Predictive analytics has emerged as a game-changing technology in the data storage landscape. By harnessing the power of statistical analysis and predictive modeling, organizations can now forecast trends, anticipate potential issues, and make data-driven decisions like never before.
As data continues to grow at an unprecedented rate, the need for efficient and intelligent storage solutions becomes paramount. However, implementing predictive storage analytics is not without its challenges. You’ll need to invest time and resources in researching the right solutions, integrating them into your existing infrastructure, and training your team to leverage the insights effectively.
Major storage vendors, such as HPE, are recognizing the immense potential of predictive analytics and are actively incorporating it into their product portfolios. This trend is set to continue as machine learning and AI technologies advance, making predictive analytics even more powerful and accessible.
To stay ahead of the curve, it’s crucial to assess your organization’s readiness for predictive storage analytics. By understanding the benefits, challenges, and best practices, you can make informed decisions and unlock the full potential of your data storage environment. Embrace the power of predictive analytics today and revolutionize the way you manage your data!
Table of Contents
Question 1
Predictive analytics isn’t going to be a major trend moving forward.
A. True
B. False
Answer
B. False
Explanation
Once a specialized feature, predictive storage analytics has risen to prominence in the past year. As the demand for real-time intelligence for storage and performance has grown, predictive analytics has seen wider use. Big data has also spurred the need for storage analytics, and it is only becoming a bigger concern. Being able to analyze large amounts of data enables an organization to better predict its needs, as well as identify potential problems down the line.
Question 2
Which technology can give predictive analytics a performance boost?
A. AI
B. Flash
C. Hard disks
D. None of the above
Answer
B. Flash
Explanation
Another factor in the rise of predictive analytics in storage is the evolution of flash storage technology. The incomparable speed of flash storage is practically a must-have when it comes to the complex processes involved in predictive analytics. Flash storage can enable analysis at a much larger scale and in a shorter time frame than other storage methods. Until recently, it was rendered unavailable to many organizations because of its high cost.
As the cost of flash storage continues to drop, it is becoming a more viable option for predictive storage analytics. Since flash is one of the only technologies currently able to quickly and efficiently process the information needed for predictive analytics, reductions in its cost is welcome news.
Question 3
Using predictive storage analytics can be taxing on IT infrastructures.
A. True
B. False
Answer
A. True
Explanation
Predictive analytics is a complex process that can place a burden on storage systems. The capacity and power required to run predictive analytics make it different from other conventional business intelligence processes. Dedicated storage spaces, as well as additional hardware purchases, could be needed down the line.
Implementing cloud-based predictive analytics, rather than on-premises predictive analytics, is one way to provide these dedicated storage spaces without significantly driving up costs.
Question 4
Which storage analytics method helps train predictive algorithms by constantly updating data?
A. Data mining
B. Predictive modeling
C. Machine learning
D. Analytical queries
Answer
C. Machine learning
Explanation
While all of the above are components of predictive analytics, machine learning is used to create and train algorithms and forecast specific outcomes. Predictive analytics relies on machine learning in particular to detect and diagnose problems and then solve those issues.
With machine learning and predictive analytics, organizations can address storage roadblocks before they occur and plan for future hardware and software changes. While machine learning is an important tool, choosing the right algorithms in the first place is vital.
Question 5
Which vendor did HPE acquire for cloud-based predictive analytics?
A. Nimble Storage
B. Pure Storage
C. SimpliVity
D. Cohesity
Answer
A. Nimble Storage
Explanation
Widely regarded as the pioneer of predictive analytics, Nimble Storage was acquired by HPE in 2017 for $1.2 billion. Nimble’s InfoSight uses machine learning to resolve performance issues in HPE storage platforms, and HPE has plans to further expand the predictive analytics capabilities across its portfolio.
Cohesity is a partner of HPE, while SimpliVity was acquired by the vendor for its hyper-convergence. Pure Storage is a competitor with HPE in the flash realm, with the Pure Storage FlashArray in the market against HPE’s 3PAR Flash.
Question 6
Cloud-based predictive analytics is seeing wider adoption.
A. True
B. False
Answer
A. True
Explanation
Reducing overhead involved with predictive storage analytics is a high priority, so it’s no wonder that cloud-based analytics are seeing a bump in popularity. Along with processing large amounts of data and running complex analysis, cloud-based services save organizations from having to implement internal troubleshooting and support, adding to overall savings. According to HPE, those who used InfoSight for predictive analytics saw 79% lower storage operational expenses.
Along with InfoSight, major cloud-based predictive analytics services include Pure1 from Pure Storage and Tintri Analytics.