The Internet of Things offers a world of data, visibility, and opportunity that didn’t even exist a decade ago. From manufacturing to transportation, organizations in nearly every sector are using IoT-based solutions to drive new levels of operational efficiencies by leveraging key trends in machine learning and edge computing.
While the opportunities of IoT solutions are limitless, companies are struggling with some significant challenges when moving from pilots to large-scale deployments. In addition to connectivity limitations, security risks, and uncertain return on investment, many organizations struggle to ingest, normalize, align, and then infer actionable insights from the copious amount of data streams. Getting this right is the first step to deploying effective machine learning-enabled IoT solutions.
To eliminate these challenges in 2020 and beyond, organizations will start prioritizing the quality and accuracy of their data. Fortunately, edge processing offers a new way to ensure users can rapidly attain quality data to drive real-time analysis.
Data quality impacts the success of deployments
Global IoT spending is growing at a double-digit rate and is forecast to reach $1.2 trillion in 2022, according to IDC. While IDC identified 82 major IoT use cases in twenty industries, innovative organizations are continually creating new applications.
As ideas move from proofs of concept to commercial deployments, data quality will influence the success and profitability of many applications. Trying to roll out IoT projects with insufficient, missing, or error-prone raw and streaming data often leads to inaccurate machine learning models, stagnant decision making, and a subpar return on investment.
This “dirty data” now costs organizations more than $600 billion per year in failed and underperforming IT projects, according to the Data Warehouse Institute. And data scientists now spend more than 80 percent of their time merely cleaning and organizing data, leaving only 20 percent of their time to actually analyze it, according to a report by IBM.
Now more than ever, organizations that are serious about IoT deployments must put data quality at the forefront to ensure machine learning models can accurately make more informed, data-driven decisions.
The role of edge computing in data quality
Data quality is often influenced not just by inputs and sources but by the systems used to gather and process it. With an endless variety of sensors and platforms on the market, many organizations struggle to align dozens or even hundreds of streams into a useable format that supports high-velocity streaming and real-time analytics.
As the volume and sources of the data expand, organizations need new ways to gather and process raw data insights from remote data sources to create a complete picture of operations.
One way to make the most of the data and to improve quality is to move computing power closer to where the data is generated. Edge computing is especially useful in IoT deployments because it enables organizations to turn raw data at the source into actionable insights with real-time processing and analytics. Edge-enabled devices help clean and format dirty data locally, which improves the training and deployment of accurate and effective machine learning models. Indeed, industry researchers believe edge-based use cases for IoT will be a powerful catalyst for growth across the key vertical markets - and that data will be processed (in some form) by edge computing in 59 percent of IoT deployments by 2025.
Industry researchers believe edge-based use cases for IoT will be a powerful catalyst for growth across the key vertical markets. Forty-four percent of companies are already using some type of edge computing in their deployments, according to a report by Strategy Analytics. And Gartner predicts that by 2025, three-quarters of all enterprise-generated data will be created and processed outside of the cloud systems or centralized data centers.
Edge computing is used in many industries and can take many forms, from onboard processes to stand-alone devices. In factories, manufacturers now use edge processing in sensors to gain real-time insights into how their production lines and equipment is operating. Through machine learning models, they can identify product outliners to reduce defective parts and to minimize unplanned downtime, maximize yield and increase machine utilization. And in the transportation industry, edge computing in cameras, driver assistance, and collision avoidance technologies enable greater efficiency, reliability, and safety.
While edge computing will provide a vehicle to attain these new insights, quality data is the fuel that will power it. By cleansing and enriching dirty data at the point of its creation, edge computing can significantly enhance data quality and refine repetitive machine data for better operational efficiencies.