3. Do you see data as an asset?
To be truly successful, organizational leadership must agree that data is an asset worth investing in. From the boardroom to the C-suite, this focus on data and analytics driving decision making is crucial. To make this a reality, data quality and good data management will be required to embrace leading methods such as AI, neural networks and machine learning.
Never underestimate the importance of data assets; focus on building and maintaining solid data models, quality processes, and scalable frameworks.
4. How are you delivering value fast?
Design thinking, Agile methodology, Lean and Kanban are examples of rapid response approaches to help frame and address organizational challenges. Enhancing AI, data and analytics capabilities with these approaches can optimize business applications fast so that they keep pace with the speed of your teams.
Using rapid experimentation is a great way to show value fast. Allocate two weeks to a hypothesis and see what can be achieved. This will give a good sense of whether the data quality, technology and skills are there to deliver the desired result. It’s also important to clearly understand the different hypotheses, which ones are low risk and simple and which are hard with significant business risk. Balance the portfolio to ensure there is constant delivery while still pursuing the harder use cases. Make sure there is always value being delivered across basic business and simpler innovation cases while using the more complex transformational hypotheses to keep the business engaged in the process.