Why does data labeling make sense in business?
Every year we are increasingly moving our businesses online in the cloud. We gain increasing amount of data from each operation and task. The data can be transformed in machine learning systems to draw the right conclusions and to execute the proper strategic actions. How to do it effectively? Arrange, organize, and above all label efficiently the data to streamline processes and focus on increasing productivity.
Each AI system is built on three main layers:
- Proper system training.
A large, homogenous and significant data set is crucial for the operation of any artificial intelligence system. To obtain significant results, it is important that all data is accurately marked.
Labelling the original data set provides the system with real data that has been clearly labelled and transformed into information. This process reduces the information noise and ensures the proper real life semantic context. Without a properly trained annotator and consistent marking, the system will achieve a poor result.
Professional data labelling leads to a faster system convergence to the desired results. This significantly reduces calculations and training time. The system is much faster, ready to operate, and more importantly, it is robust enough to work in a real environment with real data.
No algorithm is yet intelligent enough to compensate for bad labelling. Incorrectly labelled text data leads to a longer process to obtain an extremely low quality result that will most likely be unusable .
An example of a data text problem:
If we label the invoices from the accounting department and label the tax amount field incorrectly, the invoice may be incorrectly registered. In this case, it will be necessary to re-process all the invoices or to risk to pay the fines to the Tax Office, both options will have a significant financial impact on the company’s expenses.
In case of image data, in case of poor labelling there will be significant problems for the object detection and the semantic segmentation.
Example of the object detection problem:
If we label images that will be used to verify items on an image, then the system must be labelled in great detail. For example if the images contain satellite pictures of trees to count the number of trees per hectare for agriculture incentives, if the system receives incomplete data or it is marked carelessly, such verification will produce unreliable results, that will either result in a lower count with a loss of funds for farmers or in an excessive count with the high risk of fines.
Detailed and appropriate data labelling is the foundation of a correct algorithm’s training. With a strong foundation the system will provide reliable and consistent results that translate into increased productivity and significant cost reductions.