Role of Edge Analytics in Smarter Computing & Business Growth

Aashish Yadav | May 13, 2022 | 167 views

Role of Edge Analytics in Smarter
As businesses are moving towards using more and more data for decision-making, data-driven insights have become the most valuable asset for any company. Today, businesses are feeling the need to process data and access analytics in real-time. In the past, businesses collected data from various IoT devices and sensors, centralized it in a data warehouse or data lake, and then analyzed it to get insights.

What if businesses could bypass the data centralization or integration stage entirely and go straight to the analysis stage? This technique is known as edge analytics. This method allows businesses to accomplish autodidact machine learning, improve data security, and reduce data transfer costs.

With edge analytics and edge computing, businesses can not only generate more sales but also boost efficiency, enhance productivity, and save costs.

Let’s dive deeper into edge analytics, how it complements cloud computing, and why businesses are increasingly opting for it.


How can Edge Analytics Complement Cloud Computing?

Real-time decision-making is still challenging in IoT systems due to factors like bandwidth, latency, power consumption, cost, and various other considerations. This problem, however, can be addressed by using of artificial intelligence in edge analytics, which also makes cloud computing better.

Cloud computing and edge computing are very different approaches and purely depend on the software implemented. These two technologies don’t discredit each other, but rather complement each other.

  • Reduces utilization of data bandwidth or transfer
  • Ends the need for continuous connectivity to the cloud
  • Boosts the real-time performance with faster processing
  • Enhances data security


Common Pitfalls to Dodge with Edge Analytics and Edge Computing

According to Statista, the number of Internet of Things (IoT) devices will reach 30.9 billion units by 2025. Moreover, the global IoT market is expected to grow to $1.6 trillion by 2025.

The cost of transferring and storing all of that data, combined with the lack of a clear advantage, has led many to question whether the IoT is worth the hype. That is why the industry is shifting its focus to edge analytics or computing to fully leverage the data collected from IoT devices. Let’s take a look at some of the challenges that can be addressed with the help of edge analytics:

  • Many industrial IoT solutions require complete uptime.
  • Consumer IoT apps need to process localized events in real-time.
  • A power outage might result in a security breach.
  • Difficulties in adhering to data regulations.


Why You Should Employ Edge Analytics?

“To remain competitive in the post-cloud era, innovative companies are adopting edge computing due to its endless breakthrough capabilities that are not available at the core.”

- David Williams, managing principal at AHEAD.

Edge analytics solutions assist businesses wherever data insights are needed at the edge. It can be used in various industries for numerous things, such as retail customer behavior analysis, remote monitoring and maintenance, detecting fraud at ATMs and other financial sites, and monitoring manufacturing and logistical equipment. Here are some reasons you should choose edge analytics and edge computing for your business.


Saves Time

The prime objective of adopting an edge analytics system is to filter out unnecessary information prior to analysis, and only relevant data is sent via higher-order methods. This saves a lot of time when it comes to processing and uploading data, which makes the complex analytical process done on the cloud a lot more valuable and effective.


Reduces Cost

The use of edge analytics in IoT cuts the cost of data storage and administration. It also saves operating expenses, bandwidth requirements, and resources spent on data processing. All of these things add up to substantial financial savings.


Safeguards Privacy

Edge analytics assists in the preservation of privacy when sensitive or confidential data is gathered by a device, such as GPS data or video streams. This sensitive data is pre-processed on-site rather than being transferred to the cloud for processing. This additional step ensures that only data that complies with privacy laws leaves the device for further analysis.


Reduces Data Analysis Delay

Edge analytics tools enables faster, autonomous decision-making since insights are identified at the data source, preventing latency. It is more effective to analyze data on the defective device itself and shut down the faulty equipment immediately instead of waiting for the data from the equipment to be transferred to a central data analytics environment and waiting for the result.


Solves Connectivity Issues

By making sure that applications are not disrupted by restricted or interrupted network access, edge analytics in IoT helps to safeguard against possible connectivity disruptions in IoT. It is particularly beneficial in rural areas or for minimizing connection costs when utilizing costly technologies such as cellular networks.

 

Industries Leveraging Edge Analytics


Closing Lines

Edge analytics is an exciting field, with businesses in the Internet of Things (IoT) sector growing their expenditures every year. Leading vendors are actively investing in this rapidly growing market. Edge analytics provides measurable business advantages in certain industries such as retail, manufacturing, energy, and logistics by decreasing decision latency, scaling out analytics resources, resolving bandwidth issues, and perhaps reducing expenditures. The potential at the edge leads to a very exciting future of smart computing as sensors get more affordable, applications need more real-time analytics, and developing optimized, cost-effective edge algorithms becomes simpler.


FAQ

What distinguishes edge analytics from regular analytics?

Except for the location of the analysis, edge analytics offers remarkably similar capabilities to regular analytics systems. One significant difference is that edge analytics apps can run on edge devices that can have memory, processing power, or communication.


What are edge devices, and what are some examples?

An edge device serves as an access point to the core networks of businesses or service providers. Some examples include routers, switching devices, integrated access devices (IADs), multiplexers, and other metropolitan area network (MAN) and wide area network (WAN) access devices.


What exactly are edge machines?

Edge ML is a technology that allows smart devices to analyze data locally through local servers or at the device level. This is done with the help of machine and deep learning algorithms, decreasing dependency on cloud networks.

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Rangle is more than a groundbreaking team of JavaScript experts, we’re your partners in innovation. We take great ideas and apply strategy, design, and technology to craft delightful customer experiences. Committed to getting to market fast, we’re anchored by Lean/Agile methodology and a DevOps mindset, backed by our badass, world-class reputation in React, Angular, and Vue.

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Babel Street delivers critical and timely insights from across a massive, multilingual digital landscape through a single pane of glass for analysis, collaboration, and action at superhuman speed. The acquisition positions Babel Street as a highly differentiated category leader in threat intelligence, risk mitigation, and identity markets. With Rosette’s complementary capabilities, Babel Street will provide expanded solutions to better serve the mission-critical needs of a broader set of government and commercial organizations worldwide. The new, comprehensive platform will support deeper entity knowledge base creation for screening and countering insider threats, leveraging Rosette’s deep AI and machine-learning-driven natural language processing (NLP) technology, as well as name-matching capabilities. “Babel Street's deep expertise in cross-lingual search across massive amounts of data, coupled with Rosette’s ability to identify, categorize, extract, index, and analyze all aspects of multi-language text, will help improve safety and security for our customers. “With the addition of Rosette, we will create a leader in threat and risk intelligence, best positioned to capitalize on the large market opportunity at a time when this technology is increasingly vital for awareness and security. We’re thrilled to welcome Rosette’s deep bench of talent to the Babel Street team.” Michael Southworth, CEO of Babel Street “We’re excited for the opportunity to see Rosette’s technology integrated with the most comprehensive and flexible open-source intelligence platform available,” said Carl Hoffman, CEO of BasisTech. “Rosette’s NLP capabilities are a natural complement to Babel Street’s advanced analytics capabilities and will provide a powerful asset in helping screen for global threats and risk with greater context and understanding.” Customers across industries can expect the integration to offer expanded, locally deployable capabilities and the addition of AI capabilities fueled by the most comprehensive range of enriched, standardized data sources across the digital landscape. This will provide greater intent understanding and trend discovery for governments and commercial enterprises across many industries including healthcare, financial services, manufacturing, and logistics. Following the close of the transaction, Babel Street will continue to market Rosette as a standalone offering and incorporate Rosette’s differentiated technologies into its own solutions. “In addition to threat detection, conversational AI-enabling technologies like NLP have the potential to become the basis for trust between companies and their customers or trading partners,” explained Dan Miller, lead analyst at Opus Research. “Rapid recognition of intent, real-time situational awareness, and machine learning empower Babel Street and Rosette to be the cornerstone for an ever-expanding set of mission-critical use cases.” Specific capabilities that Rosette will bring to Babel Street include: Advanced Natural Language Understanding: including relevancy scoring, document clustering, and Chinese, Japanese, and Korean tokenization delivering greater accuracy and richer insights across global markets. Entity Extraction: including identifying relationships between people, places, and organizations, and entity mapping to rapidly identify and filter open-source searches across the world’s largest enriched document store. Expanded Artificial Intelligence and Machine Learning Modeling: including topic classification and intent detection, as well as risk and fraud scoring, entity attribution, and pattern detection for highly extensible capabilities across a wide range of use cases, including language analytics. About Babel Street Babel Street provides the most advanced data analytics and intelligence platform for the world’s most trusted government and commercial organizations. The AI-enabled platform helps them stay informed and improves around-the-clock decision-making for threat intelligence, identity and risk management, and alerting use cases. Teams are empowered to rapidly detect and collaborate on what matters in seconds by transforming massive amounts of global, multilingual data into actionable insights so they can act with confidence. Babel Street is headquartered in the Washington, D.C. area, with offices in London, Canberra, and Ottawa. For more information, visit babelstreet.com. About BasisTech and Rosette® Data analytics and machine learning are critical to verifying identity, understanding customers, anticipating world events, and uncovering crime. BasisTech provides businesses and governments with advanced analytics and AI-powered solutions for deriving insights from multilingual text, connecting data silos, and discovering digital evidence. The Rosette text analytics platform employs machine learning and deep neural nets to extract meaningful information from unstructured data. Autopsy, a digital forensics platform, and Cyber Triage, an incident response tool, serve the needs of law enforcement, national security, and legal technologists. KonaSearch delivers deep search across Salesforce and other data sources.

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