Data is vital for businesses to get insights, analyze trends and make informed decisions. Looking ahead, the data revolution shows no signs of slowing down and by 2025, the amount of data generated each day may reach 463 exabytes globally.
With the rising reliance of businesses on data-based decision-making, the need for data entry has also witnessed an uptick.
AI, which has impacted almost all spheres of our lives, has brought promising solutions to the tedious and error-prone data entry process as well. Numerous businesses now use AI-powered data entry systems to automate data entry and extraction from diverse sources. However, despite the advancements in AI, data entry still faces some challenges, making it an unreliable solution in some cases. Thus, while AI can improve data entry, it cannot eliminate human intervention and vigilance in ensuring data accuracy and reliability.
The Challenges of Automated Data Entry
Businesses need data entry because it is the foundation of their operations and enables accurate record-keeping and informed decision-making. Data input procedures have undergone a substantial transformation thanks to AI, which has increased automation and efficiency. AI-enabled systems can quickly extract, classify, and analyze enormous amounts of data while also minimizing human error and effort. But incorporating AI into data entry has its own set of challenges. Let’s look at some of them below.
Security and Privacy Concerns
Statistics reveal that cybercrime impacted approximately 422 million people in 2022 alone. AI-based data entry can further add to these concerns about cybercrimes and security breaches. It is mainly because AI systems are fed sensitive information, including personal identifiers, financial data, and medical records. If not properly secured, this data can be used unethically without authorization, leading to misuse and privacy breaches. As AI also interacts with data sources and databases, it creates an additional threat of cyberattacks.
Web scraping is the first step in any environment where data entry or processing has to happen. It is also a critical part of the data enrichment process. However, an automated system performing this task may fail in many cases.
- Anti-scraping techniques like captchas placed on certain websites can pose obstacles for AI, requiring human intervention.
- In some cases, the accuracy might also get hampered and necessitate human involvement.
This could reduce the process’s efficiency and diminish the credibility of the scraped data. This is usually where manual data research and capture are needed for further validation and effective data entry.
Businesses wanting to leverage AI systems for data entry might be restricted by price tags. Such solutions can be heavy on the pocket, especially for smaller businesses where manual data entry might be a more cost-effective alternative. For example, UiPath is a popular AI data entry system that uses RPA (robotic process automation) to automate tasks. The cost of UiPath starts at $3,990 per year for a single user license. These expenses can be attributed to several things, such as the infrastructure needed to run AI algorithms, the recruiting and training of qualified AI experts and the time and effort required to collect and preprocess the humongous amounts of data required to prepare these models.
Although AI/ML solutions can be utilized for processing organized data, it has trouble handling information that is ambiguous or confusing. Uncertain data entry and vague instructions might cause gaps in the output produced. In such situations, the AI solution’s capacity to perform is hampered by the lack of clarity.
AI-related machine learning algorithms are created to learn from massive datasets and spot patterns to provide outputs or make predictions. However, it becomes difficult for the model to create dependable patterns when the data or instructions presented to the AI are imprecise or uncertain, which can cause gaps in the output and reduce its general reliability.
Difficulty in Processing Low-quality Data
For AI systems performing data entry tasks, processing unstructured data is a substantial hurdle. For instance, because of the wide variety of handwriting styles and fonts, it might be challenging to comprehend handwritten notes or analyze information from photos. Identifying which of the data captured from handwritten documents is useful and which isn’t can also be difficult for an AI/ML solution.
Advanced-Data Entry Services
High-level data entry requirements might be challenging for AI, especially when they call for specialized expertise or a nuanced comprehension of the subject matter. AI systems can find it difficult to accurately handle specialized data, thus leading to unsatisfactory results.
For instance, effectively recording extensive medical records with intricate diagnoses and treatment plans necessitates a profound understanding of medical language and procedures.
Declining Accuracy with New Data Types
Most AI/ML solutions are trained to work with specific data formats. When faced with new data formats or modifications to existing data structures, they might face hurdles. For example, AI solutions might know how to perform data entry on Excel, but the business wants to perform it on Smartsheet. In this case, the system might either require human assistance or retraining of the model.
Effective data management is a prerequisite for ensuring the accuracy and usability of the data. Imagine having a database of shoppers who bought from your website a year ago. Will it still be relevant? Well, no. Therefore, it is paramount to keep updating and replenishing the data from time to time. It is where AI-based data entry systems might encounter limitations. While AI can automate the tasks, it lacks the potential to maintain the data throughout its lifecycle.
Humans-in-the-loop: The Benefits of AI-based Automation with Human Oversight
The concept of human-in-the-loop involves a collaborative approach where human intelligence is coupled with the power of AI systems. This approach leverages the strengths of both humans and AI, allowing them to complement each other’s capabilities. It leads to more accurate output.
Let’s understand how the concept of humans-in-the-loop applies to the data entry process.
Ensuring Compliance and Security
AI-based data entry tools offer efficiency but relying on them without human oversight can lead to regulatory non-compliance, security vulnerabilities, and false data issues. Regulations governing data usage constantly evolve, necessitating human supervision to ensure compliance. AI systems may also lack the ability to identify vulnerabilities, exposing them to potential exploitation. Integrating third-party tools poses security risks, demanding human assessment. Additionally, AI tools may not detect false data, affecting decision-making. Human involvement is essential to set data validation rules, assess risks, and maintain regulatory compliance. Combining the strengths of AI systems with human expertise ensures accurate, secure, and trustworthy data entry processes.
Performing Complex Tasks
AI systems can indeed perform repetitive tasks swiftly and accurately, but tasks requiring domain-specific knowledge and critical thinking -basically, an understanding of contextual nuance -is handled better by humans. For instance, humans might be able to handle complex data entry services for energy management systems for home appliances, which require jargon usage, technical terminology, and details. AI systems on the contrary may give results that are unsatisfactory in such a situation.
Dealing with Exceptions
Imprecise instructions and unclear or unstructured data may be difficult for AI systems to understand. In certain circumstances involving such data, humans may be required to intervene in order to assess the data’s importance, make sense of it, and precisely enter it. Due to the fact that exceptions and edge cases are carefully handled by humans, it reduces the likelihood of errors and inaccuracies.
Detection and Correction of Errors
AI/ML solutions can not provide 100% accurate outcomes. Humans are relatively more capable at finding data flaws and abnormalities, despite breakthroughs in AI systems. They can quickly fix any errors and identify discrepancies that AI could miss. Human monitoring is needed to enhance data quality overall and lessens the possibility of errors.
Managing the data
It is insufficient to just enter data in a predetermined format. Effective data management requires continual updating, organizing, and preserving data for relevance and accuracy. Humans are indispensable in monitoring ethical operations, system maintenance, data pipeline upkeep, and preparing/monitoring AI/ML training datasets. AI solutions alone fall short in these critical areas.
A Cost-effective Way of Integrating Human Intelligence with AI Processes – Outsource Data Entry Services
The data-entry outsourcing services market share is projected to grow by USD 552.63 million between 2021 and 2026 at a 6.1% CAGR. These compelling statistics highlight the critical role of data entry outsourcing for businesses, showcasing how incorporating humans-in-the-loop can significantly enhance operations.
Businesses can achieve accuracy and quality, ensuring that important data is error-free and dependable if they outsource data entry services. Data entry professionals utilize a streamlined process that not only saves time and effort but also money because it does away with the costs of keeping an internal team. Overall, hiring specialists to handle the tiresome and hard work of data entry allows firms to concentrate on their core competencies.
It would be right to say that data is the new oil that can fuel the growth of any business. To maintain and study the data, the data entry process is extremely crucial. A wonderful advancement, AI systems can indeed make the process of data entry more speedy and effective, but it cannot completely replace humans-in-loop. Adopting the strengths of both AI and human intelligence allows for enhanced efficiency, accuracy, and innovation while maintaining the crucial human touch required for intricate and context-driven data entry tasks.