Evaluating The Benefits And Drawbacks Of Automated Vs. Manual Data Labelling

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Evaluating The Benefits And Drawbacks Of
Automated Vs. Manual Data Labelling
According to a study by Tractica, the Artificial
Intelligence (AI) market is expected to grow by
over 100 billion by 2025. From self-driving cars
to smart home assistants, customers are
increasingly demanding products that run on AI
technology. Moreover, AI technologies are
becoming more accurate, because they have been
fed and trained on carefully labeled data.
Unfortunately, a recent report published by
Cognylitica confirms that data wrangling consumes
over 80 of the time spent on most AI
projects. How does data labeling help? In simple
language, it makes data and other digital
content recognizable to the machines that are
trained through algorithms to learn and utilize
the information for making decisions and
predictions or executing tasks. It assumes more
importance as businesses invest in AI
technologies. According to Global Market
Insights, the market size of data labeling tools
exceeded 1 billion in 2020 and is projected to
grow at an annual rate of over 30 between 2021
and 2027.
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• When done correctly, data labeling can deliver
  exceptional market insights, drive sales, and
  help you reduce costs. Because data labeling can
  consume so much time and money, automation is
  developed and deployed as often as possible.
• However, there are times when data labeling must
  be handled manually. Knowing how and when to use
  each approach is vital both in terms of
  accelerating your effort and minimizing your
  costs. Lets take a look at the pros and cons of
  both Manual and Automated data labeling.
• Manual Data Labeling
• Manual data labeling is performed by a team of
  data experts who are assigned the task of
  identifying objects of interest and adding
  metadata to these objects manually. Typically,
  these experts examine hundreds of thousands of
  images and objects to construct comprehensive
  and quality AI training data for your model.
  Seems labor-intensive and time-consuming, right?
  Lets discuss the pros and cons of manual data
  labeling.
• Pros Of Manual Data Labeling
• More Accurate Results
• For any business, human annotators are your go-to
  resource when it comes to precision and quality
  in labeling data. These experts have several
  years of experience in tagging data and
  understanding the requirements of different
  machine-learning models.
• They can also identify anomalies that are
  otherwise missed by automated processes. Whether
  you are building computer vision or natural
  language processing (NLP) models, labeled
  features will be more accurate when they are
  consistent with real-world conditions.

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• Easier To Customize
• Human experts in data labeling and annotation are
  more in tune with your evolving business
  requirements and objectives. As a result, they
  have the flexibility to incorporate changes that
  are tuned to your end users needs, product
  changes, or modifications in data models. This
  flexibility allows them to quickly shift gears
  and tackle data annotation projects corresponding
  to your specific business needs.
• Better Data Quality Assurance
• Data quality is the most critical component when
  it comes to the accuracy of data labeling.
  Well-trained individual data labelers review the
  quality of your labels and release only the
  approved objects for analysis. This always
  ensures quality and precision in model training
  datasets. For example, imagine the task of
  labeling the various components of a car. Manual
  labeling tools are better equipped to capture
  the edge cases of the object that would be missed
  by automated labeling tools.
• Stronger Data Security
• With in-house data labeling, organizations are in
  control of their data, thus maximizing data
  security. With a correct and efficient security
  system and protocol, the risk of data leakage is
  significantly lower for your business.
• Cons Of Manual Data Labeling
• Slower
• Labeling big datasets takes time and effort when
  your enterprise relies on human experts. This is
  one of the major constraints preventing companies
  from labeling data manually.

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• For example, lets say your company wanted to do
  a sentiment analysis of your customers reviews
  on social media. Now imagine your company wants
  to use 90,000 reviews to build an accurate data
  model. If a labeler takes 30 seconds to annotate
  each comment, they will spend 750 hours
  completing the task.
• More Expensive
• As data science and artificial intelligence are
  some of the most in-demand industry skills,
  experienced professionals in data labeling would
  be highly-paid resources. At times, businesses
  need to spend an incredible amount of money and
  resources hiring and training experts to execute
  relatively simple annotation tasks. Moreover,
  maintaining a small team of data labeling
  professionals in-house can be prohibitively
  expensive for most organizations.
• Automated Data Labeling
• Automated data labeling simply refers to labeling
  not performed by people. Machine learning models
  are self-trained to recognize which labels to
  attach to which data points. The model needs to
  self-learn the labeling rules for objects and
  data points.
• Machine learning algorithms allow these models to
  sense, reason, act, and adapt by experience and
  as much as possible, mimic the human brain. For
  instance, for any unstructured customer data or
  content, automated data labeling can be deployed
  to identify segments of customers with similar
  combinations of attributes and treat them
  similarly in marketing campaigns.

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• Pros Of Automated Data Labeling
• Faster And Less Expensive
• Because there is little (or no) human
  intervention in automated data labeling,
  businesses save significant operational costs and
  time they would otherwise spend to hire
  technical experts or create an in-house team.
• More Precise Learning And Improvement
• Using active learning, a semi-supervised
  approach, automated data labeling provides
  highly accurate data annotation. Active learning
  requires the labeler to select an initial sample
  from unlabeled data and then label more data
  based on the results. In addition, automation
  can be leveraged to continually enhance and
  improve your manual data labeling processes.
• Cons Of Automated Data Labeling
• Problems With Labeling Unseen Data
• When you use automated labeling exclusively,
  machine learning models are trained according to
  available sample datasets. Objects and data
  points that are external to the sample set might
  not be labeled accurately. Human experts are
  capable of addressing such untrained or
  unexpected cases.

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• Probability Of Future Errors
• If a data point is incorrectly labeled, future
  errors tend to occur and go unnoticed, because
  the machine learning model is being trained
  according to the existing incorrect results.
  This will adversely impact the performance of
  downstream processes and the accuracy of
  predictive models.
• So, What Does This All Mean For You?
• In 2020, around 59 zettabytes of data were
  generated. Data labeling will assume more
  importance as organizations continue to leverage
  AI technologies to extract value from all of
  those information assets. To optimize your
  results, we suggest using a blend of both manual
  and automated data labeling approaches depending
  on the urgency, scale, and potential business
  impact of the specific business process.
• As discussed, both approaches provide important
  benefits. Finding it difficult to manage and
  extract business value from an enormous amount of
  data? At EnFuse Solutions, we offer end-to-end
  services in data labeling, tagging, and
  annotations. As a solution provider, we are
  committed to optimizing your data quality for
  training your AI and ML models, ultimately
  improving your business results. Want to learn
  more about how we can help you succeed? Contact
  us today.
• Read more about Data Labeling here When Should
  You Partner With A Data Labeling Company?