The Essential Skills That Data Annotation Experts Must Possess

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The Essential Skills That Data Annotation Experts
Must Possess
With the fast-paced development of the Artificial
Intelligence (AI) and Machine Learning (ML)
domain of technologies, the field of data
annotation is gaining more importance. The
global market for AI- and ML-relevant data
preparation solutions is expected to grow to
3.5 billion by the end of 2024.
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77 of the devices that are in use presently
utilize ML in some form or other. From virtual
personal assistants like Apple Siri, Amazon
Alexa, Google to social media platforms like
Facebook, the use of AI and ML technologies is
projected to increase over the coming years.
From healthcare and automotive to the IT and
retail sectors, these technologies are being used
across sectors. Data annotation and data
labeling play a critical role in preparing the
data to train the AI/ ML models. To keep up
with this growing demand, business enterprises
across industry domains are looking for data
annotation experts or providers who can think
strategically and help reap the benefits of AI
and ML initiatives. The Need For Data
Annotators Data is now emerging as the backbone
of modern customer experiences. As enterprises
gather more insights into their customers, AI is
making the collected data actionable. To deliver
actionable insights, the smart algorithms need to
be trained on data. This is where data
annotators (or labelers) can help. For instance,
even the most advanced computer is unable to
differentiate a man from a woman using a
picture. It requires the right algorithm along
with supervised training to execute tasks that
are deemed easy for the human brain. Data
annotators make it easy by labeling content such
as text, images, audio, and videos so that the
machine learning models can recognize those and
use them to make useful predictions. However,
data annotation is not as easy as it sounds. It
requires several skills, domain expertise, and
patience to be an excellent data annotator. We
will discuss the eight most important skills that
data annotators need to possess
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• An Eye For Detail
• Data annotators must pay attention to the finest
  details. Incorrect annotation can reduce the
  data quality and jeopardize the entire ML
  algorithm. Be it text or images, annotators must
  highlight specific data pieces that can be
  interpreted easily by machine algorithms. For
  example, annotating the specific legal clauses
  and context in a court ruling statement.
• Further, data labeling for image recognition also
  takes observation skills and attention to
  detail. For example, a data labeler must know
  where to draw the bounding box around only the
  part of the image that has the characteristics
  described in the label (for example, exact facial
  features for a face recognition model).
  Including too much (or too little) of the image
  could result in inaccurate data model outputs.
• Expertise In Working With Large Volumes Of Data
• Unstructured data makes up more than 80 of
  enterprise data, and it is growing at the rate
  of 55-65 each year. In the absence of tools to
  analyze these massive data volumes,
  organizations are just left with vast amounts of
  valuable data on the business intelligence
  table.
• Further, to be accurate, AI and ML models also
  need large volumes of training data. On their
  part, data annotators must have the skills to
  handle and process massive volumes of structured
  and unstructured data without compromising its
  quality. With a massive amount of unlabeled data,
  data labeling is a high-volume task and goes a
  long way in data preparation and preprocessing
  for building AI models.
• Ability To Deliver High-Quality And Consistent
  Data Output Skilled data annotators who can
  deliver high-quality training data can help
  in developing accurate AI and ML algorithms.
  Be it an image or text annotation, high-quality
  data is an absolute must for accurate model
  outputs.

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• Essentially, the quality of data is determined by
  the accuracy, consistency, and integrity of data
  annotation experts.
• For example, a computer vision system trained for
  autonomous vehicles using poor-quality images of
  mislabeled road lanes can lead to devastating
  results. Hence, the ability to deliver accurate
  and consistent output is critical for data
  annotators.
• Managing Data Complexity
• According to TechJury, data creation is projected
  to grow to over 180 zettabytes by the year 2025.
  This means more data types and sources are being
  introduced. The complexity of data indicates the
  level of difficulty enterprises face when trying
  to translate them into business value. Data
  annotators must be able to handle complex
  data-related operations and work with more data
  types.
• For example, image recognition systems often
  require bounding boxes drawn around specific
  objects, while product recommendation and
  sentiment analysis systems require natural
  language processing skills along with a cultural
  context. Essentially, data annotators should be
  skilled enough to take into account the
  complexity of the task and the size of the
  project.
• Strict Adherence To Project Timelines
• Data annotation is a collaborative effort that
  includes multiple stakeholders. Non-adherence to
  project timelines can delay the overall project
  and increase costs. On the other hand, a limited
  timeline may impact the output quality of the
  labeled data. Project managers in charge of the
  data annotation effort need to carefully assess
  the timelines based on the involved datasets,
  available workforce, and the overall complexity.

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• Domain Expertise
• Ontologies (or the understanding of the entities
  that exist for a particular industry domain) are
  a crucial part of any ML project. Do business
  enterprises need to have subject matter experts
  when it comes to efficient annotation work? That
  is determined by the complexity of the data
  project.
• Data annotators can deliver better data quality
  with proper domain expertise. This includes
  high-demand industry domains such as security,
  defense-related satellite image analysis, and
  medical diagnosis (that include potentially
  life-threatening conditions).
• Technology Knowledge
• Essentially, this means how oriented are data
  annotation professionals at learning new
  technologies and software tools. Computer
  programming skills arent mandatory, although
  it would be a nice-to-have skill in any data
  annotation project. Data annotators also need to
  be adept at learning about machine learning
  models, to deliver model-ready data that can be
  processed without any delay.
• Perseverance
• As data labeling is a time-consuming process, it
  requires data annotators to have perseverance in
  data iteration and features as they train and
  tune the models to improve data quality and
  model performance. With growing data complexity
  and volume, data labeling is likely to become
  more labor-intensive.
• For example, video annotation is especially
  labor-intensive, with each hour of video data
  collected consuming about 800 human hours to
  annotate. Effectively, a data annotator should
  be able to sit for long hours and pay attention
  to whats happening on the screen, without being
  easily distracted and making mistakes.

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Conclusion The number of data annotators is
expected to increase in the upcoming years with
the rise of AI and machine learning. Several
large corporations like IBM, Google, and
Facebook are already recruiting new people for
data labeling. Its time you also hop on to it
and look for someone who enjoys technology and is
eager to learn new tools and techniques of data
labeling. At EnFuse Solutions, our team of data
annotation experts adheres to the best data
security standards and timelines to guarantee
speed, high quality, and security for your data
projects. Want to reap maximum gains on AI
initiatives? Get in touch with us now. Read
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