When Should You Collaborate With A Data Labeling Company?

1
When Should You Collaborate With A Data Labeling
Company?

• Be it Artificial Intelligence (AI) or Machine
  Learning (ML), data quality is critical for the
  successful implementation of any data-based
  model or project. Most businesses are
  adopting AI and ML technologies to automate
  their decision-making and business processes and
  nearly 80 of the time invested in such programs
  is spent on data-related tasks such as data
  preparation or training datasets for algorithms.
• This includes the process of data labeling or
  annotation. According to a recent McKinsey
  article, data labeling or annotation is among the
  leading challenges for the successful adoption of
  AI-related technologies. The global market for
  data labeling and annotation services is expected
  to reach 5.5 billion by 2026.

2

• So, what exactly is data labeling and is now
  the right time for your businesses to partner
  with a data labeling service provider?
• What Is Data Labeling And What Are Its
  Applications?
• When it comes to supervised learning, ML
  algorithms self-learn from labeled data (or data
  tagged with labels). Data Labeling is the process
  of preparing tagged datasets specifically for use
  in machine learning. In other words, data
  labeling is an integral part of the data
  preparation process. For example, data labeling
  for a facial recognition model requires the
  tagging (or labeling) of specific features of
  your face like eyes and nose.
• For ML-based models, data labeling is required in
  the following stages
• Initial training of the data model enables it to
  infer the desired output (for example, eye
  color) from the provided input (for example, a
  face image).
• Continuous improvement, where any errors in the
  model output can be corrected by feeding it back
  into the ML model to improve its accuracy and
  performance.
• When Should You Partner With A Data
  Labeling Service Provider?
• Any business that has invested heavily in AI and
  ML technologies needs to focus on the process of
  data labeling to optimize its data quality.
  Poorly labeled and low-quality datasets can
  result in inefficient operations and loss of
  business. Poor labeling can also pose major
  safety concerns that can derail an entire
  technology project.

3

• Data Labeling Can Be Done
• In-house
• By freelancers
• With the help of a holistic, end-to-end service
  provider
• Partnering with a holistic, end-to-end service
  provider skilled in data tagging, labeling, and
  annotation services improves your likelihood of
  sustainable success. Having a data labeling
  partner boosts productivity and accelerates your
  overall development timeline. Additionally, data
  annotation service providers have the
  comprehensive expertise and technology to meet
  all your data requirements.
• It Is Advisable To Work With A Data Labeling
  Partner When
• The success of your process depends upon having
  high-quality data
• You dont have an in-house team with
  data-labeling expertise
• You have an urgent need for properly annotated
  data
• You are required to follow industry best
  practices and exhaustive quality assurance
• So, once youve decided to work with a data
  labeling partner, how do you go about selecting
  the right one?
• How To Select The Right Data Labeling Partner

4

• Relevant Industry Experience
• While every solution provider claims to have
  extensive industry experience, that may not
  always be the case. Take a deeper look at their
  experience in data labeling through client
  testimonials and case studies. An experienced
  service provider will be able to guide you
  through the initial design phase and
  specifications regarding data labeling specific
  to your industry. If they cant, buyers beware.
• Data Quality
• As mentioned before, the success of your AI or ML
  programs is dependent upon data quality. Your
  service provider must be able to detail the
  processes and mechanisms they use to optimize
  data quality (e.g., double-pass annotations to
  improve data accuracy).
• Data Security
• Data labeling services require you to share your
  sensitive data with a third-party vendor, which
  can lead to confidentiality concerns. Be sure to
  find out what security protocols service
  providers use to safeguard your data.
• Types Of Data Labeling Services
• Broadly, data labeling services are segmented as
  Text labeling (including tagging human sentiments
  like happiness and anger), Image labeling (with
  techniques like bounding boxes and 3-D cuboids),
  and Audio-Video labeling. Your service provider
  should offer each of these labeling services to
  help improve the overall data model.

5
5. Tools And Technology Technology can play a
key role in improving data accuracy or reducing
manual labeling work. For example, labeling tools
can preprocess unstructured data using ML models
and labeling data partially. Data labeling and
annotation tools are constantly evolving. Take
the time to understand which tools and
innovations your potential partner has
implemented and how they are adapting to keep
pace with future disruptive technology. Data
Labeling - The Critical Building Block Of
AI And ML Programs As outlined in this
article, optimizing data quality is essential for
any business to maximize the value of their
investments in their AI and ML programs.
Partnering with the right service provider will
ensure you harness the true potential of your
data to effectively scale your business and
accelerate growth while mitigating risk. 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. Want to learn more about how we can help
you succeed? Contact us today. Read more
Automated vs. Manual Data Labeling Evaluating
Pros and Cons