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ELearning: What is Big Data and IoT?

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Voice Over • Elearning
15

Description

British BBC English, eLearning Module, Technology, IoT, Big Data and Databases

Vocal Characteristics

Language

English (British)

Voice Age

Middle Aged (35-54)

Accents

British

Transcript

Note: Transcripts are generated using speech recognition software and may contain errors.
as we share more of our lives via social media and organisations generate more information daily, a data overload begins to develop. According to a quote from IBM has mentioned in a British broadcasting company. BBC article in March 2014 over 2.5 exabytes That's 2.5 billion gigabytes of data was generated every day in 2012. About 75% of data is unstructured, coming from sources such as text, voice and video. Big data is a term for any large collection of data sets. These data sets are so enormous and complex that they become difficult to process using traditional data processing applications. The databases are de normalised, meaning there are instances of redundancies in the data, sets industry analyst Doug Laney in 2000 and one conceptualise the now conventional definition of big data. With the inception of the three V s volume, velocity and variety volume, numerous factors contribute to the increase in data volume. These factors range from transaction based data to unstructured data. Storage costs are decreasing. Excessive data volume is now the new norm. The challenge now is to determine the relevance of the data within large data volumes and how to use analytics to create value velocity data is being uploaded, downloaded and streamed at groundbreaking speed to maintain its value. This data must be processed in a timely manner or it will become stale. Reacting quickly enough to deal with the always changing data is a challenge for most organisations. Variety information today looks very different than how it was presented in the past. We can share videos and photos instantly, comment on a thought, using social media and transfer funds in our bank accounts on our smartphones. So it's no surprise that our data today comes in all types of formats. Structured data resides in a fixed field within a record or file. This includes data contained in relational databases and spreadsheets. Semi structured data does not conform to the formal structure of data models, but contains tags or other identifying as two separate semantic elements. Finally, there is unstructured data which refers to information that either does not have a predefined data model or is not organised in a predefined manner. E text. Html et cetera. Organisations struggle with deriving value from each type of data. Another cause of this data explosion can be attributed to the Internet of things. Iot, as defined by techno PD to Iot, is a computing concept that describes a future where everyday physical objects will be connected to the Internet and be identifiable to other devices. An object that can represent itself digitally becomes something greater than the object by itself. It does so by using emerging technologies such as near field communications, real time localisation and embedded sensors to convert. As Terry Sing in 2014 stated in his article, Digital Transformation. Dumb Things Into Smarter Things, The item enveloped with Iot Technologies, develops what can be referred to as a digital identity. The object no longer relates just to you, but is now connected to surrounding objects and database data. The challenges that arise from both big data and Iot are plentiful, and the changing landscape of how data is created through these methods leaves much to discover. Luckily, there are individuals who are willing to resolve these challenges and are not intimidated by the necessary work needed to decipher such intricacies. These individuals are data scientists. one may ask, who are these information savvy individuals, and how can I emulate them?