Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with large body of information that we could not comprehend when used only in smaller amounts.
Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Thus a fourth concept, veracity, refers to the quality or insightfulness of the data. Without sufficient investment in expertise for big data veracity, then the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data.
Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big 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." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on". Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology, and environmental research.
The size and number of available data sets have grown rapidly as data is collected by devices such as mobile devices, cheap and numerous information-sensing Internet of things devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×260 bytes) of data are generated. Based on an IDC report prediction, the global data volume was predicted to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. According to IDC, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. While Statista report, the global big data market is forecasted to grow to $103 billion by 2027. In 2011 McKinsey & Company reported, if US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data. And users of services enabled by personal-location data could capture $600 billion in consumer surplus. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.
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 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."
Skegness (/ˌskɛɡˈnɛs/ skeg-NESS) is a seaside town and civil parish in Lincolnshire, England. On the Lincolnshire coast of the North Sea, the town is 43 miles (69 km) east of Lincoln and 22 miles (35 km) north-east of Boston. With a population of 19,579, it is the largest settlement in the East Lindsey district; it also incorporates Winthorpe and Seacroft, and forms a larger built-up area with the resorts of Ingoldmells and Chapel St Leonards to the north. The town is on the A52 and A158 roads, connecting it with Boston and the East Midlands, and Lincoln respectively. Skegness railway station is on the Nottingham to Skegness (via Grantham) line.
The original Skegness was situated farther east at the mouth of The Wash. Its Norse name refers to a headland which sat near the settlement. By the 14th century, it was a locally important port for coastal trade. The natural sea defences which protected the harbour eroded in the later Middle Ages, and it was lost to the sea after a storm in the 1520s. Rebuilt along the new shoreline, early modern Skegness was a small fishing and farming village, but from the late 18th century members of the local gentry visited for holidays. The arrival of the railways in 1873 transformed it into a popular seaside resort. This was the intention of the 9th Earl of Scarbrough, who owned most of the land in the vicinity; he built the infrastructure of the town and laid out plots, which he leased to speculative developers. This new Skegness quickly became a popular destination for holiday-makers and day trippers from the East Midlands factory towns. By the interwar years the town was established as one of the most popular seaside resorts in Britain. The layout of the modern seafront dates to this time and holiday camps were built around the town, including the first Butlin's holiday resort which opened in Ingoldmells in 1936.
The package holiday abroad became an increasingly popular and affordable option for many British holiday-makers during the 1970s; this trend combined with declining industrial employment in the East Midlands to harm Skegness's visitor economy in the late 20th century. Nevertheless, the resort retains a loyal visitor base and has increasingly attracted people visiting for a short holiday alongside their trip abroad; tourism increased following the recession of 2007–09 owing to the resort's affordability. In 2011, the town was England's fourth most popular destination for UK residents, and in 2015 it received over 1.4 million visitors. It has a reputation as a traditional English seaside resort owing to its long, sandy beach and seafront attractions which include amusement arcades, eateries, Botton's fairground, the pier, nightclubs and bars. Other visitor attractions include Natureland Seal Sanctuary, a museum, an aquarium, a heritage railway, an annual carnival, a yearly arts festival, and Gibraltar Point nature reserve to the south of the town.
Despite the arrival of several manufacturing firms since the 1950s and Skegness's prominence as a local commercial centre, the tourism industry remains very important for the economy and employment. Its low wages and seasonal nature, along with the town's aging population, have contributed towards high levels of deprivation among the resident population. Residents are served by five state primary schools and a preparatory school, two state secondary schools (one of which is selective), several colleges, a community hospital, several churches and two local newspapers. The town is home to the divisional police headquarters, a magistrates court and a lifeboat station.