Your business’s future is closely related to how well it can adapt Artificial Intelligence. This is because businesses of all backgrounds are relentlessly pursuing this technology, with the global AI market expected to touch US$641.3 Billion by 2028. If you don’t follow suit, you’ll lose not only your market edge but also your market share. Going with AI gives you numerous advantages that will increase both of those crucial factors. And the way to adopt AI quickly, accurately, and cost-effectively is through data annotation. 

Data annotation (or labeling) refers to defining a dataset with labels and feeding it to an AI algorithm. Through annotation, the AI model gains an understanding of its environment and improves. But the process is complex and requires a lot of effort and resources. Enterprises often outsource data annotation services to a professional agency to overcome those complexities while getting the desired results. 

While outsourcing helps you get through many development bottlenecks, some pain points affect data annotation that you should be aware of. Read on to learn about them and the appropriate solutions that can help you overcome them. 

The 5 Data Annotation Pain Points and Their Solutions

  • Insufficient Data Samples

The lack of intuition in computers means machines can’t accurately postulate the complete version of the target data in a sample or recognize it under different conditions. For example, it can’t recognize a tree in evening lighting conditions if it’s only been fed examples of a tree under morning lighting conditions. 

So,  data labeling services professionals should have as many data samples at their disposal as possible to make the annotation process a success. However, this is not always the case. If there aren’t enough samples, the entire process may fail since the required accuracy can’t be achieved. 

Such instances of sample deficiency are likely to occur when models are being developed for niche applications where present data relating to them is limited. The user here is forced to choose between going with a flawed AI model for their purpose or delaying adoption, both of which will adversely affect them. 

Solutions

Opt for multiple data sample providers/sources that match your development requirements. Discuss the same with your outsourcing services provider. There are many datasets providers offering samples in the millions for every data annotation type. 

See if you can be flexible about your project requirements so that your dataset can be expanded beyond a limited range. In the case of niche applications, provide as much data from your end as you can and use a data scraping service to gather more. 

  • Inadequate Data Security and Privacy

Enterprise data is increasingly being targeted by cybercriminals due to the value it holds and poor data security/privacy measures. 2022’s third quarter alone has seen a 28% rise in them compared to the same period in 2021. Your company’s data is also vulnerable, especially while undergoing a process like data annotation. 

The risk to your data security and privacy varies depending on your choice of the outsourcing agency, the application for which the annotation is happening, your data security and privacy measures, and other factors. 

If you haven’t established a clear data-sharing policy with your outsourcing agency, and it isn’t strictly enforced, then you up the risk factor. It also goes up if you haven’t reviewed their policy around this issue, including contingency plans. 

In some cases, you may have to disclose confidential data for annotation purposes to the agency. You have to do this with a certain amount of trust and good faith in them that they’ll protect your data, regardless of pertinent clauses in your contract. 

Solutions

Do a thorough background check always before hiring your preferred agency. Enquire about every last detail regarding the data labeling services provider’s data security and privacy policy and certifications. Decide on your data security and privacy practices, including your data sharing policy, before outsourcing it. 

Use the latest security software and don’t communicate with them through random channels. In case of confidentiality requirements, have a detailed discussion and understand how they’ll go about it. Always keep yourself updated about their status in this regard and your data security practices. 

  • Compliance Issues

Data is a regulated commodity now, and the regulatory intensity increases depending on the type of data. Data labeling services agencies tend to be well-versed with all applicable data regulations and work accordingly. However, there are instances where compliance conflicts variable arise. This is likely to be the case if you’re developing an AI model that you wish to apply across markets. 

Every country has its set of data regulations that you must adhere to if you wish to operate in it. Variations occur in data security and privacy regulations, data transfer clauses (particularly international transfers), data storage, etc. So, annotations done for one market may not be legal in another. The problem gets compounded when it involves data relating to your international client/source from such a country. 

Solutions

Always be aware of all applicable regulations regarding your project and the associated task. Consult a legal professional before the commencement of your project. If the outsourcing agency has one or has data annotation experts who are familiar with the regulations, then go along with their recommendations. Reannotate according to a country’s regulations if necessary. 

  • Lack of Consistency In Quality and Delivery Time

Time-to-market is one of the most important factors that directly impact your sales figures. When outsourcing a complex task like annotation, it becomes all the more critical that the required deliverables are available on schedule. Along with that, the output quality should also be according to the standards set by you and prevalent across the industry. However, this may be different from what you get. 

Variations in annotation process output quality and delivery times can occur due to various reasons. Some of them can be unforeseen, such as the recent COVID pandemic. Others may be due to unprecedented internal issues on the side of the data labeling services provider. 

In other instances, it could be due to systemic problems that may have escaped your view while doing your initial background checks to select an outsourcing partner. Either way, a delay for any reason, including lack of initial output quality, will throw your plans off balance. And you’re likely to expend more resources to compensate. 

Solutions

Be thorough with your selection of the annotation outsourcing agency. Run through a shortlist of agencies by asking questions about their approach to the process. Learn about the tools they use along with the number and quality of their talent. Inform the agency about your standards and schedule at the start of the project. 

Monitor the same with regular meetings and discuss any issues to find a mutual solution. Also, inform them about changes to your data annotation requirements early enough so that they’ll have the time to change and maintain their quality and output time.

  • Project Management Issues At Your End

Annotation is a sophisticated and tedious process that requires technical know-how to execute correctly. If your company isn’t into AI and related services, and you’re new to it, you will likely face project management issues. The problem starts with a lack of knowledge about the process and what it entails. You will likely need a consultant to get you up to speed on it. 

Until then, you won’t be able to make the necessary resource allotments for the process. This could throw your operation schedule off, causing delays and losses, especially if it’s unplanned. Another problem is deciding between outsourcing it to a data labeling services provider and conducting it in-house. While the former is the better option due to its many advantages, you may choose otherwise. Then, you should be able to manage all project-related tasks in-house. 

This is likely to prove a costly affair on many fronts. Personnel hiring, training, maintenance, office space, equipment, software, and other expenses will add up and balloon your budget. This induces your project management with more uncertainties you may need help to handle. 

Or, you may need help finding an outsourcing partner that can meet your specific requirements like budget, quality, types of services, deadlines, etc. The possibilities are many, and you should account for all of them. 

Solutions

Plan for the project ahead. Ask the data annotation outsourcing agency or a consultant about the process and build a strategy around it. Go for the in-house option only if you have no other choice. Use project management software and be certain about the personnel you’ll be using too. 

Conclusion

Automation is pushing the boundaries of all types of businesses. You could push your business’s limits, if you embrace it in every possible way via data annotation. By watching out for the above-mentioned pain points and applying the solutions, you can experience a surge in efficiency and performance that can catapult your business to achieve its objectives.