Avoid these 8 Data-related Mistakes on Data Projects
insideBIGDATA, Monday, April 29th, 2024
Many businesses fail to see value from their investments in data, analytics and AI resulting in wasted money, time and opportunity for the company.
The main reasons data science efforts fail can be categorized as business-related or data-related. In this second of two articles, we will describe some of the most common data-related reasons for these failures and what you can do to avoid these pitfalls.
Data science teams are not just project performers, waiting to receive instructions. They should be listeners, problem solvers and guides. They are the business customers' collaborative partners. Good data scientists listen to customer's needs, invest time, ask the right questions, patiently answer technical questions, challenge assumptions, and speak in a language that non-specialist can understand.