A STRATEGIC PLAYBOOK FOR DATA, ANALYTICS, AND MACHINE LEARNING
Becoming a data-driven organization
is achievable through a mix of technology, people, and
processes, bolstered by a broad view of data as a stra-
tegic asset. While most organizations have data-driven
transformation in their sights, the shift remains elusive
for many:
u 99% of organizations have invested in data and
artificial intelligence (AI) initiatives
u 65% have spent more than $50 million on these
efforts
But…
u Just 38% say they are a data-driven organization
u Only 27% say they've successfully built a data-
driven culture (Source: 2020 NewVantage Partners
Big Data and AI Executive Survey)
The numbers show a clear gap between organizations'
aspirations for using data strategically and the reality of
what they've accomplished to date.
Some have already taken steps to close the gap,
using the cloud as a foundation for a modern data
architecture:
u Georgia-Pacific migrated 50 TB of structured
and unstructured production data from a legacy
database infrastructure to a cloud-based data lake
in order to cost-effectively ingest, transform, store,
and analyze that data. Layering analytics tools on
the data has helped Georgia-Pacific optimize key
manufacturing processes, including the ability to
predict equipment failure 60 to 90 days in advance,
which reduces unplanned downtime.
u Zappos is using cloud-based analytics and machine
learning to personalize product sizing and search
results for retail shoppers while preserving a highly
fluid and responsive user experience. The result:
Zappos has reduced repeated searches and product
returns, achieved higher search-to-product-click-
through rates, and raised the position of customer
selections in search results.
u After The Pokémon Company International (TPCi)
launched its Pokémon GO mobile game in 2016,
the number of users requiring access to the system
increased to more than 300 million in two years. To
address the massive influx of Pokémon GO users,
TPCi migrated from a third-party NoSQL document
database to AWS (Amazon Web Services) fully man-
aged database services, which allowed it to reduce
the number of database nodes from 300 to 30 and
decrease its monthly database costs by tens of thou-
sands of dollars.
These examples underscore the benefits of moving
away from antiquated, monolithic applications that
run on one-size-fits-all relational databases to highly
distributed, microservice-based systems running on
multiple purpose-built databases. It also means moving
from on-premises and old-guard legacy data ware-
houses to open and flexible data lakes and "lake house"
architectures.
"It's not a one-size-fits-all world anymore," says Shawn
Bice, Vice President, Databases, with AWS. "How you
approach data in the cloud is the foundation for future
business growth. A native cloud application architecture
allows businesses to innovate faster than ever before, at
lower cost, and faster time to market because you're not
limited to what one thing can do."
Wanted: A modern, data-driven enterprise
u
t
3
