The shortage of talent to promote and apply the interdisciplinary computer, mathematical and domain knowledge places many projects in pilot purgatory. The overhead of data preparation, model governance, bias, trust, and deployment continue to inhibit democratization. To enable and maximize the creation of value, the integration and utilization of these data science technologies coupled with a rigorous approach is required. Recent progress seeks to radically change our operations and workflows. Advancements in computing power and open source technologies have become a competitive advantage of day-to-day business by fundamentally improving the way the industry operates. McKinsey estimates that AI could potentially deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. With open source, hybrid clouds, high speed networks, increased computing power, and responsive platforms simulation, optimization, AI and ML are being democratized and intertwined at a rapid pace. Waiting for the perfect environment is no longer a strategy, but an agile learning culture is the priority. The question arises: How can all this data drive innovation? Being able to harness the power of data through simulation, optimization, artificial intelligence (AI) and machine learning (ML) can help to improve financial, sales, manufacturing and supply-chain operations enable a better, more intimate customer experience or reduce downtime if done correctly. Many CEOs, CTOs, senior executives, and other decision-makers are seeing an advantage from the rise of big data and faster computing power.
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