Читать книгу Smarter Data Science - Cole Stryker - Страница 26

Through climbing the ladder, organizations will develop practices for data science and be able to harness machine learning and deep learning as part of their enhanced analytical toolkit.

Data science is a discipline, in that the data scientist must be able to leverage and coordinate multiple skills to achieve an outcome, such as domain expertise, a deep understanding of data management, math skills, and programming. Machine learning and deep learning, on the other hand, are techniques that can be applied via the discipline. They are techniques insofar as they are optional tools within the data science toolkit.

AI puts machine learning and deep learning into practice, and the resulting models can help organizations reason about AI's hypotheses and apply AI's findings. To embed AI in an organization, a formal data and analytics foundation must be recognized as a prerequisite.

By climbing the ladder (from one rung to the next: collect, organize, analyze, and infuse), organizations are afforded with the ability to address questions that were either previously unknown (When will a repeat buyer buy again?) or previously unanswerable (What were the influencing factors as to why a given product was purchased?).

When users can ask new questions, users can benefit from new insights. Insights are therefore a direct means to empowerment. Empowered users are likely to let specific queries execute for multiple minutes, and, in some cases, even hours, when immediate near-zero-second response is not fully required. The allure of the ladder and to achieve AI through a programmatic stepwise progression is the ability to ask more profound and higher-value questions.

The reward for the climb is to firmly establish a formal organizational discipline in the use of AI that is serving to help the modern organization remain relevant and competitive.

In the next chapter, we will build on the AI Ladder by examining considerations that impact the organization as a whole.