Integrating AI and Data Science
What can Artificial Intelligence and Data Science do for me? Beyond the hype, it’s widely recognized that Data now represents a real accellerator for growth. But how can we optimize our data and integrate Artificial Intelligence into the company's development plan and technologies? How to frame your project and what are possible applications? What tools should be deployed in response to different needs?
Our experts offer a series of articles on the integration of AI and Data Science into your business projects.
Generative AI: 4 steps to deploy it in your business
ChatGPT and similar tools have acted as catalysts, bringing generative AI into the mainstream for the general public. But make no mistake, the greatest value of generative AI will be found within companies, by adapting these tools to their specific needs and knowledge base. To achieve this, a structured and iterative deployment is essential, one…
Adopting generative AI in the enterprise: what methodology should you implement?
74% of business leaders believe the benefits of generative AI will outweigh the concerns, according to the Capgemini Research Institute report Harnessing the value of generative AI: Top use cases across industries. Yet only 19% of companies have launched concrete initiatives. So where to begin? This article outlines the key steps. What are the prerequisites…
The different types of generative AI use cases in business
How can generative AI be used in business? What new opportunities does the technology unlock, and what are its limitations? Like any emerging solution, generative AI raises questions and sometimes concerns because it introduces entirely new ways of working. The following examples around text generation illustrate this shift. While many applications of generative AI are…
How can we deploy trustworthy AI?
68 percent of employees use ChatGPT at work without their employer’s knowledge. That means more than two out of three companies are unintentionally sharing personal or confidential data with a public, unsecured application. This alarming reality raises important questions about the ethical and legal implications of generative AI, and more broadly, about what it means…
Green AI: Responsible artificial intelligence is also frugal
When it comes to Artificial Intelligence, it’s not only about improving performance at any costs. Its benefits along its adoption requires AI to be responsible by also including an environmental side. Taking environmental issues into account is no longer an option. The IPCC’s April 2022 report is clear. It is also, pun unintended, one of…
AI Industrialization: the key steps to a MLOps approach
The industrialization of artificial intelligence – one of the 7 hot data topics for 2022 requires the implementation of MLOps. This approach includes some necessary steps, including a common platform and a feature store. To learn more about this approach, we offer you a how-to-guide for an iterative, but unavoidable transformation. After years, which were…
Data Science applied to Vertical Farming: the future of sustainable farming
By 2050, there will be over 6.5 billion people living in urban spaces according to a United Nations report, making it a real future challenge to feed them all. Vertical farming is becoming a critical component of agriculture’s future. This concept aims to optimize plant growth and soilless farming techniques using as little space and…
Data Ethics/AI Ethics: the 2 faces of a responsible future
Artificial Intelligence is at the heart of all attentions and concerns right now. Did you know that the real difficulty with Artificial Intelligence is not the algorithms, nor the design of the models but it is above all the Data! And yet, Data is increasingly distrusted today. How to solve that and produce trusted &…
Artificial Intelligence: Stay in control of your future!
If there is one topic that really ignites passion and fuels all ideas and discussions in the world of new technologies, it’s Artificial Intelligence. What are the opportunities for enterprises? How to launch AI projects? What are the best practices, benefits, and risks? You will find all the answers in this white paper, available and…
Statistics versus Machine Learning: should they really be opposed?
This “seemingly” old debate deserves to be revisited with fresh perspective. Data Science (such as Big Data) is a constantly evolving field with nowadays proven applications namely in the fields of customer knowledge and marketing… Statistics and machine learning in the era of Data Science and customer knowledge Even though the field of application is fairly recent, the basic methods used in Data…
The 5 key Data Science practices
In the wake of Big Data, many companies embarked on the Data Science journey, the field having established itself as the inescapable route towards Big Data transformation into knowledge and actions. Discover in this blog article the 5 key practices to observe in order to ensure project success. 1. Methodology Data Science methodology is essentially agile and iterative. It derives from inductive reasoning, which…
The key to Data Science success is the CRISP methodology
The CRISP methodology (originally known as CRISP-DM), first developed by IBM in the 60s for data mining projects, remains, today, the only truly efficient process used for Data Science projects… CRISP methodology: User guide The CRISP methodology includes 6 steps that range from business understanding to deployment and implementation. 1. Business understanding The first step involves acquiring a good understanding of the business elements and issues that Data Science aims to improve or solve. 2. Data…
Data Science and AI: how to properly scope your business projects?
An increasing number of companies are opting for data-driven strategies and embarking on marathon Data Science and Artificial Intelligence projects, in the hope of sharing the benefits of new technologies and data. What is the best way to ensure the success of Data Science or Artificial Intelligence projects? Which players will help effect change and…
How is the Port of Antwerp optimising logistics with data science?
Looking for fast, intelligent exploitation of its mass of data, the Port of Antwerp turned to Orange Business to optimise and secure the safety and efficiency of its maritime transport. A report.
CRM and artificial intelligence: how to develop and optimise your data
We are launching a series of three articles setting out our current vision of digital projects connected with customer relations. In this first article we will investigate the current trends for enriching a CRM and how to optimise data quality.
Artificial intelligence, machine learning, data science: are these terms interchangeable?
Many writers talk about AI, machine learning and data science, as if these terms were broadly interchangeable. What’s going on exactly?