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Artificial Intelligence Has a Key Challenge: Data Quality

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Artificial Intelligence does not represent, in itself, a real novelty. It is in fact a discipline that has existed for decades. However, it is only thanks to recent technological developments that it has found wide application in the business, also favoring the birth of facial recognition software, voice assistants and chatbots, Machine Learning models and paving the way for numerous automations.

Driver of Digital Revolution

Artificial Intelligence has proved to be one of the main drivers of the digital revolution, accelerating the need for companies to have the enormous potential guaranteed by platforms for Data Analytics and the use of Machine Learning models.

It must also be considered that in the current era, increasingly oriented towards Artificial Intelligence and robotics (including that integrated with mobile devices, cloud, sensors and analytics), there is now a very strong demand for scientific and digital skills, especially in areas such as data science, big data, cloud, edge computing, IoT, cybersecurity, machine learning, deep learning, and numerous other areas. Very often, these are Skills and new professional figures that are not always easy to find or understand.

Key Element of Technology

Artificial intelligence and machine learning will be the key technological element of the next generation thanks to concretely innovative capabilities such as computer vision, natural language processing, advanced analytics which, in synergy, will allow us to create more and more solutions. vertical and specific, inevitably impacting the global economy. Already now the synergistic effects of these technologies are accelerating the competitiveness of companies exponentially, although there is still a lack of full collaboration between machines and humans.

Dearth of Data Literacy

Data may well be the Achilles Heel of AI, industry observers agree. There’s a dearth of data literacy that is slowing the pace of progress. Such literacy is an understanding of the value of data and how to manipulate and use it to generate value. The issue for many companies, he points out, is they often lack the appropriate resources, such as data scientists, data engineers, or technology-oriented subject matter experts. These experts have a unique role of looking at the business challenges and the potential for data to unlock solutions to these challenges.

In addition, it’s often difficult to engage in another data-driven activity, articulating business value or ROI. This is also a core competency that many end-users lack. Add to the mix challenges leveraging data from disparate legacy systems and sources which can make it cost-prohibitive to develop truly meaningful AI applications.

Inertia Within Companies

There is also inertia within companies to traverse in the direction of data literacy, connectivity, and human skills. They are either investing and not seeing the value or not investing enough time and money in data management systems to make it successful. The pre-requisite to efficient AI is high-quality data. Many companies are lacking in this area.

At the same time, opening up data full force to AI systems may be troublesome, introducing bias and misguided information. We view every adoption of new AI breakthroughs through the lens of ethical responsibility. We look very closely at how to safeguard against misuse and the harm that can be caused from algorithms that have and inherent bias in the training data. This is more of a due diligence than an issue, but rightfully will hold back more rapid adoption.

Conclusion

Gaining a complete understanding of the data required to ensure greater accuracy in output will pave the way for more advanced forms of AI. Patience, continuous training, curiosity for new approaches and directions, and the integration of new experts in the field into the company team are all equally important factors in obtaining a truly useful artificial intelligence solution that, with time, can truly take on tasks.

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