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Sources of Data

This article dives deeper into "The Data Flood".
© Luleå University of Technology

We live in an era where we are constantly flooded with various types of information, or as we call it, data. This data has become the backbone of the organizational and societal decisions.

The data comes in different formats, such as:

  • structured (for example excel sheets)
  • semi-structured (for example e-mails)
  • and unstructured (for example audio and video).

In the illustration below you can see the sheer amounts of data generated every second from different sources:

Sources of data

The data can be acquired from either internal or external sources. Data collected from internal sources is called internal data, while data gathered from outside the organization is referred to as external data.

Other sources of data:

Statistical: Statistical data comes in the form of surveys collected purposefully to fit research or project purposes.

Census: Census data is collected at large scale by census offices e.g., in Sweden and later used by state and non-state officials in order to for example make decisions. See for example

International statistics: This statistical data covers a wider range and normally takes global, as well as regional, perspectives. See for example

Data marketplace: This is the data which is offered by companies that work as data-as-a-service providers. They collect and organize data and customizes it to the needs of certain industries or segments. See for example

IoT data: Machine-generated data created from the Internet Of Things (IoT) makes up a valuable source of data. This data is usually generated from the sensors that are connected to electronic devices. With IoT, data can now be sourced from medical devices, vehicular processes, video games, meters, cameras, household appliances, and weather monitoring among others. See for example

Open data: This is data that is normally very diversified and opened by some government agencies. See for example

These sources have resulted in the notion of Big Data. Big data could be described as data that comes in large volumes, and is high in both variety and velocity. Big data requires an appropriate data storage and processing solution, which is called data architecture. The next step will discuss data architecture in further detail.

It is also to be noted that big data has fueled the trend toward cloud computing. Very few organizations could have enough storage and adequate skills to manage their data on-premises. Therefore, the majority controls it via the cloud. Cloud storage accommodates structured and unstructured data and provides businesses with computing as a service instead of heavily investing in hardware and other infrastructural components.

Data for climate change

There is a plethora of options to gain access to climate change data sources that help us understand the planet and make the planet better for living on.

Examples of those data sources include:

© Luleå University of Technology
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Data Science for Climate Change

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