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Data scientists have a tough job when it comes to collecting data from cloud platforms. This affects how well they can find useful information. Here are some of the main challenges they deal with:
Data Privacy and Rules: When data is collected from cloud services, it often includes private information. There are rules like GDPR and HIPAA that need to be followed. If these rules aren’t followed, companies can face big fines—over $20 million or 4% of their yearly earnings, whichever is more!
Mixing Data Problems: When trying to bring together data from different cloud sources, things can get messy. A study by Gartner found that more than 70% of projects that mix data fail because the data is not consistent. This can lead to wrong conclusions.
High Costs: Cloud platforms can seem like a good idea because they can grow with your needs, but storing and moving data can get pricey. For example, sending data can cost between 0.12 for every GB. For companies with a lot of data, these costs can add up quickly.
Slow Performance: Sometimes, getting data from remote cloud locations takes longer than expected. This can be a problem for real-time analysis. Studies show that 60% of businesses notice their performance dropping during busy times.
Data Security Risks: Cloud platforms can be at risk of hacks and security issues. A survey from 2021 showed that 79% of business leaders are worried about keeping data safe in the cloud. This makes them hesitant to move sensitive information.
In short, data scientists face many challenges like following rules, mixing data correctly, managing costs, ensuring fast performance, and keeping data secure while collecting information from cloud platforms.
Data scientists have a tough job when it comes to collecting data from cloud platforms. This affects how well they can find useful information. Here are some of the main challenges they deal with:
Data Privacy and Rules: When data is collected from cloud services, it often includes private information. There are rules like GDPR and HIPAA that need to be followed. If these rules aren’t followed, companies can face big fines—over $20 million or 4% of their yearly earnings, whichever is more!
Mixing Data Problems: When trying to bring together data from different cloud sources, things can get messy. A study by Gartner found that more than 70% of projects that mix data fail because the data is not consistent. This can lead to wrong conclusions.
High Costs: Cloud platforms can seem like a good idea because they can grow with your needs, but storing and moving data can get pricey. For example, sending data can cost between 0.12 for every GB. For companies with a lot of data, these costs can add up quickly.
Slow Performance: Sometimes, getting data from remote cloud locations takes longer than expected. This can be a problem for real-time analysis. Studies show that 60% of businesses notice their performance dropping during busy times.
Data Security Risks: Cloud platforms can be at risk of hacks and security issues. A survey from 2021 showed that 79% of business leaders are worried about keeping data safe in the cloud. This makes them hesitant to move sensitive information.
In short, data scientists face many challenges like following rules, mixing data correctly, managing costs, ensuring fast performance, and keeping data secure while collecting information from cloud platforms.