Data science and AI are two powerful tools that can help decision makers in many different ways.
AI can be used to predict future outcomes, which is very useful for decision makers. It can also be used to optimize the process of making decisions, by reducing the time it takes to make a decision and improving the accuracy of those decisions.
Data science can also be used for decision-making purposes. It is often applied in marketing, where it is used to study customers’ preferences and make predictions about their behavior.
The demand for data scientists has increased dramatically and will keep rising with advancements in AI. Data analytics and AI will continue to play significant roles in business and society, according to a recent article by Thomas H. Davenport and DJ Patil in the Harvard Business Review. Data science is Read more…
With AI getting adopted by every industry, leaders need to be able to understand what is the best way to go about data transformation. Digital transformation is the process of using digital technologies to transform business operations, while AI transformation involves incorporating artificial intelligence into business processes to enhance performance Read more…
How do developers deploy, automate, and maintain Machine Learning models?
MLOps tells you how, the tools, the features, and the future.
A new book on AI, data science and blockchain I am very proud to announce that my new book is out! The book is called “Business Models in Emerging Technologies” and covers a variety of topics around AI and blockchain. The audience the book is addressing consists primarily of leaders Read more…
Emotion and technology Affective computing is a term that refers to the synergy between AI and psychology in order to understand and affect emotions. Another term for it could be emotional AI. It is a niche within AI that is not very popular, but I really believe it is going to Read more…
Data maturity Data is the new oil. All businesses, big or small, need to understand and adopt a data-driven approach in order to stay competitive. Data maturity is the concept of how mature an organization’s data strategy and data culture are. It is measured through a set of questions that Read more…
Customer churn and data science Customer churn is a major concern for any business. It is the process of customers leaving their service provider for a competitor’s service. This can be due to many different reasons, including financial constraints, poor customer experience, or just general dissatisfaction with the company. Predicting Read more…
The amount of data generated by businesses has exploded in recent years. Many opportunities await companies to improve innovation, customer satisfaction, and operations with the help of data analytics. A shift toward a data-driven culture in your organization will help you make better strategic decisions and achieve your business goals. Read more…
Data maturity is a term that is used to describe the process of understanding how to use data to make decisions. Data maturity is not a one-time event. It’s an ongoing process that requires continuous investment and learning in order to achieve the highest level of data maturity. The data Read more…
AI adoption for the enterprise is on the rise. The advantages of AI are clear – it can automate repetitive tasks, it can provide predictive insights to businesses and it can improve customer experience. Enterprise AI adoption is not always easy. There are many factors that can affect the success Read more…
You must be logged in to post a comment.