7
between tightly related records. This ensures organiza-
tions can use best-in-class functionality for all workloads,
meaning there is no compromise on performance, scale,
or cost. With purpose-built data services, organizations
get the best price/performance for all of their applica-
tions and analytics needs.
4. Use ML and AI
to solve business challenges
Whether organizations want to enhance their customer
experience, improve productivity and optimize business
processes, or speed up and scale innovation, they can
access ML and AI services to meet their business needs.
AI and ML technologies enable organizations to do more
with data sets that were previously almost unusable. For
example, unstructured data found in content such as
PDFs, audio, video, earnings transcripts, and reports can
now be run through ML processes for fresh insights.
"Instead of having analysts read hundreds of thousands
of documents, we can start to have machine learning go
through those documents, create structured data, and
build applications on top of it," says Michael O'Rourke,
Senior Vice President and Head of AI/Technology,
Investment Intelligence at Nasdaq, which has embraced
the cloud, data, and AI/ML as foundational elements
for innovation and growth. AI and ML play an increasing-
ly important role not just for Nasdaq's data business, but
across the entire organization.
"In the financial industry, the opportunity for AI is
enormous," says O'Rourke. "Within Nasdaq, every single
business line is looking at how they can utilize machine
learning and AI to make better products, improve pro-
ductivity, and create new solutions."
2. continued
Deploy a data catalog or other centralized manage-
ment mechanism that automatically discovers, tags, and
catalogs data so you can manage and audit policies all in
one place. This enables you to provide fine-grained access
to data to the right user at the right time, and effectively
meet regulatory governance and compliance requirements.
Work with your cloud service provider (CSP) to
help you manage compliance across different geog-
raphies. Specifically, make sure your CSP has a way to
control where data physically resides, since the cloud uses
virtual machines that could theoretically be located any-
where. Creating and maintaining a compliance database
can help; mapping out digital compliance standards by
country creates a clear, active structure for compliance.
3. Deploy purpose-built data
and analytics services for the
best price/performance
The exploding volume of data points to be analyzed and
correlated is driving many enterprises to migrate more
of their data and analytics infrastructure to the cloud.
A cloud foundation has the infinitely scalable compute
and storage resources required to analyze mass quan-
tities of data, deliver meaningful, actionable insights,
and provide the rich training data needed for accurate
ML-based automation.
Organizations are using purpose-built databases, analyt-
ics, and ML services to better solve analytics use cases by
storing or processing data in a way that is optimized for
each particular use case. For example, a document data-
base would be apt for a mobile application that requires
great scalability and performance, while a graph data-
base could help developers explore hidden connections
BUILDING A MODERN STRATEGY FOR ANALYTICS AND MACHINE LEARNING SUCCESS
