The business landscape played a huge role behind the global artificial intelligence (AI) growth by 154% in 2019. Such is the popularity and demand of AI in today’s world and it’s all driven by the thirst to bring about a competitive edge. Industries like retail, healthcare, transportation, finance and technology have shown immense interest in introducing AI and have greatly inspired other industries to try their hand in this field by experimenting with use cases which can cut cost and enhance profits.
AI has been playing a key role in various aspects like from optimizing digital supply chain operations for manufacturing to upgrading diagnoses of patients in healthcare.
Be that as it may lose its reliability as well as functionality if it is not built upon the foundation of accurate, diverse, consistent and valid data.
To ensure the accuracy of initial database, organisations turn towards manual data entry to improve the chances of successful results. As the experiments and applications gradually peak, data entry enjoys more and more importance as it provides the resources to implement and harbour these intelligent systems and solutions.
The logistics companies, financial services, legal, and real estate are such organizations known to be heavily dependent on hard copy documentation have shown immense pursuit in manual data entry to facilitate channel to intelligent automation.
Significance of Data Entry in AI
- Data Cleansing – The importance of data cleansing in Artificial Intelligence algorithm development can be proven by the fact that data scientists choose to spend as much as 80% of their time to cleanse the data. The data of a company, either big or small, is stored in warehouses, and it is usually raw meaning that it may contain redundancies, or even duplicate data. Therefore, manual data entry comes to the aid in organizing, cleaning and defining a systematic order to originate AI driven results that will elucidate a certain business complication.
- Classification – In AI, classification means more than just grouping despite of what many people claim. Artificial Intelligence’s manual data classification’s cynosure is output and subdivides into numbers, actions, action sequences, categories, waveforms and movements. Undeniably this is quite exigent for building AI use cases and with its necessity comes the intricacy to actually perform this task.
- Performance Metrics – To determine whether the outcome of a use case is worth the effort or not, performance metrics of a use case are resolved before it can be promoted to initial discovery stages. The premium data entry experts customarily carry out the task of data evaluation which aids system developers ascertain if the data available is enough, trustable and most importantly diverse. Data evaluation also function as advisors in various Artificial Intelligence projects scenarios.
Challenges Faced During Current Data Entry Process
Till now it must have become clear to you that data entry is the process of converting the data from its original physical state to a brand new digital state. This practice of entering the data manually has the tendency of becoming tricky and mind-numbing quickly which can lead to some common issues such as –
- Cost Expansion – The employees have to be properly trained before they can efficiently perform manual data entry which can cost money and resources.
- Time Consuming – To manually enter a company’s most data if not all will obviously take time no matter how many employees are assigned to this task.
- Increased Faults – The physical data can be misinterpreted, misread the information which can lead to drastic outcomes.
