Here’re the top 5 Data Labeling challenges organizations face to successfully train their AI/ML models.
Let’s begin with some numbers! Statista reports show that in 2018 alone, the market for manual data labeling stood at around $500M, an essential process towards improving the accuracy of Artificial Intelligence and Machine Learning. However, further reports from Cognilytica suggest that the data labeling industry is set to take off over the next five years and is predicted to reach an evaluation of $1.2B by 2023. What makes data labeling challenging is the process of manually training the algorithm backed by precisely annotated data to further improve its recognition accuracy.
To train an AI algorithm to identify and differentiate between green and red lights in traffic signals, someone has to manually scrub through thousands of images and annotate red lights against green lights. And as industry experts already know, AI confidence can only be built through rigorously trained systems with well-structured smart data. Data labeling has its unique set of challenges that deteriorate its efficiency, primarily the lack of data quality, as 19% of businesses have faced data quality issues in their industry-wide adaptation of AI.
LET’S UNDERSTAND DATA LABELING FIRST
At its core, Data Labeling or Data Annotation refers to the pre-processing stage during the development of an AI or ML model. However, Data Annotation can continue even after the deployment of the final AI/ML model to further improvise on the accuracy. This human-intervened process requires manual identification of Big Data (in the form of images, audio, or videos) and precisely annotating them to specify the content. Following this, the annotated data is fed into AI/ML models, allowing them to make accurate predictions in AI, ML, Computer Vision, and NLP.
5 DATA LABELING CHALLENGES THAT DETERIORATE EFFICIENCY
While the core concept seems no big deal, there are concerns that Data Engineers must address. Including the lack of data quality that 19% of businesses reported, 5 major blockers bring down the efficiency of data labeling.
Blocker #1: Not Having Realistic Goals, Budget, or Key Performance Indicators
“Goals properly set are halfway reached” – Zig Ziglar
Recent reports from Statista show that budget has always been the concern in the deployment of AI/ML projects. Notably in the United States, 33% of respondents reported higher costs of data labeling as the primary barrier to integrating AI/ML in business. Similarly, without setting clear goals, data annotators cannot collaborate, while the team will fail to align actions and it would be impossible to know if the project has reached its goal. Furthermore, without Key Performance Indicators, leaders would be making decisions on the grounds of inaccurate feedback.
Blocker #2: Lack of Training Data Skills/Expertise Including Domain Knowledge
“Information is the oil of the 21st century, and data analytics is the combustion engine!” – Peter Sondergaard, Gartner Research
To develop and train AI/ML models that deliver the most accurate results not just requires vast pools of big data, but also requires well-trained manpower. In data labeling, it’s important to maintain high standards in both quality and quantity. Any human-prone errors, in worst cases, can trigger error-chains, ultimately rendering the project invalid. What concerns, even more, is the fact that organizations often lack domain experts who are not just trained in data science but also possess some sort of prior experience in technology-related aspects in business applications.
Blocker #3: Inefficiencies in Workforce Management
New AI/ML models require vast amounts of big data. And given that the data labeling process hasn’t yet benefited from existing AI deployments that can at least filter out unstructured data into manageable chunks, the process still requires intensive manual labor. As the datasets grow, organizations hire huge workforces to annotate enormous data to feed into AI/ML models. This imposes another challenge – workforce management! Quantity aside, labeling unstructured data to the optimum quality is the key to achieving higher accuracy, where organizations are failing.
Blocker #4: Lack of Human-Machine Collaboration
Artificial Intelligence has an awful reputation of being an employment killer. While this statement is true to some extent, the outcomes are ultimately possible through collaborative intelligence – the most prominent example being data annotation itself. Failing to understand this collaboration, organizations don’t take the maximum advantage of how humans and AI/ML can complement strengths of one another and effectively augment machines to yield better results. While machines can enhance humans’ ability in leadership, as well as redesign core business processes.
Blocker #5: Ineffective Quality Assurance Processes
Artificial Intelligence and Machine Learning are something that equally benefits from the quality and quantity of big data. While quantity can be targeted easily with an increase in workforce, if the data quality isn’t on par with certain standards, AI/ML models won’t be trained with the right inputs, rendering the project invalid. Alongside rendering high-quality data, validating whether the data complies with standards, is another challenge to data annotation organizations. Not to miss out on the importance of consistency to ensure the right predictions by the AI/ML model.
Schedule Your Free Consultation with Our Data Labeling Experts
If you are concerned about the data labeling challenges that can bottleneck your project, you’d be relieved to know that consistent data labeling, while ensuring enhanced data security, is possible right now. The demand for high-quality data labeling has never been higher, and with over 25 years of experience in data labeling, Apex CoVantage can provide you with the right resources to help train your AI/ML models faster, that too, with better accuracy. Schedule your free consultation with our world-class data labeling experts at Apex CoVantage.