Business exploration for data scientists and citizen data scientists has been accelerated through augmented analytics, allowing more accessible exploration. Advanced prototyping increasingly uses open-source technologies, for which many vendor platforms offer support, with key stages like feature engineering and algorithm selection becoming automated. Production refinement is important for machine learning as organizations mature around operationalization and analytics leaders demand tangible returns on investment.

1. Critical Capabilities for Data Science and Machine
Learning Platforms (Apr 2019) - Review

2. Business exploration, which is key for
expert data scientists and citizen data
scientists, has been accelerated and made
more accessible by augmented analytics.

3. Advanced prototyping increasingly uses open-
source technologies, for which many vendor
platforms offer first-class support. Key stages
of prototyping, such as feature engineering
and algorithm selection, have become prime
targets for automation.

4. Production refinement is more vital than ever to
machine learning as organizations mature
around operationalization and data, and
analytics leaders demand tangible ROI.

5. Nontraditional data science is still in its
early days, but a significant population of
citizen data scientists and developers are
using tools and functionality tailored to their
skill sets.

6. Strategic Planning Assumptions
● By 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new
investment in the overall business intelligence and analytics market.
● By 2020, more than 40% of data science tasks will be automated, resulting in increased
productivity and broader usage by citizen data scientists.
● By 2020, 85% of CIOs will be piloting AI programs through a combination of buy, build and
outsource efforts.
● By 2022, enterprise AI projects with built-in transparency will be 100% more likely to get
funding from CIOs.
● By 2024, a scarcity of data scientists will no longer hinder the adoption of data science and
machine learning in organizations.

7. https://www.linkedin.com/in/michael-kogan-76865622/
https://www.linkedin.com/in/borismergold/
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Link to the full research