Recognizing data types – indispensable in the age of Big Data

Recognizing data types – indispensable in the age of Big Data

Data are the new value chains. Experts are sure that many of the current business problems are linked to a lack of knowledge about Big Data. The first step to find solutions consists in knowing how to identify the type of data you are dealing with and linking it to the business problem in question.

15 Jan. 2019

Big Data means handling large quantities of data, which may be of different types. The consulting firm IBM has its own classification of data types, which can be used as an example. There are five types:

  1. Web and social networks

Web content and information retrieved from social networks such as Facebook, Twitter, LinkedIn, etc.

  1. Big Transaction Data

Records of billing, detailed records of calls (CDR), etc. Transactional data in semi-structured and unstructured formats.

  1. Machine-to-Machine (M2M)

M2M means the technologies for connecting to other devices. M2M uses devices such as sensors or meters which capture a specific event and transmit it to other applications via wired, wireless or hybrid networks. The applications then translate these events into significant information, security and intelligence. Biometric data are important for investigation agencies.

  1. Biometric

Biometric information such as digital fingerprints, retinal scan, facial recognition, genetic recognition, etc. In security and intelligence, biometric data are important information for investigation agencies.

  1. Human-Generated data

People generate data such as the information saved by a call center during a phone call, voice notes, emails, electronic documents, medical exams, etc.

Also, data can be classified into three types depending on their structure:

  1. Structured data

Data whose length and format are well defined and stored in tables. They can be ordered and processed easily by any data management tool. Examples of structured data: dates, data sheets or databases.

  1. Semi-structured data

Not regular; as such, this information cannot be managed in a standard way. These data have their own semi-structured metadata describing objects and their relationships; they may eventually be accepted by convention. HTML, JSON and XML are semi-structured data.

  1. Unstructured data

Binary data with no identifiable internal structure. Mass, disorganized set of data with no value until they are organized (identified and stored). Examples include images, videos, audios, PDFs, RRSS and .txt.

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