HuMAN-CENTERED DATA LABELING
By 2027, the market size of data labeling is projected to be
$11.6 Billion
As organizations continue to adopt AI and machine learning applications, the demand for high-quality labeled data is growing, driving the growth of the data labeling market. With accurate data labeling, organizations can improve the accuracy and reliability of their machine learning models, leading to better decision-making and a competitive advantage in the market.
Data labeling can be a bottleneck in AI development, as labeling tasks can
take up to 60-80%
of the total time spent on data preparation. Inaccurate labeling can lead to suboptimal decision-making, decreased efficiency, and financial loss. To fully harness the power of their data, organizations must invest in high-quality data labeling to improve the accuracy and reliability of their machine learning models and gain a competitive advantage.
Human-Centered Data Labeling Tool
Are you tired of relying solely on anomaly detection for your data analysis? Do you want to unlock the full potential of your data? Pandata Tech's Human-Centered Data Labeling tool combines the power of AI and machine learning with human expertise to provide reliable and accurate labeling. With automated data validation and human-centered labeling, you can reduce data cleaning and validation time by up to 60% while gaining nuanced, real-world insights from your datasets. This means you can start building more robust predictive models and stay ahead of the competition.
By using our Human-Centered Data Labeling tool, your organization can save time and resources and unlock the full potential of your data. You can solve domain problems and improve predictive models with accurate and reliable labeling. Don't settle for just detecting anomalies. Upgrade to our system and gain comprehensive insights into your data.
What the HCDL Tool Helps Prevent
Downtime
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Human-Centered Data Labeling can prevent false positives and false negatives by providing better data to domain and maintenance models.
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Human-Centered Data Labeling helps to prevent unexpected equipment downtime by identifying potential maintenance issues early, reducing lost productivity and revenue.
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Human-Centered Data Labeling can help prevent equipment failures by identifying potential issues early, enabling maintenance teams to take proactive steps to prevent downtime.
Inaccurate and Bad Data Quality
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Human-Centered Data Labeling ensures that data is properly labeled and consistent, reducing errors and improving the quality of data-driven insights.
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Human-Centered Data Labeling provides actionable insights from data by ensuring that it is properly labeled and organized, making it easier to identify trends and patterns.
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Human-Centered Data Labeling ensures that data-driven recommendations are based on accurate and complete data, reducing the risk of making decisions based on incomplete or inaccurate information.
Maintenance Issues
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Prevent data outages: Human-Centered Data Labeling can help prevent data outages by identifying potential issues in real-time and ensuring that data is properly labeled and categorized, making it easier to recover from outages and maintain operational resilience.
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Human-centered data labeling can help prevent knowledge transfer issues by ensuring that data is labeled and organized in a clear and consistent manner. Properly labeled data can be used to train machine learning models, which can automate knowledge transfer processes and improve the accuracy of knowledge transfer outcomes.
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Human-centered data labeling can help prevent issues that arise from generalized models that are not specific to individual operations. Labeled data can help make generalized models more resilient and reduce risks.
Operational Vulnerabilities
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Human-Centered Data Labeling improves the accuracy of predictive maintenance models by up to 30% by providing properly labeled data.
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Human-Centered Data Labeling helps to identify maintenance issues faster, reducing resolution time by up to 50%.
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Human-Centered Data Labeling provides nuanced, real-world insight from datasets, allowing maintenance teams to specify maintenance problems and troubleshoot for solutions.
Interested in learning how Pandata Tech’s Human-Cenered Data Labeling system can help your organization?
Real World Impact
SCADA/ICS CYBERSECURITY
In 2015, the Ukrainian power grid was targeted by a cyberattack that caused a blackout for
230,000+ Customers
The attack was successful in part because the ICS systems were not properly labeled, making them vulnerable to exploitation.
Human-centered data labeling can prevent such vulnerabilities by ensuring that sensitive information is properly labeled and protected in data analytics models.
Real World Impact
PREVENTING MACHINE-LEARNING INACCURACIES
In 2020, MIT Sloan Review found that labeling data correctly was critical to the performance of machine-learning models, as incorrect labels
Reduced Accuracy
by up to 95%
Human-centered data labeling can prevent such inaccuracies and help improve machine-learning models by ensuring that the data is labeled correctly.
Real World Impact
DOMAIN OPTIMIZATION
Properly labeled data improves the accuracy of predictive maintenance models by
up to 30%
and reduces the time it takes to resolve domain issues by
up to 50%
Human-centered data labeling can prevent inaccurate predictive maintenance models and help identify domain issues faster.
Benefits of the HCDL System
Improved Data Quality and Accuracy
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Boosts data accuracy by enabling organizations to make better-informed decisions and build stronger user relationships.
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Enhances data reliability by combining AI and human expertise, providing a comprehensive approach to data labeling.
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Ensures the quality of data for digital transformation initiatives, helping organizations achieve their strategic goals.
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Labeled data is data with context— which is easier to use and better for modeling.
Increased Efficiency and Cost Savings
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Reduces data cleaning and validation time by up to 60% with automated data validation and human-centered data labeling, allowing organizations to allocate resources more effectively.
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Helps organizations manage and label massive amounts of data as the global datasphere grows by providing a scalable and efficient data labeling solution.
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Minimizes the financial impact of poor data quality on organizations by providing a reliable data labeling system that reduces revenue loss due to inaccurate data.
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Reduces operational costs and improves efficiency by mitigating the negative impacts of poor data quality on business processes and decision-making.
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Minimizes the risk of inaccurate predictions by providing accurate and reliable data labeling, leading to better-performing AI and machine learning models.
Enhanced Decision-Making and Predictive Modeling
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Addresses the concerns of CEOs regarding data accuracy for operational decision-making by providing a comprehensive and reliable data labeling solution.
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Enables organizations to realize the full value of their data and gain a competitive advantage by providing a high-quality data labeling system.
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Improves the performance of AI and machine learning models by ensuring access to high-quality, accurately labeled data.
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Supports data-driven decision-making, leading to higher user satisfaction and profitability rates.
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Enables organizations to unlock the full potential of their data by providing accurate and reliable data labeling, leading to better insights and improved operational outcomes.
Better Outcomes for Maintenance and Domain Models
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Helps organizations solve maintenance problems by providing detailed, accurate data labeling that allows for better decision-making and improved maintenance outcomes.
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Enhances the accuracy of predictive models by ensuring high-quality data, resulting in efficient operations and reduced costs.
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Enables organizations to identify historical operational events more accurately, leading to better predictions and operational planning.
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Empowers organizations to stay ahead of the competition by leveraging accurate and reliable data labeling to build better predictive models and make data-driven decisions.
