Page 48 - newDATAmagazine | 02>06>2021

And as these organizations go elsewhere for create longer term data and analytics debt.
their data and analytic needs, some fatal
developments occur:
Ÿ Data Silos. These are data repositories
that pop up outside the centralized data
lake or data hub. And with the ease of
procuring cloud capabilities (got a
credit card anyone?), it is easy for
impatient business units to set up their
own data environments.
Ÿ Shadow Data and Analytics Spend. The
growing presence of software-as-a-
service business solutions make it
easy for impatient business units to
just buy their solution from someone
else. Consequently, money that could
b e i n v e s t e d t o e x p a n d
organization's data and analytics
capabilities is now being siphoned off
by one-off, point solutions that satisfy
an immediate business need, but
Figure 4: Data Monetization Roadmap Inflection Point #2. Author image
48 newDATAmagazine.com
Ÿ Orphaned Analytics. Orphaned
Analytics are one-off Machine Learning
(ML) models written to address a
specific business or operational
problem, but never engineered for
sharing, re-use, and continuous
refinement. The ability to support and
enhance these one-off ML models
decays quickly as the data scientists
who built the models get reassigned to
other projects, or just leave the
company.
The result: instead of creating data and
analytics assets that can be easily shared,
reused, and continuously refined, the
t h e organization has created data and analytics
debt that drives up maintenance and support
costs which quickly overwhelms the economic
benefits of the data and analytic assets.
Welcome to Inflection Point #2 (see Figure 4).