Good Data is the Foundation for AI Innovation

By: Harmeet Singh

Has the air changed? I'm not sure, but it certainly is supporting the aroma of Artificial Intelligence. No doubt, Artificial Intelligence (AI) and Machine learning (ML) are soon going to have the lion's share in every business. AI is projected to bring a wave of change in the functioning of various industries ranging between cybersecurity and healthcare.

Just as AI is recognized as life support to modern-day industrial automation, good data is considered a fuel for AI-powered technologies. It doesn't matter how advanced AI and machine learning algorithms become unless they are not fed with the data required for comprehensive analysis and insights generation.

In concrete terms, your AI and ML supported applications or software can only be as good as the quality of data collected. Thus, it becomes essential that we comprehend the significance of a good data foundation for AI.

What is a good data foundation, and why does AI need it?

Every AI-powered application depends on the massive amount of data, including structured and unstructured formats. Useful data will lead to accurate and ideal results. Since these results drive critical business decisions, any inaccuracies in output may give rise to failures and unwanted complications.

A good data foundation can be ensured with the help of data cleaning, which helps to rule out incorrect, incomplete, irrelevant, duplicated, or improperly formatted data—the characteristics that frequently contribute to data issues.

The fact that quality of data is the determinant of the quality of outcome is reflected in the phrase coined by data scientists—"Garbage in, garbage out."

For a better understanding, consider AI as a human and data it consumes as food. Now, when this individual takes junk food (bad data), there will be negative effects on his/her health. These will further make him/her prone to diseases.

On the contrary, if he/she has a healthy (good data) intake, it'll have positive effects on his/her health. This will lead to stronger immunity and better functionality.

Defining the integration of data and AI

Data integration is the process of collecting data from different sources and combining it into a unified set. Industries, in the modern-day, are heavily dependent on data analysis to make decisions and raise stakes for data integration.

The success of every AI initiative depends on the data collected — from channel usage and geolocational data to consumer beliefs and behaviors. With the exponential growth in the amount of data from new (unstructured data, departmental data, end-user data) and traditional data sources (CRM, ERP, RDBMS, file system data, etc.), data integration becomes challenging. But that's how it is the competitive market.

In general, Data integration and Artificial Intelligence collectively are proving to be a perfect solution for data scientists. Machine learning, a part of artificial intelligence, is the future of data integration due to its capability of handling a huge volume of data from different sources with ease.

Deep learning, another subset of AI, is a suitable framework to help evolve the integration of data based on past human decisions and apply the knowledge to the data across organizations. All-in-all, AI makes for a revolutionary technology for the integration of data.

What can be said of the AI's present innovation equation?

In recent years, AI has emerged as a great player in the technology-driven industrialization. This is as a result of AI-powered applications entering diverse domains.

For instance, Support Genie facilitates the use of automated chatbots, which can immensely enhance the interaction between your business and the targeted consumer. Not only this, Support Genie's AI-based platform efficiently works to attend customer issues by leveraging the big-data databases.

Such is the prominence of AI that industries across the world are taking deep dives to bring out the long-anticipated potential of technology. If that doesn't speak for the innovation revolution, what does?

Even during the times of pandemic, health care industries are inclining towards AI for the rendering of a feasible solution. Through AI and deep learning, organizations are trying to identify an opponent for COVID-19. Such systems' functionality has been segregated in the following way:

—investigate the current literature pertaining to the disease,

—study the DNA and structure of the virus,

—consider the suitability of various drugs,

The aforementioned applications of AI suggest that it has an all-encompassing nature when it comes to satisfying the innovation equation. However, it paves the way for AI innovation challenges in the name of data privacy, data security, algorithm bias, and data scarcity. All of these should be addressed constantly by employing a specific strategy.

What does AI innovation mean for the customer support industry?

Customer service is one such field that is welcoming AI innovation merrily. A familiar example of AI innovation is Chatbots; these are AI-augmented applications that help customers in various ways, such as making clinic appointments, resolving common queries, make suggestions, etc.

Machine Learning (ML) and Natural Language Processing (NLP) are two major capabilities that have led to such innovations. AI also helps improve the existing methods of calling customer services by devising innovative methods where each call is recorded and analyzed for call duration, expressions, voice tone, and other attributes for feedback purposes.

Understanding the future of AI

To predict the future of AI, we must understand the present of it. As of today, billions of dollars are being invested by tech giants around the world. Universities are making AI a prominent part of their curriculum.

Almost every industry plying its trade on the Internet is already embracing the technology. Still, the influence depends on numerous factors such as workforce, competency modeling, functionality, and niche.

The industries which are most likely to get influenced in the next five years include:

—Transportation

—Manufacturing

—Healthcare

—Education

—Media

—Customer Services

This also raises an important question about whether the jobs will be affected. Well, the answer to this lies in 'How redundant is a job?'. Because AI can, within the routine task, optimize itself. The more objective or repetitive the job is, the higher are the chances that it will be merged with the technology.

It is clear that AI is on the verge of world domination. This technological revolution will be far influential than any other in history for all the good.