No artificial intelligence without data architecture
In the last few years, the world of informatics has been focusing on a specific concept: the importance of data. We often hear suggestions that data is humans’ most valuable asset, and with growing data and the quality of data retained, how artificial intelligence (AI) can work wonders in analytical systems is something to think about. Experts give us a road map of data, and companies follow these recommendations. But perhaps these institutions need to take a step back before they go too far and make sure they set up the initial requirements correctly. Why?
The year 2020 will be one of the hybrid cloud in the field of corporate software. According to IBM, the value of the hybrid cloud market is $1 trillion. The cloud is no longer a type of storage where only individual users send photos and documents they want to keep, reflecting their most precious memories. At this point in the software extension of Industry 4.0, software and AI companies now want corporations to protect their data, and they want companies to create their own AI architecture by offering open source technology.
In other words, for software companies to invest in the hybrid, they must hold data of corporations as well as individuals, use it correctly and thus add value to the data itself. In 2020, software companies will encourage organizations to embark on a hybrid cloud attack. Figures show that only 15% of data appears globally, while 85% remains idle and is not suitable for use in business. In the simplest terms, institutions cannot derive income from the data they have accumulated.
While many companies are halfway through the data-cloud-AI level in the global sense, how should the world and Turkey progress “in the second part?” How should they convert their data to value? We discussed this issue with IBM vice president for Artificial Intelligence and Global Sales, Alyse Daghelian, who delivered a speech titled “Journey to Artificial Intelligence” at the IBM Think Summit in Istanbul on Dec. 4.
Recalling that AI is a technology that has been on the agenda since the 1980s and that more AI applications have evolved with increasing computational power, Daghelian said that in recent decades, the accumulation of data in institutions has led to the interpretation of data.
“The real value created by consumers lies in how we make sense of this data. As this data becomes meaningful, new applications are constantly emerging where we can use these meanings. At this point, both new areas of use emerge, and AI provides better predictions for these uses,” Daghelian said. “The best example is health care. As the areas of use develop, better patient care emerges. Or just take a look at customer services. Better customer service is now available. Look at production, there are new applications that can make millions of dollars. AI makes sense of this data and leads to new applications in new fields. If we look at the applications of AI in the field of production and business, we see that it remains as single-digit figures.
