Despite substantial investments in AI, many organizations ²¹°ù±ð²Ô’t seeing the payoff. Data science and machine learning (ML) capabilities can play an essential role – they connect people, data, and technology to bring AI to life and help it deliver value. This comprehensive framework will help you assess your organization’s data science and ML capabilities to close the gap between raw data and real organizational value.
A cautious culture, variable data quality, skills gaps, and unreliable analytics present major roadblocks to maximizing the potential of the data science and ML function. By assessing the maturity of their data science capabilities, data leaders can expose and address the relevant gaps, then strike the right balance between off-the-shelf tools and purpose-built solutions to build a foundation for AI that can deliver continuous innovation.
1. 91ÖÆÆ¬³§ only what you need.
Not every data science and ML capability needs to be at the highest level of maturity to deliver value. Instead, focus on getting the fundamentals right: solid technical foundations, strong leadership, and a data culture that supports smart, scalable use.
2. Solve the right problem.
Teams often focus too much on symptoms or rush into technical solutions that miss the real problems. Progress relies on clearly defining the problems with an eye on strategic goals and ensuring your data teams are in sync with organizational needs.
3. Culture is the great accelerator.
Even the best data science and ML strategy will stall without the right mindsets and change readiness in place. High maturity in organizational culture and change management accelerates progress in all other capabilities.
Use this step-by-step blueprint to unlock the full potential of data science and machine learning
Our research offers comprehensive guidance and a simple-to-use assessment to help you understand and close the gaps in your data science and ML capabilities. Use this step-by-step framework to develop scalable, value-driving capabilities.
- Formulate well-defined problems aligned to functional objectives to be maximized or minimized.
- Assess data science and ML maturity level to determine the minimal essential maturity level of each capability needed to meet ROI success indicators.
- Determine target state maturity and activities that will improve the effectiveness of each data science and ML capability.