Artificial Intelligence is shaping the transformation of our world in many ways. From travel to tourism, to fashion, the influence of AI is leading to advancements that were barely imagined a few decades ago. But what feeds AI? While deep insights into the industry, real-time information and actionable recommendations are easy wins by Artificial Intelligence in almost every industry today, data is the lifeblood of artificial intelligence.

The quick progress made by models such as machine learning, natural language processing and so forth, have placed AI on a higher pedestal. AI-based technology is now viewed as an integral part of our daily lives.

What is lacking in this process is the presence of enough data to support the AI drive. Companies whether small, medium or large need AI-powered solutions to thrive in a world that will soon be dominated by 5G connectivity. As consumers constantly push for better services and products, this demand must be met. Design teams and businesses will need to fashion an appropriate response to this demand. This is where data comes in.

Why is data so important?

Data is the lifeblood that enables AI solutions function. What is Data? Data is everything, from what we do immediately we wake up, to the kind of actions we take just after we leave work. An infographic designed by Visual Capitalist describes the size of what happens on the internet in a minute.

While this “internet in a minute” is mind-blowing, it also offers an itty-bitty peek into the potential data has in changing innovation. If your local barber shop knows when you need a haircut, they can easily suggest to you a better timing to get that haircut, right? If your preferred bookstore knows what your favorite genres are, they can easily suggest to you what kind of new books they have in store.

These are some of the simple ways in which data affects how we interact with products. We easily ignore an email that has no content of interest to us, but we will jump at one which brings us exactly what we need, at the time that we need it. That is the power which data offers to enterprises.

Do businesses deploying AI really need data?

Yes, they do. We love self-driving cars, we love the smart home appliances, and we love the face recognition apps on our phones. These features create ease in the use of tech products generally. However, for them to thrive in the tech ecosystem, the platforms on which they are built continually requires these data that we refer to.

A ready example is the Tesla model cars which can self-drive. This innovation is super cool but would it work just great without data? To create these AI-powered driverless cars, the Tesla Automobile Technology has to capture different road signs, images of cars, road markings, and even humans. Each form of the data which this technology has to learn must be collected, gathered, grouped and properly labeled before it is actually useful for an AI solution.

Because AI solutions are expected to act with human-like intelligence, data aids the process of supervised or unsupervised learning. Machine Learning algorithms which are used to build AI solutions require clean, large datasets in order to train and retrain their AI.

This creates a demand for services that possess clean, properly labeled and representative data. Enterprises with AI solutions can easily adopt this data to train their AI models to a deployable state.

How to train AI using Data

Clearly, data is an important currency for the future of technology globally. But how exactly does this work? The first step is getting the data. In a world plagued with privacy concerns, it is imperative that data solution providers gather data in a compliant manner, or face sanction. For instance, the European Union General Data Protection Regulations has stern sanctions for companies who gather data in a non-compliant manner. Therefore, this data, in whatever form, has to be gathered by a competent data controller.

The next step is the data sets need to be clean, prepared and manipulated to fit the appropriate learning models. Then, the AI model is trained and the data is used to test the AI technology. Imagine a fashion-focused AI solution being fed with unorganized datasets of cars, houses and ships, instead of images of different wears, ridiculous? This only compromises the learning process of such AI, affecting the accuracy of AI projects that are set up by enterprises.

The use of data in training AI models strengthens customer service and provide more trustworthy products into markets by businesses.

It’s not Just Data we need, but quality data

The nature of data used to train AI models such as machine learning, natural language processing and computer vision must be standard and of great quality. As such, enterprises must focus, not just on the acquisition of data, but on ensuring that acquired data are of optimum quality. In an industry with unequal partners, this outcome is not quite easy.

Is there a data drought for industry heavyweights like Microsoft and Amazon in the AI industry? Certainly not. However, can we say the same for small businesses who wish to deploy AI solutions? More importantly, regardless of the size of a business wishing to deploy AI solutions, the quality of data being deployed to train such solutions must be top-notch.

While global companies and multinationals may not break a sweat in obtaining data, it is particularly hard for small businesses to do same. Because the AI industry is still a pioneer industry, it is inevitably costly for small businesses to manage large databases they use to train their AI.

This has created a wide gap between local or home-place stores and heavyweights in keeping up with industry trends. At the end of the day, the effect of this gap is detrimental to society. How do we avoid this and create standard data solutions for small business operators? Certain innovative solutions are springing up by the day to address this.

These solutions are targeted at the root of the data drought for small businesses. Businesses no longer have to rely on only synthetic data which compared to real-life data is unsustainable for AI models. The aim is to provide real-life, quality, processed data that create insights to small businesses who are seeking to stay ahead of the innovation curve. With solutions like these, small business owners have a fighting chance to provide better products and services to consumers.

As the global economy tethers, the agility of small businesses should be strengthened by sustainable ecosystems creating data solutions.