Why Business Needs Machine Data Analytics

Why Business Needs Machine Data Analytics

Why Business Needs Machine Data Analytics

Companies are deploying the latest in machine data analytics to benefit big data and internet of things efforts, as well as IT operations and cyber-security.

Protected Production

81% of the IT decision-makers surveyed said their organization uses machine data analytics for technology operations management, and 60% cited using it for security.

Data Driven

51% said their company depends on machine data analytics for big data projects, and the same percentage cited internet of things (IoT) uses.

Benefits of Machine Data Analytics

Faster time to value: 24%, Increased revenue: 23%, Improved productivity: 18%, Faster time to resolve incidents: 17%, Faster time to market: 11%

IoT-Related Machine Data Analytics Areas

Logistics: 52%, Industrial IoT: 49%, Predictive maintenance: 39%, Smart buildings: 36%, Smart meters: 32%

Routine Function

86% of the IT decision-makers surveyed said that at least 11 people interact with their machine analytics technology at least once a week, and 15% said more than 50 people do.

Employees Most Likely to Interact With Machine Analytics

IT operations and DevOps staff: 78%, Data analysts: 68%, Data scientists: 49%, Business analysts: 38%, Business users: 36%

Preferred Paths

39% of the IT decision-makers surveyed said their machine data analytics technology is open source, 36% said it's proprietary and 25% said it's a mixture of both.

Cost Considerations

Of those using at least some form of open-source tech for machine data analytics, 52% said they do so due to the low upfront costs, and 49% cited low ongoing costs as a factor.

Open Minded

Of those using some form of open-source tech for machine data analytics, 42% said they do so because the IT staff prefers open source, and 23% said “it was the best tool for the job.”

Challenges of Machine Data Analytics Tech

Demands on infrastructure resources: 36%, Demands on staff: 33%, Difficulties in scaling: 33%, Slow production of analytics and reports take too long to design: 32%, Excessive costs: 31%