Machine-Generated DataMachine generated data is the information generated by machines (computer, application, process or another inhuman mechanism). Machine generated data is known as amorphous data as humans can rarely modify/change this data.
Explore Data & Analytics Statistics
- By 2025, the amount of the global datasphere subject to data analysis will grow by a factor of 50 to 5.2 zettabytes.
- The number of IT professionals using descriptive and predictive analytics grew from the mid-40th percentile to high 60th percentile between January 2018 and January 2019.
- Data warehouse optimization is considered the most important big data analytics use case, and is considered critical or very important by 70 percent of businesses.
- 61 percent of businesses that recognize the effect of data and analytics on their core business practices say their companies either have not responded to these changes or have taken only ad hoc actions rather than developing a comprehensive, long-term strategy for analytics.
- Analytics leaders are nearly twice as likely as others to report enacting a long-term strategy to respond to changes in core business practices.
- Insights-driven businesses are growing at an average of more than 30% each year, and by 2021, they are predicted to take $1.8 trillion annually from their less-informed peers.
- Customer/social analysis is considered the second most important big data analytics use case, followed by predictive maintenance.
- In a survey of approximately 700 business professionals, only 15% said their organization is currently very effective in delivering a relevant and reliable customer experience. In the same survey, only 3% of respondents said they are able to act on all of the customer data they collect; 21% say they can act on very little of it.
- By 2025, 60% of the 163 zettabytes of existing data will be created and managed by enterprise organizations.
- 26 percent of businesses say data and analytics have significantly changed the nature of industry-wide competition.
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