Find ways to deal with the scarcity of labeled data

Compared to other available choices such as unsupervised and deep learning algorithms: the whole learning process of supervised machine learning (ML) based algorithms is much simpler; the training & deployment costs are considerably low too; and most importantly supervised models work perfectly for "low hanging fruit" projects that enhance business processes - precisely this is where the majority of organizations are concentrating their AI efforts these days.

Clearly, supervised machine learning (ML) based algorithms are uniquely positioned to meet most enterprises' AI needs. The issue with them, however, is that they need labeled data for training. Unfortunately, this kind of data is not readily available in organizations not even in those who have abundance of big data. Enterprises, therefore, somehow have to transform their ordinary data into labeled ones, usually with the help of service providers in the area. Likewise, any other data transformation, converting data into labeled data has its own challenges. The prominent ones are :

• Investment - Enterprises spent decent time, energy, and money in data labeling exercises. Around 25% of the total ML project time is spent on data labeling related exercises
• Scalability - Considering the sheer amount of data and the wealth of AI opportunities, manual data labeling is not scalable. The unscalability in turn limits the AI development
• Quality - Quality control is a mammoth task specifically when each mistake or inaccuracy negatively affects a dataset’s quality and the overall performance of a predictive model

To flourish AI development, enterprises should get rid of explicit data labeling requirements. They must find out different ways to automatically label the data directly from the users and if possible at the source only during the data capture. Existing workflows GUIs (graphical user interface), for instance, can be tuned to automatically label the data. Transition to ML-UX expands on this further.

Alternatively, enterprises can take the advantage of the fact that corporate users on a typical day utilize two systems of records and four systems of engagement applications. For each system, they need to log in at least ten times a day. The majority of these applications are SSO (single sign-on) enabled. Today, user authentication and authorization is the only value enterprises get out of SSOs. However, if SSOs are redesigned to offer multi-value (similar to CAPTCHA), companies would have an unprecedented amount of machine learning data to solve their supervised machine learning problems.

 Find out more perspectives on AI adoption and digital transformation :

• Stop, wait or move forward with the AI adoption journey
• Who fits the bill: ML or rules??
• Hear surroundings' roar to soar in AI efforts
• The magic of tricking AI algorithms
• Policies' unrhythmic music in the digital transformation song
• AI success depends on proximity
• Unlocking proprietary formats data is the key to speed-up AI
• Onboard a cartographer rather than going random on the AI path
• Blend digital talent with economists
• Transition to ML-UX

Send me your thoughts, questions, observations, and predictions by hitting "Add a comment" to this post. Differing perspectives are always welcome do not hesitate in posting them as well.

Thank you for reading my post. I regularly write about the newest digital technologies and digital transformation initiatives. To read my future posts simply click 'Follow'. Feel free to connect with me as well.

Also, you might like to read more about cutting-edge digital technologies in Artificial Intelligence: The Star of the Digital Galaxy: A study of Digital Disruption, Innovation, and Economic Transformation. It's packed with real-life examples and intended to serve as a primer to simplify and explain the concepts, implementations, and implications of the AI-powered digital galaxy.

About Amit Asawa

Amit Asawa is a strategic business & digital technologies practitioner and advisor. He helps enterprises improve their business performance by seeking, evaluating, and implementing technological advancements. He has extensive experience in IT implementations, digital optimizations, transformations, and modernizations including systems integration, business process re-engineering, and organizational change management.

Note: This is the author's personal opinion. This content has not been read or approved by a current or former employer before it is posted, and does not represent their positions, strategies, or opinions.