Growth of Data

 The fascinating phenomenon of growth of data driven by our increasingly interconnected world has some important aspects like data explosion, factors fuelling data growth or storage capacity. For example, in 2020, this data volume reached an impressive 64.2 zettabytes and in the next five years global data creation is projected to soar to more than 180 zettabytes.

We have to mention that other factor like covid19 accelerate data growth process too due to increased remote work, online learning and home entertainment options.

In summary, our digital universe is expanding at an astonish pace, an managing this data deluge remains a critical challenge for enterprise and individuals alike.

Comments

Post a Comment

Popular posts from this blog

Big Data

Image
It is a diverse set of information that grow at ever-increasing rates. It encompasses three different aspects which often referred to as “The Three Vs” (Volume, Velocity and Variety). So Basically, Big Data is how the large, complex and variety of data is handled so this allow organizations to address business challenges and manage this information in the most efficient and proper way possible.
Read more

Limitations of Predictive Analytics

  It is a powerful tool but it comes with many limitations that affects its accuracy and reliability. Here we have come examples: 1.     Data Quality : Predictive models rely on large, accurate, and relevant datasets. If the data is incomplete, inaccurate, or biased, the predictions will be flawed. 2.     Human Behaviour : Predictive analytics cannot always accurately predict human behaviour , which can be influenced by numerous unpredictable factors. 3.     Data Relevance : The data sets need to be consistently updated to remain relevant, as outdated information can lead to incorrect predictions. 4.     Clear Goals : Without clear goals, predictive analytics can produce results that are not actionable or relevant to the business’s needs. 5.     Complexity of Models : The more complex a model, the harder it is to interpret the results, which can lead to misunderstandings or misapplications of the data. 6.     Overfitting : Models that are too closely fitted to historical d
Read more

Contemporary applications of big data in businesses

  Big data became a game changer across various industries, enabling organizations to get valuable insights and drive decisions making. Factors that we can mention Marketing, Transportation, Government and public Administration, business operation, health care, cybersecurity, etc. Let’s mention some of the applications examples. For example, In marketing they use the forecast customer behaviour to analyse customer information, predict market trends, and understand buyers habits so this helps prioritize products and service effectively. In transportation we have to mention assist in GPS navigation, traffic and weather alerts so big data helps to optimize routes, predict traffic congestion and provide real time weather information or traffic. Big data is also used in cybersecurity where helps identify security risks, detect anomalies, and protect against cyber threats. As we mention before in our previous posts, big data is not just about managing vast volumes of data but its abo
Read more