This article is second in our series on Big Data. To know about Big Data, read our Primer on Big Data.
Due to the advent of new technologies, devices, and communication means like social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time until 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected. 90% of the world data was generated in the last few years.
Big data means really big data, it is a collection of large data sets that cannot be processed using traditional computing techniques. 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[1]:
- 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 feed, clickstreams 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 has 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, audit ability, 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 is 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.
