While it’s not always easy to turn raw data into smart data, there is one process that helps add vital bits of information to raw data – providing structure to data that is otherwise just noise to a supervised learning algorithm – data annotation. Ultimately, artificial intelligence can’t succeed without access to the right data. Feeding it the right information with a learnable ‘signal’ consistently added at a massive scale is going to drive constant improvement over time. That’s the power of data annotation. However, before you begin with any data annotation project, it’s important to consider the following questions. https://innodata.com/blog/5-questions-data-annotation/

1. 5QUESTIONS
TO ASK
BEFORE GETTING
STARTED WITH
DATA ANNOTATION

2. Annotation plays a crucial role in ensuring your AI and
machine learning projects are trained with the right
information to learn from. It provides the initial setup for
supplying a machine learning model with what it needs to
understand and discriminate against various inputs to
come up with accurate outputs.
By frequently feeding tagged and annotated datasets
through an algorithm, you’re able to establish a model that
can begin getting smarter over time. The more annotated
data you use to train the model, the smarter it becomes.
DATA
ANNOTATION

3. ANNOTATION IS THE
SECRET TO HACKING AI
• 80% of AI project time spent on data preparation*
• Companies spend 5X as much on internal data labeling than
with 3rd parties*
• Annotation and labeling is essential for training AI and machine learning; it’s
what makes them truly intelligent.
• Even small errors could prove to be disastrous, therefore human-annotated
data is essential
• Humans are simply better than computers at managing subjectivity,
understanding intent, and coping with ambiguity
*Cognilytica, 2019

4. ANNOTATION PROVIDES
GROUND TRUTH FOR AI
There are many different types of data annotation modalities,
depending on what kind of form the data is in:
SEQUENCING
Text or time series from
which there's a start (left
boundary) an end (right
boundary) and a label.
CATEGORIZATION
Binary classes,
multiple classes, one
label, multi-labels, flat
or hierarchic, otologic
SEGMENTATION
Find paragraph splits,
find an object in image,
find transitions between
speakers, between
topics, etc.
MAPPING
Language-to-language,
full text to summary,
question to answer, raw
data to normalized data

5. 5
QUESTIONS TO
ASK BEFORE
GETTING STARTED

6. 1 | What do you need to annotate?
• Text Documents
• Images
• Video
• Web Documents
• Audio Files
Annotation can be
applied to many types
of assets:

7. 2 | Is your annotation accurately
representative of a particular domain?
Before you start labeling data, you
should understand the domain
vocabulary, format and category of
the data you intend to use – also
known as building an ontology.
• Financial Services
• Pharma
• Healthcare
• Legal
• Regulation & Compliance
Industries with unique
rules and regulations
for data:

8. 3 | How much data do you need for your
AI/ML initiatives?
The likely answer is as much data as possible,
but in some instances certain benchmarks can
be established based on the specific need (e.g.
the past 10 years of SEC regulatory data).

9. 4 | Should you outsource or
annotate in-house?
Building the necessary annotation tools often
require more work than some ML projects. But for
many companies, security is an issue, so there is
often hesitation to release data. But many
companies have privacy and security procedures
in place to address these concerns.

10. 5 | Do you need your annotators to
be subject matter experts?
Depending on the complexity of the data you are
annotating, it is vital to have the right expert
handle annotations. While several companies use
the crowd for basic annotations, more complex
data requires specialized skills to ensure
accuracy.

11. Check Out 9 Data Annotation
Best Practices from Leading
Companies
https://info.innodata.com/accelerate-ebook
Nine best practices from industry
leading data-driven companies
ACCELERATE AI WITH
ANNOTATED DATA