By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    Promising Benefits of Predictive Analytics in Asset Management
    11 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Report: New Logistics Pave Road for Machine Data Analytics
Share
Notification Show More
Latest News
ai digital marketing tools
Top Five AI-Driven Digital Marketing Tools in 2023
Artificial Intelligence
ai-generated content
Is AI-Generated Content a Net Positive for Businesses?
Artificial Intelligence
predictive analytics in dropshipping
Predictive Analytics Helps New Dropshipping Businesses Thrive
Predictive Analytics
cloud data security in 2023
Top Tools for Your Cloud Data Security Stack in 2023
Cloud Computing
become a data scientist
Boosting Your Chances for Landing a Job as a Data Scientist
Jobs
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Report: New Logistics Pave Road for Machine Data Analytics
AnalyticsBig Data

Report: New Logistics Pave Road for Machine Data Analytics

Ryan Kh
Last updated: 2017/05/20 at 8:32 PM
Ryan Kh
5 Min Read
machine data analytics
SHARE

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.

Contents
How Brands Are Turning to Machine Data AnalyticsGreater Efficiency is the Future of Machine Data

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.

More Read

machine learning and mesh networks

Machine Learning Improves Mesh Networks & Fights Dead Zones

7 Mistakes to Avoid When Using Machine Learning for SEO
Use this Strategic Approach to Maximize Your Data’s Value
Machine Learning is Invaluable for Mobile App Testing Automation
Top 8 Machine Learning Development Companies in 2022

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.

TAGGED: machine learning
Ryan Kh May 9, 2017
Share this Article
Facebook Twitter Pinterest LinkedIn
Share
By Ryan Kh
Follow:
Ryan Kh is an experienced blogger, digital content & social marketer. Founder of Catalyst For Business and contributor to search giants like Yahoo Finance, MSN. He is passionate about covering topics like big data, business intelligence, startups & entrepreneurship. Email: ryankh14@icloud.com

Follow us on Facebook

Latest News

ai digital marketing tools
Top Five AI-Driven Digital Marketing Tools in 2023
Artificial Intelligence
ai-generated content
Is AI-Generated Content a Net Positive for Businesses?
Artificial Intelligence
predictive analytics in dropshipping
Predictive Analytics Helps New Dropshipping Businesses Thrive
Predictive Analytics
cloud data security in 2023
Top Tools for Your Cloud Data Security Stack in 2023
Cloud Computing

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

Sign Up for Our Newsletter

Subscribe to our newsletter to get our newest articles instantly!

[mc4wp_form id=”1616″]

You Might also Like

machine learning and mesh networks
Machine Learning

Machine Learning Improves Mesh Networks & Fights Dead Zones

7 Min Read
machine learning seo
Machine Learning

7 Mistakes to Avoid When Using Machine Learning for SEO

6 Min Read
analyzing big data for its quality and value
Big Data

Use this Strategic Approach to Maximize Your Data’s Value

6 Min Read
machine learning helps with the testing process for mobile app development
Machine Learning

Machine Learning is Invaluable for Mobile App Testing Automation

9 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?