Data are the great drivers of artificial intelligence, together they promote innovation and business success. These technologies create automatic learning algorithms that allow them to react and respond to all kinds of events in real time. In today’s industrial setting, Artificial Intelligence (AI) driven by Big Data systems is changing the business as we know it.
We are moving towards increasingly automated decision making, creating a strong competitive advantage for organisations seeking to leverage AI efficiently and effectively. However, given that “almost half of companies expect AI to be a game changer” (IBM, 2019), it is important to clearly understand the extent of the capabilities of these two technologies and to dispel the myths surrounding them.

7 disassembled myths

1. Big Data and IA will replace the work done by humans and dismiss them from their positions.

Technology is not meant to replace human ability, but to complement it in order to evolve. Likewise, AI will not be able to replace human jobs, but rather transform existing ones and create new ones. AI will increase the way people complete their work by doing things like extracting and analysing data to assist in real time decision making.
Humans will be able to focus on true innovation, critical thinking and complex reasoning, human intelligence and the outcome of their work will soar. As companies seek to harness AI in their organisations, a skills gap will arise between human employees, and their training in the field of data.

2. The more data we have at our disposal, the better the AI will work.

Larger data lakes will not necessarily help to discover more valuable and deeper knowledge. There is a need to focus on data quality, relevance and diversity, not just on size.
The same example of data repeated a thousand times does not improve the accuracy of a predictive model such as AI. From this reasoning has emerged the term “Deep Data”, a more sophisticated type of data collection that focuses on quality and ease of processing and excludes unusable or redundant information.

3. AI and Big Data are only accessible to large organisations with large resources.

A large number of small and medium business (SME) leaders have a false view of data science and believe that it is only well suited to large organisations.
This is due to the misconception that data science requires a sophisticated infrastructure to process and obtain maximum value from your data. Modern, forward-looking tools and techniques such as Nexus Integra are more accessible, powerful and affordable than ever before.

4. Data can make AI even more intelligent than humans.

AI can be as intelligent as we program it and it can become competent with targets. But without humans, there will be no artificial intelligence. AI technology is not capable of turning itself on, nor of motivating itself, nor of asking alternative questions.
Nor is it capable of drawing conclusions from itself. Without human awareness and understanding, AI cannot be useful or truly creative. This explains why high-tech companies are not successful in their data projects if they do not achieve an empowering culture.

5. Data and AI technologies are difficult to adopt because they are extremely complex.

The existing range of options for implementing cloud-based systems today makes this extremely easy and cost-effective for any organisation. A reasonable investment and training some of your employees to make the most of the data is enough to implement a robust data feedback structure.
Contrary to what many believe, the difficulty lies in the necessary change of mentality, from an expert based mentality to a much more dynamic and much more learning oriented one, instead of a fixed mentality.

6. Data science will be replaced by AI.

Implementing AI without having a complete data platform is almost impossible. Organisations must have a database platform that can scale, is hybrid in nature and has the capacity to consume all types and volumes of data. We can say that Big Data systems are the fuel of Artificial Intelligence.
AI feeds on and uses data, learning from it and developing sophisticated analytical solutions that make it possible to find, even predict answers to problems in real time. Having a cohesive ecosystem in which advanced technologies are fully integrated is the real challenge.

7. To be a data scientist, one must be an expert in programming and statistics.

Data science is about processing numbers to obtain meaningful information and making use of statistics to better understand the results. You need a logical mind, good analytical data and strategic skills but you do not need a PhD in these fields.
Big Data based forecasting is about extrapolating what is most likely to happen in the future, based on what you know happened in the past or even at the moment if it is real time data. Understanding the business domain and knowing how to apply the tools correctly is the key to solving problems and achieving competitive advantage.

The value of Big Data and Artificial Intelligence

Machine learning is not intrinsically valuable; it is worth as much or as little as the potential of your data and the ability to extract it in a way that makes it valuable and meaningful.
Both Big Data and Artificial Intelligence are key to the future of the industry, provided they are used properly. The data must match the intent of the Machine Learning models. If models that fit the data typology are not applied, the technologies will become a worthless liability.
To realise the full potential of data science, it is equally important to do the necessary research and clear up any confusion and misconceptions before actually getting involved.

Implementation with Nexus Integra

From Nexus Integra we seek to clear up all your doubts so that you make the best possible decision when implementing a Big Data and Artificial Intelligence system in your company. The Nexus Integra platform will be your best ally on the way to squeeze the maximum advantages of the digital transformation.
Our software connects machines, sensors, any data source and applications, making it possible to structure, process, homogenise and intelligently exploit all the data. With it you will be able to operate advanced technologies in a simple way, establish predictive and preventive performance analysis and maximize the productivity of your value chain. Don’t hesitate to contact us and we will tell you how!