In most companies, there isn't a Data Science platform for creating experiments that follows a methodology and ensures quality, uniformity, governance, and parameters.

An important key factor for many companies is time to market and competitiveness. While some are struggling to scale and get successful Machine Learning Models into production with a traditional R&D approach, other companies are rapidly turning to off-the-shelf, out-of-the-box platforms to accelerate time to market, reduce operational costs, and increase the success rate.

Either way, having professionals with the appropriate training - both technically and with the business vision - in the development and implementation of Advanced Analytics solutions will significantly increase the chances of success.

The following are the key roles in Advanced Analytics projects. 

1. Data Engineering: Translate business needs into solutions that combine modeling and data flow development activities.

2. ML Engineering: Support the development of Machine Learning solutions in conjunction with Data Scientist teams and bring the most perspective to software engineering.

3. Data Science: Design the solution from a quantitative point of view to meet business needs.

4. Business Insights: Translate business problems to the most appropriate analytical approach and extract actionable insights from analytical models.

Synergy and collaboration between these different areas are key to achieve optimal implementation of the strategies.


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From AI to Z, an executive guide for today's leaders looking for business insights.

Understand the fundamentals of Advanced Analytics in "Data-Driven" companies, how it can add value to business strategies, and the most common use cases being applied by leading companies in major industries.