| | JUNE 20208CIOReviewIN MY OPINIONBy Dr. Richard Benjamins, Data & AI Ambassador, Telefónica, LUCAThe global business value of Artificial Intelligence (AI) is estimated to be in the trillions of dollars in the coming years. There are great applications of AI such as improved diagnosis of cancer, self-driving cars, automatic translation, personalisation, and even art creation or music composition. What has enabled AI to make such leapfrog in the last decade that it has become so powerful? There are basically three reasons: i) the abundance of data; ii) democratised access to economic processing power; and iii) new Machine Learning techniques such as Deep Learning (many-layer neural networks). It is the combination of those factors that make AI applicable to almost any tasks there is, and hence the estimated global business value. However, it is not easy for large enterprises to become data-driven and AI-powered; it is a long and difficult journey where progress is made through different phases. The journey usually starts with an exploration phase where the potential value of data and AI is tested through some initial pilots. If successful, the next phase is a transformation phase where the organization becomes serious about data: a global roadmap is established, key use cases are implemented in the main business areas, and data silos are broken. Once in the third phase--the data-driven phase--most of the organisation's important decisions are informed by data using machine learning techniques. Data is democratised to the rest of the company, the company culture is becoming more data-friendly, and new, data-driven business models see the light. In the last phase, full advantage can be taken of AI by applying it at scale across the company. When data and AI have taken such an important role, also other aspects like trust, privacy, ethics, and social good are becoming key for sustainable growth. Many companies have learned the hard way that one of the main problems for scaling big data and AI is related to data access and fragmentation, leading to the annoying phenomenon that repeating the same project in a different business does not result in time gain--most of the time is dedicated to data integration. Some enterprises therefore have taken the decision to define a common data format across their organisation and subsidiaries allowing a rapid "lift & shift" approach of successful use cases. Telefonica has defined its so-called "Fourth Platform" that consolidates in a common data format with all data coming from its operational systems. All big data and AI applications are then executed on top of this common format. Other lessons that we have learned during our data and AI journey relate to decisions such as: to whom should the Chief Data Officer report to? What is the relation between IT and Data/AI? How to select use cases? How to measure ARTIFICIAL INTELLIGENCE AND BIG Data in the Telecoms Industry
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