When we refer to “quality training data,” we mean that the labels must be both accurate and
consistent. Accuracy is the degree to which a label conforms to reality. The degree of agreement
between several annotations on diverse training objects is known as consistency.

Emphasizing the fundamental law with training data for projects involving the creation of artificial
intelligence and machine learning by mentioning this. Poor-quality training datasets that are
provided to the AI/ML model might cause a variety of operational issues.

The ability of autonomous vehicles to operate on public roads, depends on the training data. The AI
model is easily capable of mistaking people for objects or vice versa when given low-quality training
data. Poor training datasets can lead to significant accident risks in either case, which is the last thing
that makers of autonomous vehicles would want for their projects.

Data labeling quality verification must be a part of the data processing process for high-quality
training data. You will need knowledgeable annotators to correctly label the data you intend to
employ with your algorithm in order to produce high-quality data.

Here’s how to ensure consistency in Data Labeling process

Rigorous data profiling and control of incoming data

In most cases, bad data comes from data receiving. In an organization, the data usually comes from
other sources outside the control of the company or department. It could be the data sent from
another organization, or, in many cases, collected by third-party software. Therefore, its data quality
cannot be guaranteed, and a rigorous data quality control of incoming data is perhaps the most
important aspect among all data quality control tasks.

Examining the following aspects of the data:

• Data format and data patterns
• Data consistency on each record
• Data value distributions and abnormalies
• Completeness of the data
  

• Designing the data pipeline carefully to prevent redundant data
  Duplicate data occurs when all or a portion of the data is produced from the same data source using
  the same logic, but by separate individuals or teams most likely for various later uses. A data pipeline
  must be precisely specified and properly planned in areas such as data assets, data modeling,
  business rules, and architecture in order for an organization to prevent this from happening.
  Additionally, effective communication is required to encourage and enforce data sharing throughout
  the company, which will increase productivity overall and minimize any possible problems with data
  quality brought on by data duplication.

• Accurate Data Collection Requirements
  

Delivering data to clients and users for the purposes for which it is intended is a crucial component
of having good data quality.

It is difficult to show the data effectively. It takes careful data collection, analysis, and
communication to truly understand what a client is searching for.
The need should include all data situations and conditions; if any dependencies or conditions are not
examined and recorded, the requirement is deemed to be lacking.
Another crucial element that should be upheld by the Data Governance Committee is the
requirement’s clear documentation, which should be accessible and easy to share.
Another crucial element is having clear requirements documentation that is accessible and
shareable.

Compliance with Data Integrity

Not all datasets are able to reside in a single database system when the volume of data increases
along with the number of data sources and deliverables. Therefore, applications and processes that
are defined by best practices for data governance and integrated into the design for implementation
must be used to ensure the referential integrity of the data.

Data pipelines with Data Lineage traceability integrated

When a data pipeline is well-designed, the complexity of the system or the amount of data should
not affect how long it takes to diagnose a problem. Without the data lineage traceability integrated
into the pipeline, it can take hours or days to identify the root cause of a data problem.

Aside from data quality control programs for the data delivered both internally and externally, good
data quality demands disciplined data governance, strict management of incoming data, accurate
requirement gathering, thorough regression testing for change management, and careful design of
data pipelines.

Boost Machine Learning Data Quality with Data Labeler

Maintaining consistency, correctness, and integrity throughout your training data can be logistically
feasible or dead simple.

What distinguishes them? Your data labeling tool will determine everything. Data Labeler makes it
simple to assess data quality at scale thanks to features like confidence-marking and consensus as
well as defined user roles. Contact us to know more!