What is Big Data and why it matters?

Big Data

What it is and why it matters ?

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Big data is a term for data sets that are so large or complex that traditional data processing application softwareis inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

Lately, 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 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, fintech, 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 Internet of things devices such as 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]

Characteristics[edit]

Big data can be described by the following characteristics:^[25][26]

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 data quality of captured data can vary greatly, affecting the accurate analysis.^[33]

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

• Connection (sensor and networks)
• Cloud (computing and data on demand)^[34][35]
• 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.^[36][37]