs: by connecting origin, location and the time of each prescription, a research unit was able to exemplify and examine 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.
• Joining up data: a local authority Data blending 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.

• In 2012, the Obama administration announced the Big Data Research and Development Initiative, to explore how big data could be used to address important problems faced by the government. The initiative is composed of 84 different big data programs spread across six departments.
• Big data analysis played a large role in Barack Obama's successful 2012 re-election campaign.
• The United States Federal Government owns four of the ten most powerful in the world.
• The Utah Data Center has been constructed by the United States National Security Agency. When finished, the facility will be able to handle a large amount of information collected by the NSA over the Internet. The exact amount of storage space is unknown, but more recent sources claim it will be on the order of a few . This has posed security concerns regarding the anonymity of the data collected.

• 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.
• Windermere Real Estate uses location information 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.
• FICO Card Detection System protects accounts worldwide.
• Omnichannel retailing leverages online big data to improve offline experiences.

• 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% of these streams, there are 1,000 collisions of interest per second.
• 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 (). 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 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. It is considered one of the most ambitious scientific projects ever undertaken.
• When the Sloan Digital Sky Survey (SDSS) began to collect astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy previously. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2020, its designers expect it to acquire that amount of data every five days.
• Decoding the human genome originally took 10 years to process; now it can be achieved in less than a day. The DNA sequencers have divided the sequencing cost by 10,000 in the last ten years, which is 100 times less expensive than the reduction in cost predicted by Moore's law.
• The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster.
• Google's DNAStack compiles and organizes DNA samples of genetic data from around the world to identify diseases and other medical defects. These fast and exact calculations eliminate any "friction points", or human errors that could be made by one of the numerous science and biology experts working with the DNA. DNAStack, a part of Google Genomics, allows scientists to use the vast sample of resources from Google's search server to scale social experiments that would usually take years, instantly.
• 23andme's DNA database contains the genetic information of over 1,000,000 people worldwide. The company explores selling the "anonymous aggregated genetic data" to other researchers and pharmaceutical companies for research purposes if patients give their consent. Ahmad Hariri, professor of psychology and neuroscience at Duke University who has been using 23andMe in his research since 2009 states that the most important aspect of the company's new service is that it makes genetic research accessible and relatively cheap for scientists. A study that identified 15 genome sites linked to depression in 23andMe's database lead to a surge in demands to access the repository with 23andMe fielding nearly 20 requests to access the depression data in the two weeks after publication of the paper.
• Computational fluid dynamics (CFD) and hydrodynamic turbulence research generate massive data sets. The Johns Hopkins Turbulence Databases ( JHTDB) contains over 350 terabytes of spatiotemporal fields from Direct Numerical simulations of various turbulent flows. Such data have been difficult to share using traditional methods such as downloading flat simulation output files. The data within JHTDB can be accessed using "virtual sensors" with various access modes ranging from direct web-browser queries, access through Matlab, Python, Fortran and C programs executing on clients' platforms, to cut out services to download raw data. The data have been used in over 150 scientific publications.

In Formula One races, race cars with hundreds of sensors generate terabytes of data. These sensors collect data points from tire pressure to fuel burn efficiency. Based on the data, engineers and data analysts decide whether adjustments should be made in order to win a race. Besides, using big data, race teams try to predict the time they will finish the race beforehand, based on simulations using data collected over the season.

• , eBay.com uses two at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising.
• 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 they had the world's three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.
• Facebook handles 50 billion photos from its user base. , Facebook reached 2 billion monthly active users.
• Google was handling roughly 100 billion searches per month .

Governments used big data to track infected people to minimise spread. Early adopters included China, Taiwan, South Korea, and Israel.

In March 2012, The White House announced a national "Big Data Initiative" that consisted of six federal departments and agencies committing more than $200 million to big data research projects.

The initiative included a National Science Foundation "Expeditions in Computing" grant of $10 million over five years to the AMPLab at the University of California, Berkeley. The AMPLab also received funds from DARPA, and over a dozen industrial sponsors and uses big data to attack a wide range of problems from predicting traffic congestion to fighting cancer.

The White House Big Data Initiative also included a commitment by the Department of Energy to provide $25 million in funding over five years to establish the Scalable Data Management, Analysis and Visualization (SDAV) Institute, led by the Energy Department's Lawrence Berkeley National Laboratory. The SDAV Institute aims to bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the department's supercomputers.

The U.S. state of Massachusetts announced the Massachusetts Big Data Initiative in May 2012, which provides funding from the state government and private companies to a variety of research institutions. The Massachusetts Institute of Technology hosts the Intel Science and Technology Center for Big Data in the MIT Computer Science and Artificial Intelligence Laboratory, combining government, corporate, and institutional funding and research efforts.

The European Commission is funding the two-year-long Big Data Public Private Forum through their Seventh Framework Program to engage companies, academics and other stakeholders in discussing big data issues. The project aims to define a strategy in terms of research and innovation to guide supporting actions from the European Commission in the successful implementation of the big data economy. Outcomes of this project will be used as input for Horizon 2020, their next framework program.

The British government announced in March 2014 the founding of the Alan Turing Institute, named after the computer pioneer and code-breaker, which will focus on new ways to collect and analyze large data sets.

At the University of Waterloo Stratford Campus Canadian Open Data Experience (CODE) Inspiration Day, participants demonstrated how using data visualization can increase the understanding and appeal of big data sets and communicate their story to the world.

Computational social sciences – Anyone can use application programming interfaces (APIs) provided by big data holders, such as Google and Twitter, to do research in the social and behavioral sciences. Often these APIs are provided for free. Tobias Preis et al. used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic products (GDPs) are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviors and real-world economic indicators. The authors of the study examined Google queries logs made by ratio of the volume of searches for the coming year (2011) to the volume of searches for the previous year (2009), which they call the "future orientation index". They compared the future orientation index to the per capita GDP of each country, and found a strong tendency for countries where Google users inquire more about the future to have a higher GDP.

Tobias Preis and his colleagues Helen Susannah Moat and H. Eugene Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends. Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports, suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets.

Big data sets come with algorithmic challenges that previously did not exist. Hence, there is seen by some to be a need to fundamentally change the processing ways.E. Sejdić (March 2014). "Adapt current tools for use with big data". Nature. 507 (7492): 306.

Much in the same line, it has been pointed out that the decisions based on the analysis of big data are inevitably "informed by the world as it was in the past, or, at best, as it currently is". Fed by a large number of data on past experiences, algorithms can predict future development if the future is similar to the past. Big Data requires Big Visions for Big Change. , Hilbert, M. (2014). London: TEDx UCL, x=independently organized TED talks If the system's dynamics of the future change (if it is not a stationary process), the past can say little about the future. In order to make predictions in changing environments, it would be necessary to have a thorough understanding of the systems dynamic, which requires theory. As a response to this critique Alemany Oliver and Vayre suggest to use "abductive reasoning as a first step in the research process in order to bring context to consumers' digital traces and make new theories emerge". Additionally, it has been suggested to combine big data approaches with computer simulations, such as agent-based models and complex systems. Agent-based models are increasingly getting better in predicting the outcome of social complexities of even unknown future scenarios through computer simulations that are based on a collection of mutually interdependent algorithms.Epstein, J. M., & Axtell, R. L. (1996). Growing Artificial Societies: Social Science from the Bottom Up. A Bradford Book. Finally, the use of multivariate methods that probe for the latent structure of the data, such as factor analysis and cluster analysis, have proven useful as analytic approaches that go well beyond the bi-variate approaches (e.g. contingency tables) typically employed with smaller data sets.

In health and biology, conventional scientific approaches are based on experimentation. For these approaches, the limiting factor is the relevant data that can confirm or refute the initial hypothesis. A new postulate is accepted now in biosciences: the information provided by the data in huge volumes (omics) without prior hypothesis is complementary and sometimes necessary to conventional approaches based on experimentation. In the massive approaches it is the formulation of a relevant hypothesis to explain the data that is the limiting factor. The search logic is reversed and the limits of induction ("Glory of Science and Philosophy scandal", C. D. Broad, 1926) are to be considered.

Consumer privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information; expert panels have released various policy recommendations to conform practice to expectations of privacy. The misuse of big data in several cases by media, companies, and even the government has allowed for abolition of trust in almost every fundamental institution holding up society.Bond-Graham, Darwin (2018). "The Perspective on Big Data" . The Perspective.

Barocas and Nissenbaum argue that one way of protecting individual users is by being informed about the types of information being collected, with whom it is shared, under what constraints and for what purposes.