Data Annotation for ML Projects – 5 Most Effective Ways to Label Data
Process automation is not the only important aspect of any data processing project, if several other benefits such as improved process control and efficiency are neglected.
Several outsourcing service providers use business process management, workload balancing and productivity reporting to manage various tasks within any data processing activity. But none of these need any sophisticated data processing technology, nor are any significant efforts required to fine-tune the processed results. Companies can enjoy the efficiencies gained through improved throughputs and improved knowledge, to find out process bottlenecks and how quickly to address them.
Arguing over that, a company can benefit merely with improved business processes, in a scenario where unrealistic customer expectations of 99.9% accuracy are increasing.
Since last few years the shift of unrealistic expectations to automated documents processing is on advent. Data processing service providers are willingly trying new technologies to resolve challenges, in a hope that something somehow will improve. However; the reality remains that there is no technology out there for document classification or data extraction that delivers such high levels of success.
With our experience of more than 25 years, we vouch that both are important. YES, both accuracy and efficiency are important. Let’s understand that efficiency is a byproduct of accuracy. It can be attained through the automation of manual document-oriented processes.
Example: An organization with staff of 10 people to manage sorting and separation of incoming business documents such as Affidavits, Agreements, Deed of Trust, Power of Attorney, Mortgage, Foreclosure, Discharge and many more. Inclusion of activities like scanning and document preparation workflow will certainly reduce unwarranted slowdowns of bottlenecks. However; it is possible to gain 50% or more of the benefits only if some of the actual activities done by this team of 10 members are automated.
Expert execution of document auto-classification, such as assigning and sorting documents, reduces by 50% or more by some basic tuning. This reduction in the workload enables existing team members to be assigned with task of handling expectations and quality control that are critical to reduce errors. But the sad part is that, it is often neglected due to lack of additional resources. This is a juncture where we do not advocate reducing the workload, however; achieving 50% is more than enough to attain prominent improvements. The point here is to prove that accuracy of data processing team must be extremely high and really close to or better than what a person or team earlier used to deliver. Said that, the accuracy only needs to address 50% of the incoming document workload to attain significant automation and cost savings.
The accuracy of data processing, including data extraction or data recognition or data entry for that matter, is extremely important and projects success necessitates implementing this accuracy to a percentage of the document based information. While it is always an objective to increase the percentage of documents that are automated, starting with lower percentage will work as a small step towards gaining bigger savings, efficiencies and improvements, which most of the organizations crave for.
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