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What Is Big Data?

Big data sets have high volume, velocity, and variety characteristics that require specific technology and analytical methods beyond traditional data processing.

Apart from data sources such as mobile phones and computers, there has also been a boom in Internet of Things (IoT) devices. Items that may be in your home today like security systems, temperature regulators, and refrigerators generate data. Automated machines in factories and self driving cars are also constantly collecting and sending data to servers, the cloud, and the edge.

With hi-speed internet access , 4G/5G, and a simultaneous increase in users, there has been an exponential growth in the volume of incoming data. This is expected to grow as the current worldwide digital population as of April 2022 is about 5 billion and is forecasted to climb to 5.63 billion in 2025. 

This large-scale usage of the internet generates millions of bytes of data every second, which poses challenges like how to process and analyze the data effectively in a short amount of time.

Unstructured vs. structured data

Not all data collected as a part of big data has the same characteristics, it is divided into two high-level categories. Structured data is usually stored in the form of text, it is well organized in nature with data sequentially arranged in the form of rows and columns. This type of data is well suited for analysis and data mining tools and compatible with relational database management systems (RDBMS). Unstructured data can belong to any format such as audio, video, text, or radio sensors, and is unfinished and unorganized in nature, and therefore is not easily compatible with RDBMS or other data mining tools.

Challenges with analyzing big data

Data can provide valuable business intelligence but it is also time-dependent. Given the rapid flow of information through dense high-speed connections, it is imperative to process it in a short amount of time so the information extracted doesn’t become outdated. Efficient processing is a challenge with big data as computational sources from servers to the cloud can become expensive at high volumes.

Ways to analyze big data

Here are some methods that can be used to analyze big data:

  • Cluster analysis, such as a K-means algorithm
  • Genetic algorithms
  • Natural language processing
  • Machine learning, neural networks, predictive modeling, regression models
  • Social network analysis, sentiment analysis, such as using tweets to determine customer sentiments
  • Data visualization to summarize insightful information


Big data is defined in different ways but contains these common characteristics- high velocity, volume, and variety. It can be divided into two main categories: structured and unstructured data. Big data analysis presents several challenges that can thwart effective, efficient, and instantaneous processing. 

Macrometa’s real-time analytics help solve these challenges and make it easier for businesses and decision makers to access insightful information. Data manipulation functions can also be used to spot early trends or patterns in data and prevent or quickly respond to business opportunities and threats. Moreover, it allows the integration of data from different enterprises/nodes which can then be processed and sent into other systems.


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