How Outsourcing Data Annotation Can Help With Your Revenue Inflow

outsource data annotation

Businesses are adopting AI technology to automate decision-making and benefit from new business opportunities. Data labeling enables machines to gain an accurate understanding of real-world conditions and opens up opportunities for a wide variety of businesses and industries. Grand View Research estimates data labeling services industry revenues to touch a staggering figure of USD 8.22 billion by 2028. be USD 316 million in 2018. They expect the industry to grow to USD 1.6 billion by 2025 and register a 256.6% CAGR throughout the 2021-2028 forecast period. According to PwCMcKinsey, AI has the potential to deliver additional global economic activity of around $15.73 trillion by 2030.

What is Data Annotation?

In order to make machines more intelligent and similar to human brains, artificial intelligence requires data that is tagged or annotated appropriately so that the algorithms can recognize the relevant data and present it to the user. So, simply put, data labeling or data annotation is a process in which raw and unorganized data is refined by attaching correct labels that contextualize it and make it meaningful for the machines to learn and pick up from the database. 

What Is Data Annotation Used For? Why Is It Important?

Data annotation is a component of data processing that helps ML algorithms to identify information correctly. As technology continues to spread its reach far and wide, we are witnessing newer phenomena like smart devices, home automation, self-driven cars, etc. All these require clean and labeled data sets in order to function correctly. For example, in hi-tech self-driven cars, the software needs to know which image is a road, which one is a house, and who is a human. Without having a database that labels different images correctly, you can imagine what horrors such cars will cause. 

Indubitably, training data preparation has always been one of the most tedious tasks in the machine learning process.  Labeling unstructured data requires human help. It is an important step while creating training data sets. However, it is also time-consuming and difficult one. Hence, several organizations choose data annotation outsourcing while leveraging skilled resources at competitive pricing models. Though choosing the right partner and working with the external team(s) isn’t a cakewalk and comes with its own set of challenges, it comes with its own advantages and that’s what this article is about. 

Here we will outline some of the best practices that will help you in choosing the right one to outsource data annotation services for machine learning providers and optimizing your annotation partnerships. 

What Are The Benefits Of Data Annotation Outsourcing Services? 

Data Annotation Outsourcing benefits

Outsourcing data annotation services is a proven way for teams to boost productivity, decrease development time and stay ahead of the competition. Individuals, researchers and companies, are increasingly turning to data annotation companies as a viable solution to obtain both crowdsourced annotators and off-the-shelf annotation tools. Some factors that highlight the benefits that are driving organizations to outsource data annotation services are:

1. Access to experts

One of the biggest benefits of outsourcing data annotation services is that you can get hold of some experts hands who have been in the field for years and have a rich hands-on experience. The decision to hire such expert resources yourself can be a costly affair and you may not even end up finding as many as you want. But when you outsource data annotation to a reliable partner, you will have experts at your disposal working their magic on your data.

2. Refined error-free data

When you choose to outsource data annotation, you can be assured of receiving high-quality, refined data that will have no errors. As a team of experts will be handling your project who will solely dedicate their entire time and energy and also use the latest tools and software for data cleaning, you will get superior quality annotated data that will be ready to use.

3. On-time delivery

Timely delivery is another one of the data annotation outsourcing benefits. Imagine you have an important marketing campaign to plan and you need high-quality precise data for your chatbots. As we know data annotation is a time-consuming task, asking your in-house team may take a longer time to get your hands on the kind of annotated data that you require. But when you go for data labelling outsourcing, you can ask the team to deliver on the required date. Such data annotation service partners also allow you the liberty to size-up your team in case you have a higher requirement. 

4. Affordable rates

Data annotation outsourcing is one of the most cost-efficient ways to label your data. Do a little bit of research and you will be surprised to find out reliable and experienced annotation service providers at such reasonable rates. Most ones also give you the option to decide the size of the team that you will work with. So, whenever the workload is more, you can go for more resources and when it is less, you can size it down at your will. Isn’t that way cheaper than hiring full-time annotation experts for your organization.

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Tips That Will Help You Find The Apt Data Annotation Outsourcing Partner

Tips To Choose Data Annotation Outsourcing Partner

Now that we know the  benefits of outsourcing data annotation, let us find out a few tips that will help you lock in the right data labeling outsourcing partner for your requirements. 

Step 1: Define your goals

This may seem obvious, but it is important to know exactly what your needs and expectations are before commencing the search. Consider preparing an RFP (Request for Proposal) document or statement of work that defines the goals and expectations in terms of deliverables. In simple words, it outlines what is expected of your outsourcing partner. You can then utilize this to compare each vendor you speak with against the same criteria. There are many factors to consider when putting together your statement of work or RFP, including:

  • Detailed project overview
  • Project timeline
  • Data volume, and project budget

You can also include information regarding payment, quality, and customer support. It’s important to define your standards of quality and expectations to avoid any mishaps later. Answering these questions will be essential to equipping your team for the next step in the process.

Step 2: Evaluate Multiple Vendors

One of the biggest mistakes organizations make when opting to outsource data annotation for AI is to underestimate the need for skilled expertise. While data labeling may often seem like a simple task, it requires great attention to detail and a special set of skills to execute the project efficiently and accurately. With thousands of companies offering data annotation services like video & image annotation for machine learning, choosing ‘the one’ might be tricky. So how do you know which company or vendor would be best suited for your needs? 

Listed below are several factors to take into consideration when researching potential data labeling outsourcing service providers:

  • Ask for experience – Every company strives to prove it has ample years of industry experience. Testimonials and case studies allow you to get a closer look into the background, solutions, and outcomes.
  • Which tools & technology do they employ – When you outsource data annotation services, you get to pre-built data annotation tools. This eases the pressure on your team to create in-house tools from scratch. Make sure the vendor you choose optimizes your data annotation process while saving you both time and money. The best annotation tools are user-friendly, minimize human involvement, and maximize efficiency while maintaining data quality.
  • How good is their data security standard – Confidentiality is a major concern when outsourcing data labeling to a third-party. If you require secure annotation services, have a discussion with the company’s IT team to learn about their security protocols for sensitive data.
  • What is the quality of the data that you will get – The performance of your AI model is determined by data quality. Before deciding to partner with any company, ask them what quality control mechanisms they have in place to ensure the quality of the end product.
  • Do they offer affordable price packages – Choose an agency that refrains from quoting a price before they’ve had the chance to review your data, as the price can vary widely depending on the service or data type. The best feature about the decision to outsource data annotation is that such companies focus on your ROI. Hire a vendor that can honestly evaluate your project, the cost, and potential solutions in terms of ROI over time.

Step 3: Request a Proof-of-Concept

After you’ve narrowed down your choices, conduct a pilot or a sample project before jumping in headfirst. It is a bite-sized task that resembles a larger project.  Getting sample data helps in cases when a project is either complex, or you are not sure of the vendor’s capability to deliver. This allows you to tweak project parameters or guidelines before processing the full set of production data. The timeline for the proof-of-concept must be strict. The service provider’s delivery record and performance in the sample project will play a major role in the overall decision-making process.

Step 4: Iterate and Scale

Depending on how the pilot project goes, you’ll know whether to entrust them with your annotation project or carry on the hunt.  Your decision will be directly related to your project’s and business’s success. As you work and team up with your partner, make sure the annotation process is progressive and iterative. A proactive partner will work closely with you to improve project design over time while ensuring the tools, workforce, and methodologies provide the agility and flexibility you need to innovate.

Final Thoughts

Choosing the right data labeling outsourcing partner won’t intimidate you if you have clear goals and an understanding of what you need to achieve them. Make sure you carry each step with due dedication because when you outsource data annotation, the company you choose can either turn your idea into a successful project or an operational nightmare. Most importantly, selecting the best vendor from the outset should lead to your team receiving the highest possible quality for your crucial training data.

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More Related To Data Annotation Outsourcing

1. What are the different types of data annotation?

Types of Data Annotation

Some commonly done data annotation are:

  • Computer vision– here labeling experts label pictures, images, etc. to create a training data set. This dataset is then used to create a computer vision model that will be programmed to automatically pick out and label similar data.
  • Natural Language Processing– here experts manually identify data or parts of the data and tag them with defined labels in order to associate correct context to the data. Natural language processing is mostly used for sentiment analysis, entity recognition, etc.
  • Audio processing– here, experts identify different kinds of audios like the human voice, wildlife sounds, etc., and put them under a predefined format using the correct labels for machine learning purposes.

2. Can data annotation be done with an in-house team?

Unlabeled data can be easily procured at cheaper rates. Many organizations often assume that they will buy the unlabelled data and employ their in-house data experts to clean the data and annotate it. However, it is a massively time-consuming task that requires a great number of resources as well. Hence, it can drain out your resources, time, and money considerably. 

3. What should you look for while outsourcing data annotation services?

It is ideal that before finalizing a data annotation outsourcing partner, you should do a small homework about the company. Some important factors that you should keep in mind are the history of past clients, their track record in the industry, how robust is their data security and privacy guarantee, etc.

With Data Entry India, you do not have to worry about these factors as it is a reputed name in the industry, delivering excellence since 1999. Here you can be assured of high-quality results as we come with the following benefits:

  • Superior standards of data as we are an ISO 9001:2015 certified company for “Data Quality”
  • Improved data security as we are an ISO 27001:2013 certified company for “Information Security”
  • Timely committed delivery
  • Customized data annotation solution packages
  • Round the clock assistance
  • A dedicated project manager to ensure high-quality output
  • An assured accuracy of 99.95%

4. What tools companies use for data annotation?

Data annotation tools are cloud-based software that marks the different content in a set of data appropriately. Some commonly used data annotation tools are Labellmg, LabelMe, Fiji, Dataloop AI, etc.

5. Automated data annotation vs. human annotations: What is better?

Preparing a high quality training data calls for a good combination of data annotation tools and highly-skilled human resources. In any data set, if there is a small error, automation tools may fail to find it out and continue to repeat it. Hence, manual labeling by experienced human annotators remains crucial to spot out errors and deliver high-precision data sets. When you choose to outsource data annotation services, you get to enjoy highly-refined datasets labeled by the best annotators and double-checked by the latest tools. 

6. What types of data annotation can one outsource?

There are several kinds of data annotation services that are available for outsourcing. You can outsource image annotation, video annotation and text annotation services. All these services aim to help you identify the right inputs so that your brand can increase its business and maintain a spotless image too.

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