Читать книгу Smarter Data Science - Cole Stryker - Страница 15

“The first characteristic of interest is the fraction of the computational load, which is associated with data management housekeeping.”

—Gene Amdahl

“Approach to Achieving Large Scale Computing Capabilities”

To remain competitive, enterprises in every industry need to use advanced analytics to draw insights from their data. The urgency of this need is an accelerating imperative. Even public-sector and nonprofit organizations, which traditionally are less motivated by competition, believe that the rewards derived from the use of artificial intelligence (AI) are too attractive to ignore. Diagnostic analytics, predictive analytics, prescriptive analytics, machine learning, deep learning, and AI complement the use of traditional descriptive analytics and business intelligence (BI) to identify opportunities or to increase effectiveness.

Traditionally an organization used analytics to explain the past. Today analytics are harnessed to help explain the immediate now (the present) and the future for the opportunities and threats that await or are impending. These insights can enable the organization to become more proficient, efficient, and resilient.

However, successfully integrating advanced analytics is not turnkey, nor is it a binary state, where a company either does or doesn't possess AI readiness. Rather, it's a journey. As part of its own recent transformation, IBM developed a visual metaphor to explain a journey toward readiness that can be adopted and applied by any company: the AI Ladder.

As a ladder, the journey to AI can be thought of as a series of rungs to climb. Any attempt to zoom up the ladder in one hop will lead to failure. Only when each rung is firmly in hand can your organization move on to the next rung. The climb is not hapless or random, and climbers can reach the top only by approaching each rung with purpose and a clear-eyed understanding of what each rung represents for their business.

You don't need a crystal ball to know that your organization needs data science, but you do need some means of insight to know your organization's efforts can be effective and are moving toward the goal of AI-centricity. This chapter touches on the major concepts behind each rung of the metaphorical ladder for AI, why data must be addressed as a peer discipline to AI, and why you'll need to be creative as well as a polymath—showcasing your proficiency to incorporate multiple specializations that you'll be able to read about within this book.