Report: New Logistics Pave Road for Machine Data Analytics
Machine data analytics is the process of using big data from a variety of devices to solve complex, real-world challenges. Machine data analytics can aggregate data from smartphones, websites, desktop devices and Internet servers.
How Brands Are Turning to Machine Data Analytics
Machine data is expected to transform the service models of countless businesses across the world. Machine data analytics can be used in a variety of other applications, including:
- Identifying real-time security threats and mitigating the risk of online fraud
- Understanding customers better
- Improving the functionality of smart homes
- Making smart cars viable
- Syndicating content more efficiently than ever
While machine data analytics is the future of the Internet of Things, it has failed to evolve in recent years. One of the biggest problems is that aggregating data from thousands of devices consumes many resources.
Machine data is playing an even more important role in marketing, especially since so many people use mobile devices. Marketers can collect data from mobile users and submit it to a data driven email autoresponder, which lets them carefully tailor their email messages to mobile users. Merging email and mobile marketing has helped many companies drastically improve their engagement and conversions.
A recent report may be the breakthrough big data scientists need to make machine data analytics feasible. The recent report was commissioned by Logtrust for 451 Research.
Logtrust surveyed 200 IT managers and found that 94% of them relied on data analytics to run their organizations. Slightly over half of them also used machine data, which demonstrates its benefits.
However, they have found that speed limitations have created a barrier for them.
“Nevertheless, IT managers remain frustrated by a performance gaps in current analytics platforms as they tackle more real-time data and attempt to blend it with batch and historical data analysis. The imperative, the report’s authors note, is straightforward: ‘The faster you can run some analytics on data, and subsequently respond to the findings, the greater the chance of having achieved something that adds business value…’”
The authors concluded that new logistics approaches are necessary to process and aggregate data more efficiently.
Pedro Castillo, the CEO of Logtrust, also added that in many instances, speed was far more important than scalability with many big data applications.
Logtrust, Google and other organizations are exploring new solutions to process machine data more efficiently. Google recently announced that its new machine learning chips are 15-30x faster than GPUs and CPUs.
“The conversation changed in 2013 when we projected that DNNs could become so popular that they might double computation demands on our data centers, which would be very expensive to satisfy with conventional CPUs,” the authors of Google’s paper write. “Thus, we started a high-priority project to quickly produce a custom ASIC for inference (and bought off-the-shelf GPUs for training). The goal was to improve cost-performance by 10x over GPUs.”
Using more efficient machine learning chips is important, but limiting the steps in the machine data aggregation process is even more so. Machine data is often aggregated from devices to a central repository and then accessed by other applications via Hadoop and other big data analytics tools. The process could be conducted more efficiently if crucial data was stored on other devices instead.
Of course, this isn’t feasible for all applications, especially those that require significant amounts of data. However, it could be viable for applications that rely on smaller quantities of data, where speed is a much greater priority.
As Castillo and many other experts have pointed out, brands often don’t need larger quantities of data. They often would prefer a more streamlined data aggregation process, where they can access the data they need.
Greater Efficiency is the Future of Machine Data
Brands relying on machine data have finally come to terms with their priorities. According to the Logtrust report, 51% of IT experts hope to be able to process machine data in a matter of milliseconds. Unfortunately, they haven’t come close to reaching that goal.
They are finally realizing that revising their analytics approach to focus on efficiency over quantity may be the solution.