Four steps to data-first modernization
With these 4 steps, you can achieve data-first modernization to help make the most of your investments. Read this solution brief to learn more.
What is a data-first transformation?
A data-first transformation involves more than just implementing new hardware or software. It requires organizations to learn how to effectively acquire and act on data, using it to reshape processes. This journey can be broken down into manageable steps that help organizations evolve into modern, data-driven entities.
Why is data-first thinking important?
Data-first leaders tend to outperform their competitors significantly. For instance, they are 11 times more likely to exceed revenue goals by over 10% and 20 times more likely to bring innovations to market faster. This approach not only enhances resilience against data loss but also boosts job satisfaction among developers and data scientists.
How can organizations prioritize their data investments?
Organizations should first identify their current level of data maturity and understand the key areas that need improvement. By focusing on sub-domains such as data strategy, governance, and lifecycle management, they can create a clearer picture of their strengths and weaknesses, allowing for more informed investment decisions that align with their data-first goals.
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