Industry executives and experts share their predictions for 2020. Read them in this 12th annual VMblog.com series exclusive.
By Josh Poduska, chief data scientist at Domino Data Lab
Consolidation of power and platforms will accelerate in 2020
The explosion of AI efforts will be accompanied by a growing trend
toward consolidation of data science organizational power. The idea of setting
up an internal data science practice is not new, and most companies have
already invested here. Executives realize that the competitive advantage of
the next five years will belong to those who can build the best data science
flywheel. Integrating model insights into decision flows and significantly
increasing the number of quality machine learning (ML) and AI projects are the
high-level keys to success in building this flywheel, but the former is harder
than the latter.
In order to get lucrative
models integrated into the fabric of the business, leaders are seeking a better
process - best practices and workflows for collaborative data science that
start with the end in mind. Efforts to increase the funnel of AI projects lean on recent technology
advancements, specifically a new class of enterprise software called Data
Science Platforms, which remove dev ops barriers to model research and
deployment. They also facilitate collaboration and reproducibility, two key
elements of running effective modern data science teams. With access to
better centralized platforms, data scientists will be significantly more
productive, but business leaders will be slower to define and enforce the
processes needed to ensure that work gets successfully into production to
improve decision making. While the particular implementation details will
vary, this trend of consolidation of power and platforms will also accelerate
in 2020.
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About the Author
Josh Poduska is the
chief data scientist at Domino Data Lab. He has 18 years of experience in
analytics. His work experience includes leading the statistical practice at one
of Intel's largest manufacturing sites, working on smarter cities data science
projects with IBM, and leading data science teams and strategy with several big
data software companies. Josh holds a master's degree in applied statistics
from Cornell University.