Data-driven decision making (DDDM) is all about taking action when it truly counts. It’s about taking your business data apart, identifying key drivers, trends and patterns, and then taking the recommended actions. The end goal is to get this information into the hands of people who can make informed decisions.
How can an AI model predict customer churn? Who will stay with your business and who will switch to a competitor? It’s easy to make a basic customer churn model with Python.
What is unstructured data? Unstructured data is information which is not organised according to a fixed, predictable schema, and which may not be immediately interpretable. It’s often text-based, although it can include images, numbers, dates, and other details which can be useful to a business, and which can be valuable for AI initiatives in the business.
In the swiftly evolving world of Business Intelligence (BI), one technology is making waves: natural language processing (NLP). As businesses strive to extract value from their vast amounts of data, the combination of BI tools and NLP seems like a natural progression. But is NLP truly the future of BI? And what are the business uses of NLP?
Text Mining: Quick overview The modern enterprise has access to vast amounts of unstructured data but that data can only prove useful if the desired insights can be extracted from it.
Read more about AI for business on fastdatascience.com
What are artificial neural networks and how do they learn? What do we use them for? What are some examples of artificial neural networks? How do we use neural networks?
How Hybrid AI can combine the best of symbolic AI and machine learning to predict salaries, clinical trial risk and costs, and enhance chatbots.
Many applications of machine learning in business are complex, but we can achieve a lot by scoring risk on an additive scale from 0 to 10. This is a middle way between using complex black box models such as neural networks, and traditional human intuition.
Natural language processing can distinguish customers from salespeople Is it possible to use natural language processing (NLP) to distinguish between unwanted sales approaches and promising leads for a business’s customer relationship management? If so, this would be a great application of AI in business.
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