6 Pitfalls on the Road to Becoming Data- and AI-Driven
Avoid obstacles, and accelerate toward AI success
Let’s navigate around these common pitfalls
- Empower your CDO to lead a cultural shift
- Invest in AI-native master data management (MDM) for effective data management
- Create a business case that outlines the value of your data initiatives
- Embrace real-time capabilities for seamless data mastering
- Automate processes to prevent data engineer burnout
- Connect internal and external data sources for a holistic view
Let’s navigate around these common pitfalls
- Empower your CDO to lead a cultural shift
- Invest in AI-native master data management (MDM) for effective data management
- Create a business case that outlines the value of your data initiatives
- Embrace real-time capabilities for seamless data mastering
- Automate processes to prevent data engineer burnout
- Connect internal and external data sources for a holistic view
Discover the benefits of clean, connected data
There’s no question that data- and AI-driven businesses perform better. According to Forbes, companies that invest in data-driven behaviors see results: They are 23 times more likely to acquire customers than their competitors, about 19 times more likely to remain profitable, and nearly seven times more likely to retain their customers. Being data- and AI-driven gives these organizations a competitive edge because they have better visibility into their operations. And this visibility leads to better, more informed decisions, more effective business strategies, and fewer wasteful expenses.
While there are many reasons why organizations struggle to become data- and AI-driven, we believe there are a few common pitfalls that derail even the best of intentions. Six, to be exact. Learn what they are and, most important, how to avoid them.