Today the Artificial Intelligence industry is full of opportunities that every business wants to leverage. This is why there is a huge implementation of Artificial Intelligence in almost every industrial vertical today. Starting from automotive, retail, entertainment, manufacturing, and more industries for deploying in their businesses. The introduction of artificial intelligence in a business could bring a whole lot of competitive advantages and also help you flourish. The industry is today facing a lot of challenges regarding big data labeling and annotation processes.

Today Artificial Intelligence Models power big data and it requires massive data. Data should be big and the more you feed into a Machine Learning Model the more accurate predictions you’ll get from it. The data must also be relevant and complete which will let you achieve your goals effectively and also we divide off by blind spots and biases. This should also be labeled properly and undergo several rounds of quality checks to ensure its usability in the process.

Check out Five challenges that Data Annotation and Labeling Industries are facing today:

1. Data Privacy and Compliance

The number of use cases for Artificial Intelligence is increasing rapidly and businesses are rushing to ride the wave and develop new solutions which would love its life and experience. However, on the other hand, the spectrum lies challenges businesses of all sizes are facing data privacy concerns. This is why the government has come up with various solutions like GDP, CCPA, DP, and other guidelines, however, there are new laws and compliances which are being developed and implemented by other nations around the world to protect data privacy.

Huge amounts of data generation are causing privacy concerns and are becoming a wide sensation in all industry verticals. Sensors and computer vision generate data that have the confidential details of the people, KYC documents, license numbers, and more. This has pushed for the need of having proper privacy standards and compliance to ensure the fair usage of confidential data. Law governing bodies have already come up with several data protection and privacy laws to avoid legal consequences in the future.

2. Workforce Management

Data annotation experts spend on cleaning and structuring data and making it machine-readable. At the same time, they also ensure that the data annotation processes are of high quality. Hence organizations are facing a big challenge of balancing both quality and quantity and churning out the solutions that would make a big difference and solve a purpose.

In such cases managing a workforce becomes tremendously difficult and tiring. Most of the companies today outsource people or they have dedicated in-house teams to avoid certain challenges like employee training distribution work and performance, more.

3. Tracking Financial Cost

Most often companies struggle to budget appropriately for their AI projects. According to a survey, 26% of enterprises have complained of a lack of budget to onboard an AI solution. Hence, without metrics, responsible monitoring and objective standards of data labeling success are limited in their ability to track results concerning spending time on work.

As a result brands are either paying for their data labeling projects, in-house or contracted. And as data continues to grow exponentially prices are increasing too. Hence, most brands and organizations are facing huge trouble accommodating data labeling into their budgets.

4. Ensuring the Data quality

One of the important aspects of ensuring data quality is assessing the definition of labels in every data set. For starters let’s understand two major types of data sets. One is objective data that is universally true regardless of who looks at that? Objective data that have several perceptions based on who is accessing and for what purpose they are using. Hence, considering various circumstances, you must be smart enough to understand the true meaning of the data.

This also involves a sentiment analysis module that will be processed based on what an operator has labeled. Here’s how businesses enforce guidelines and rules for eliminating the differences and bringing a significant amount of objectivity in various subjective data sets. This is how brands are facing challenges for maintaining the consistency of data quality as well as quantity.

5. Smart Tools and Assistance

Two distinct types of annotation methods are automatic and manual and now comes a hybrid annotation model which is ideal for the future. This is because artificial intelligence systems are good at processing massive amounts of data efficiently and humans are great at pointing out errors and optimizing the results efficiently.

This is why annotation techniques are catering solutions to the challenges that more or less every industrial vertical is facing today. Smart tools enable businesses to automate work assignments, pipeline management, and quality control of auditor data and offer more convenience. Hence without smart tools, employment would be still working on old techniques and pushing humans significantly for completing the work.

About Us:

Data Labeler offers a cost-effective solution for high-quality data labels. At Data Labeler we undergo constant quality checks as we intend to become your advanced and trusted labeling partner.

We also offer advanced workforce management software which is easily scalable with highly accurate labeled data. Contact Us for more information.