Edge Analytics and the Future of Data Entry Processes

Published On January 09, 2018 - by Admin

Edge Analytics is a substitute for centralized data analysis and enables the processing and analysis of data at any location or point where data is generated. Businesses can perform analysis at the edge of a network, gateway, or data source, driving real-time capabilities and responsiveness.

More Data to be Generated Outside of Centralized Data Centers

Gartner forecasts that by 2022, 50% of enterprise generated data will be created and processed outside of traditional centralized data centers or the cloud, up from the current 10% in 2017. This figure demonstrates the need for data and analytics based solutions at the actual points of data generation, rather than relying on centralized servers and networks and the inevitable slower processing rates associated with these techniques.

IoT is the enabler for Edge Analytics, and the continued growth of censored devices has paved the way for data processing and analysis to be conducted in real-time wherever data is produced. In fact, IHS predicts that installed IoT devices will reach an astonishing 30.7 billion devices in 2020, and upwards of 75.4 billion in 2025.

Edge Analytics for Overcoming Data Management Challenges

Businesses can seize opportunities presented by IoT’s rapid growth, and improve their responsiveness and overcome data management challenges, which is where the value of Edge Analytics resides.

The shift of analysis capabilities at the place of computing is changing data entry processes to a more agile, distributed architecture. Edge Analytics is causing an inherent disruption in data entry that will demand a transformation of data entry methodologies as we move into the coming years.

Key Takeaways from Edge Analytics

Edge Analytics is a process that utilizes an algorithm that analyzes data as it’s generated, which allows companies to better control and define the data that is sent to the cloud, and the data that can be sent to a centralized server or data lake. Real-time analysis at the point of generation provides numerous advantages:

Reduction of latency
Faster data processing
Sidesteps issues related to strained central systems and slow network availability
Alleviates challenges from managing massive volumes of streaming data from so many diverse connected IoT devices
Businesses can utilize analytics tools directly at the source

Edge sources are found in locations that enable onsite data processing and analysis that’s conducted in unison with cloud capabilities:

Edge Sensors and Actuators: Operate without their power supply or operating system, and connect with Edge Devices or Gateways as channels through the cloud and IoT technologies.
Edge Devices Feature their operating system and power source to process data and manage computations autonomously, or via an Edge Gateway.
Edge Gateways: Have their operating system, but more significant storage, memory, and processing power than Edge Devices. Capable of gathering data and processing algorithms before uploading information into the cloud.

The Data Entry Connection

Edge Analytics will critically alter the current traditional configuration of data entry processes. While data entry is becoming increasingly automated to adapt to digital technologies like AI and Machine Learning, with Edge Analytics, it will become more focused on location.

Edge Analytics will impact data entry in the following ways:

Drive data collection and entry points “to the edge.”
Data entry points will increasingly be located in the field on location, and at the network edge, which also offers increased security as data will be able to move more securely between IoT devices and the cloud.
Data entry processes will benefit from reduced limitations in network bandwidth.
Lowers costs associated with data entry.
Data governance policies, including collection, organization, and storage, will need to be updated to accommodate how data is stored at the edge.
Edge Mining techniques, which occurs on censored devices at IoT edge points, will be utilized to compress data farming within Wireless Sensor Networks.

Change in Deploying Data Entry Solutions

Businesses will need to deploy data entry solutions at points of data generation to accommodate IoT censored devices and cloud capabilities.

A few examples of industry-driven data entry solutions within Edge Analytics include:

Industrial Applications: Data can be collected, processed, analyzed, and provide actionable insights at the data source on manufacturing equipment and plants, transportation equipment, factories, warehouses, and other industrial equipment. This improves safety measures and reaction time to data changes, preventing equipment failures and malfunctions while reducing costs.
Precision Agriculture: Data collection and data validation can be performed to produce insights to optimize crop growth and allow farmers to benefit from real-time decision-making
Automated Vehicles and Machinery: Self-driving cars, industrial drones, and various IoT enabled machinery will be able to make instantaneous decisions according to the data input and data collection.

Accommodating Edge Analytics with On-Site Data Entry

Edge Analytics is changing the context of data entry and all its components, including data collection, mining, validation, processing, and analysis, and pushing it to the edge of the network for improved efficiency, agility, and responsiveness.

Data Entry Outsourced (DEO) continues to develop methodologies and tools to work in conjunction with swiftly advancing technologies and computing techniques. Standing at the forefront of automated data entry processes, DEO’s data entry solutions are providing organizations with the versatility and scalability needed to take advantage of trending digital technologies.

– DataEntryOutsourced