Companies with strong data science capabilities can become transformational by seeking and codifying best practices into culture.
Moderate to strong data science organizations have integrated practices across the enterprise, creating a broad data science platform (people, tech, practices) in which senior leadership actively demonstrates that data is key to future success. Multiple successful projects across business lines have raised awareness, and data processes are aligned across departments. Data science is now business-centric, not an additional effort attached to an initiative.
At this level, improvement consists of providing data access to all employees, making data use key to understanding the business. Individuals and teams should search for opportunities to use new types of analytics outside of current practices—e.g., sentiment analysis, multi-moment analysis—pushing application for new insights and practices that align with critical business components. Data science should already be integrated, with projects spanning functions and lines, using predictive and prescriptive analytics. However, new methodologies and tools should be investigated and evaluated to ensure advancements are not overlooked in promising tech and data science enablers.
Finally, to reach transformational levels of data science, the business must consider data science as a primary enabler of all business. Most companies reaching this level began as a data-driven organization, and now use data science to underpin the entire business end-to-end. As a result, data-driven innovation enables market disruption due to the unique advancements the company delivers within its field. As the organization identifies performance multipliers and data process excellence that drives insights, all key elements are codified across the organization. At the transformational level, companies exhibit an ethos of always assessing new approaches, surfacing and capturing new insights, and codifying the best.
Learn more about data science maturity models here.