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Few things have been as talked about as Big Data. This is interesting, given that no one really knows what it is. Humans are funny people. We file law suits over a typo yet bet the bank on something we can’t even explain. Big Data is one such example, and here are some definitions of it.
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. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.Complexity. Today’s data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.
…data sets, typically consisting of billions or trillions of records, that are so vast and complex that they require new and powerful computational resources to process…
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.
Big data describes a holistic information management strategy that includes and integrates many new types of data and data management alongside traditional data. Big data has also been defined by the four Vs:Volume. The amount of data. While volume indicates more data, it is the granular nature of the data that is unique. Big data requires processing high volumes of low-density, unstructured Hadoop data—that is, data of unknown value, such as Twitter data feeds, click streams on a web page and a mobile app, network traffic, sensor-enabled equipment capturing data at the speed of light, and many more. It is the task of big data to convert such Hadoop data into valuable information. For some organizations, this might be tens of terabytes, for others it may be hundreds of petabytes.Velocity. The fast rate at which data is received and perhaps acted upon. The highest velocity data normally streams directly into memory versus being written to disk. Some Internet of Things (IoT) applications have health and safety ramifications that require real-time evaluation and action. Other internet-enabled smart products operate in real time or near real time. For example, consumer eCommerce applications seek to combine mobile device location and personal preferences to make time-sensitive marketing offers. Operationally, mobile application experiences have large user populations, increased network traffic, and the expectation for immediate response.Variety. New unstructured data types. Unstructured and semi-structured data types, such as text, audio, and video require additional processing to both derive meaning and the supporting metadata. Once understood, unstructured data has many of the same requirements as structured data, such as summarization, lineage, auditability, and privacy. Further complexity arises when data from a known source changes without notice. Frequent or real-time schema changes are an enormous burden for both transaction and analytical environments.Value. Data has intrinsic value—but it must be discovered. There are a range of quantitative and investigative techniques to derive value from data—from discovering a consumer preference or sentiment, to making a relevant offer by location, or for identifying a piece of equipment that is about to fail. The technological breakthrough is that the cost of data storage and compute has exponentially decreased, thus providing an abundance of data from which statistical analysis on the entire data set versus previously only sample. The technological breakthrough makes much more accurate and precise decisions possible. However, finding value also requires new discovery processes involving clever and insightful analysts, business users, and executives. The real big data challenge is a human one, which is learning to ask the right questions, recognizing patterns, making informed assumptions, and predicting behavior.
Not to be outdone, Berkeley School of Information lists 43 definitions from 43 experts, some of which allude to its effects on business, our thinking and our way of life.
Companies such as Amazon and LinkedIn could not exist were it not for their ability to collect, store and use masses of data. Neuroscientists sometimes work with gigantic data files and the same is true of those conducting genomic research or building climate models. Telematics is another example. By now, most of you will have heard of No Such Agency. It’s possible they’ve heard of you, too.
“Big” is relative and Big Data is big in the eyes of those working with it. It may or may not be unstructured or streaming. Something I do know from my 30 years as a statistician is that more does not necessarily mean better and that diminishing or even negative analytic returns can make their presence felt very quickly. Big Data can be like mining a landfill, in the words of a contact of mine who knows this turf very well. Very few of us are Amazons or LinkedIns and, with apologies to Field of Dreams, built it and they may come.
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