1. Take Your Analytics
to the Cloud
How seizing the power of cloud-based analytics will drive your business
and IT goals
Written by Shawn Rogers, chief research officer, Dell Software; David Sweenor, analytics
product marketing manager, Dell Software; Jacob Spoelstra, director of data science,
Microsoft; and Christopher Ray, M.D., chief technology officer, AnesthesiaOS
Abstract
Forward-thinking organizations in a wide range of industries,
from finance to healthcare to retail, have begun to seize
the power of advanced analytics. By taking advantage of
data mining, predictive analytics, machine learning, big data
analysis and other strategies, they are improving decision
making, achieving regulatory compliance, breaking down
data silos and making more accurate predictions.
Now, analytics has a powerful new ally — cloud technologies.
By taking analytics to the cloud, organizations can further
a broad array of business and technical goals, including
reducing costs, improving availability, and enhancing business
and technical agility. This white paper explores the drivers
shaping the future of analytic strategies and details how one
company’s cloud-based analytics solution is helping healthcare
organizations improve patient care and control costs.
Introduction
The changing face of analytics
The ecosystem for analytic strategies is evolving rapidly (see
Figure 1). Not long ago, analytics was an exclusive realm,
limited to analysts trained in statistical methodologies and
experienced with poorly integrated tools. Today, however, a
diverse community of users is eager to embrace the power
of analytics — including business analysts, line-of-business
(LOB) executives, business intelligence (BI) analysts, IT
analysts, developers and data scientists.
Although these users may be experts in their fields, they
often have less statistical training than analysts in the past, so
analytical tools need to be more accessible and easier to learn
and use than ever before. In addition, to support the wide
range of analytic workloads brought by today’s diverse user
base, the tools must be broader and more powerful — beyond
just providing the math, they need to be able to aggregate and
prepare structured and unstructured data, and also efficiently
deploy and operationalize analytical models into the business.
And to meet today’s high user expectations, the tools have toPartnered with
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be highly responsive and available to
users anywhere at any time.
The changing nature of data
The data involved in analytics is
changing in nature and volume as
well. Analytics is no longer limited to
millions or even billions of rows of
structured data in a relational database.
Today, users need to perform advanced
analytics across exponentially larger
data sets, comprising both structured
and unstructured data and residing
across multiple disparate sources,
both on-premises and in the cloud,
streaming and at rest. Therefore,
analytics solutions need to interface
readily with enterprise data warehouses
(EDWs), data marts (DMs), discovery
platforms, cloud platforms, operational
systems, NoSQL databases, Hadoop
frameworks and more.
Combining advanced analytics with
cloud technologies
This evolution in the analytics ecosystem
represents a tremendous opportunity
for organizations that have the right
vision and the right tools. One powerful
strategy is to combine advanced
analytics with cloud technologies. In
fact, organizations worldwide are
already using this approach to advance
both their business priorities and their
IT goals, including reducing costs while
spurring innovation.
This white paper explores the benefits
of taking your analytics to the cloud
and then presents a case study that
details how one cloud-based analytics
solution is enabling a healthcare
organization to meet their twin goals
of improving patient care while driving
down costs.
By taking analytics
to the cloud,
organizations can
further a broad
array of business
and technical goals,
including reducing
costs, improving
availability, and
enhancing business
and technical agility.
Figure 1. The ecosystem for analytic strategies is evolving rapidly. (Source:
Enterprise Management Associates, Hybrid Data Ecosystem, “Operationalizing the
Buzz: Big Data 2013”)
Data mart
Discovery
platform
Enterprise data
warehouse
Analytical platform
(ADBMS)
Cloud data
Operational
systems
Hadoop
NoSQL
Business
analysts
Line-of-business executives
BI analysts
Data
scientists
Developers IT analysts
External
users
Requirements
• Load
• Structure
• Economics
• Complex
workload
• Response
Info
rm
ation managem
ent
D
ata integration
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Benefits of cloud-based analytics
Top drivers for the business and IT
A wide range of business and technical
goals drive analytics initiatives. Business
goals usually include the following:
• Reducing costs, including:
• Capital expenditures (CapEx)
for hardware and infrastructure
purchases
• Software costs, including both CapEx
and operational expenditures (OpEx)
• Implementation costs
• Administrative costs
• Training costs
• Speeding the implementation of
analytics projects
• Enabling non-technical users to quickly
become productive
• Improving business flexibility and agility
IT departments have different but
complementary objectives, including:
• Improving data security
• Reducing software maintenance
time frames
• Improving software availability
• Improving technical agility
• Ensuring scalability to quickly meet
changing business needs
Moving analytics to the cloud serves
both business and IT goals
Adopting cloud technologies — in either
a cloud-only or a hybrid on-premises/
cloud strategy — helps advance all
of these goals. Let’s start with costs.
Cloud-based solutions require no
investment in on-premises hardware or
software; instead, flexible subscription
plans enable organizations to enjoy
lower and more predictable costs. The
cloud provider handles maintenance
and upgrades, greatly reducing
administrative and training costs.
Cloud services can now come with
service-level agreements (SLAs) that
guarantee high levels of availability.
Cloud implementations are easy to
scale up or down as business needs
change. And today’s cloud technologies
can support sophisticated workloads
like machine learning and deliver
analytics at the speed of business.
In short, adopting cloud-based
analytics enables both the business and
IT to meet their goals of reducing costs
while enhancing flexibility and agility.
Popular cloud-based analytics projects
Figure 2 shows just some of the ways
organizations worldwide are already
using cloud-based analytics to support
a diverse set of sophisticated analytics
projects. It’s interesting to note how
popular these projects are in large and
Organizations
worldwide are
already using cloud-
based analytics to
support a diverse
set of sophisticated
analytics projects.
Figure 2. The cloud is ready for prime time, already supporting a diverse set of
sophisticated analytics projects.
0%
Midsized organizations
Company size
Large companies
20.0% 65.0% 15.0%
33.3% 60.0% 6.7%
30.3% 51.5% 18.2%
33.3% 59.4% 7.3%
31.2% 58.0% 10.9%
30.4% 60.0% 9.6%
23.8% 58.8% 17.5%
38.7% 52.1% 9.2%
Enterprises
20% 30% 50% 70% 90%10% 40% 60% 80% 100%
Project workload
Multidimensional analytics
(for example, top customers,
product by region)
Forecasting
(for example, what-if analysis)
Optimization modeling
(for example, maximize output)
Descriptive analytics
(clustering attributes)
Predictive analytics
(future product shipments)
Graph analytics
(for example, relationships
and clustering)
Text/semantic analytics
(for example, sentiment analysis)
Cognitive analytics
(for example, best option)
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enterprise companies in particular. The
cloud is definitely ready for prime time,
especially around today’s sophisticated
initiatives like machine learning.
A case study in cloud-based
analytics
To illustrate the power of cloud-based
analytics, let’s explore how one cloud-
based analytics solution is helping
healthcare providers improve quality of
care while reducing costs.
AnesthesiaOS
In the healthcare industry, it is
becoming increasingly important
to personalize care and treatment
regimens to improve quality of care
and patient outcomes. By leveraging
the power of cloud-based analytics,
AnesthesiaOS is able to incorporate
a wide variety of healthcare data
and provide real-time insights into
anesthesia treatment regimens for
patients to reduce the risk of an
adverse outcome.
AnesthesiaOS is a cloud-based
anesthesia information management
system designed to enable anesthesia
providers to document and improve
patient care from patient admission
through discharge. AnesthesiaOS can
be deployed enterprise-wide to help
healthcare organizations eliminate
clinical data silos and gain the insight
and actionable intelligence they need to
personalize medicine, improve continuity
of care and reduce costs. For example,
the solution helps organizations:
• Accurately assess and reduce risk
• Measure, predict and improve
medical outcomes
• Reduce the number of medical errors
and complications
• Create best practice workflow models
that can be disseminated throughout
the organization
The solution features integrated
advanced analytics and machine
learning. It ensures the security of
confidential patient information while
drawing data from multiple — and often
siloed — sources. It integrates with any
electronic medical record (EMR) system,
including Allscripts and MEDITECH,
and consumes clinical records,
admissions and discharge data, financial
information, insurance coverage
and more. In addition, AnesthesiaOS
interfaces with anesthesia monitors
and machines, accurately capturing a
patient’s physiological data in real time
to provide insight to improve workflow
and outcomes. And because it is cloud-
based, it can scale efficiently to meet the
needs of the largest global deployments.
Reducing medical errors and
related costs
To understand the value of
AnesthesiaOS, it’s useful to consider an
analogy. Airlines do a great deal of work
to prepare for their users: training pilots,
flight crews and support staff; acquiring
and maintaining equipment; and
developing and rehearsing checklists
and procedures. But only so much can
be done ahead of time. As a particular
plane travels toward its destination, the
pilot needs to stay informed about the
plane’s current status and the current
surrounding conditions, and also needs
to be apprised about possible emerging
complications, such as storms or airport
closures, so that he or she can make
appropriate adjustments and improve
the overall outcome. Those alerts need
to be personalized — your pilot needs
to know what’s relevant right now for
your plane specifically, not just what’s
important for any plane at any time.
Similarly, when you’re in the hospital,
you want not only healthcare
professionals who have been properly
trained and up-to-date equipment that
has been properly maintained — you
also want your personal health history
and risk factors available to help guide
your particular care. By leveraging the
power of advanced analytics and the
cloud, AnesthesiaOS helps healthcare
providers deliver that real-time,
personalized care.
In particular, AnesthesiaOS helps reduce
preventable medical errors — the third
leading cause of death in the United
States. The tool combines general
medical information with specific patient
data and informs doctors in real time
of potential problems. For example,
if a doctor attempts to administer
penicillin to a patient who is allergic to
By leveraging the
power of cloud-
based analytics,
AnesthesiaOS is
able to personalize
medicine and provide
real-time insights
to improve patient
outcomes while also
reducing costs.
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the drug, the system will issue an alert
immediately, preventing a potentially
life-threatening reaction.
In addition to improving patient
outcomes and preventing medical errors,
AnesthesiaOS also helps control costs
by reducing the length of hospital stays,
eliminating the need for treatment in
response to errors and reducing surgical
readmission rates. Since the cost of
medical errors in the United States
is estimated to be $21 billion per year,
there are significant savings to be had.
Personalizing medicine with
machine learning
Complementing cloud-based analytics
is machine learning — AnesthesiaOS is
able to improve its analytical models
over time. For instance, one of the
most common complications of
anesthesia is nausea. The solution
includes a model that takes into
account a given patient’s risk factors
for nausea (such as the patient’s sex,
current medications, tobacco use
and length of time under anesthesia)
and makes a prediction about the
likelihood of nausea to guide treatment
for that particular patient. The results
of each new case can be fed back
into the system, which fine-tunes
the model to make more accurate
predictions. For example, factors can
be assigned new weights or eliminated
entirely, and new factors can be added.
As a result, the healthcare organization
can continually improve the level of
care it provides.
The underlying technologies
Architecture of the AnesthesiaOS
solution
AnesthesiaOS is a clear success
story that illustrates the benefits of
combining advanced analytics with
cloud technologies. Figure 3 illustrates
the tool’s architecture and reveals
some of the specific technologies
it relies upon. As you can see, data
from third-party EMRs and the
AnesthesiaOS EMR is aggregated using
Dell Boomi into the Microsoft Azure
cloud platform. Advanced analytical
(machine learning) models are created
and deployed with Dell Statistica to
determine, in real time, the likelihood
of a negative outcome for a specific
anesthesia treatment regimen. The
solution can be further scaled
out with Microsoft Azure Machine
Learning if needed. By providing
real-time analytics within the hospital
environment, the solution enables
doctors and anesthesiologists to
change treatment options for patients
to provide the best possible outcome.
Statistica is the advanced analytics
engine of Dell’s broader portfolio (see
Figure 4). Statistica enables advanced
analytics on any data — structured,
semi-structured or unstructured, and
streaming or at rest — from any source,
including both cloud and on-premises
relational and NoSQL databases. With
its easy-to-use recipes, reusable
templates and advanced visualizations,
everyone across the organization can
AnesthesiaOS
aggregates data into
the Microsoft Azure
cloud platform and
builds and deploys
machine learning
models with Dell
Statistica.
Figure 3. The AnesthesiaOS solution relies on Dell Statistica and Azure
Machine Learning.
EMR
AnesthesiaOS
Google Readmission
case studies
World Weather
Online
Weather
details
Cloud – integrate,
correlate
AnesthesiaOS
Alert provider via
AnesthesiaOS
dashboard
Dell Statistica
Advanced predictive
analytics
SQL Data
Point
Windows
Azure
Patient data
SQL intelligence
central
Data aggregation
within Azure
1. Integrate 2. Analyze
3. Act
Analytical
output
Dell Boomi
Dell Boomi
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quickly become productive with data
mining, predictive analytics, machine
learning, big data analysis and more.
The solution makes it easy to create
a variety of analytical models to
discover the best for the job at hand,
and you can deploy these models in
a single click. Since Dell Statistica is a
validated analytics platform and has
the controls, security and governance
needed to satisfy the most stringent
of regulations, it is an ideal platform
to use in the hospital setting. Plus,
Statistica is extendible, flexible and
open, so it can be easily embedded
and integrated with existing IT systems.
About Azure and Azure
Machine Learning
Azure is Microsoft’s cloud computing
platform. You may know indirectly
about Azure from the How-Old.net
website, which allows users to upload
a photo that the back-end system
analyzes to guess the subject’s age
and sex. Although this site was meant
to purely be a demo during a keynote
address, the site went viral — within
a few hours, almost 7 million images
were being uploaded per hour. Such
an unexpected load would cause most
servers to crash, but because this
site and the analytics back end were
hosted on Azure, the solution could be
scaled dynamically to handle the load —
providing a clear object lesson for why
you want to host your complicated
models in the cloud.
Azure Machine Learning is Microsoft’s
machine learning service in the cloud.
Available as platform as a service (PaaS)
and infrastructure as a service (IaaS),
Azure Machine Learning is a browser-
based development environment
that enables users to easily use
sophisticated machine learning
algorithms to learn statistical models
from their data and then deploy
those as cloud-hosted application
programming interfaces (APIs). With
Azure Machine Learning, you can
integrate machine learning into any
application, whether it’s a web or
mobile app or a complex on-premises
workflow (perhaps driven by Statistica).
Better together: Statistica, Azure and
Azure Machine Learning
Together, Statistica, Azure and Azure
Machine Learning offer a powerful
option for organizations in a broad
range of industries. You can aggregate
and prepare data from disparate
systems, create and deploy powerful
analytical models and workflows, and
easily share the results in on-premises,
cloud or hybrid environments.
Together, Statistica,
Azure and Azure
Machine Learning
offer a powerful
option for
organizations in
a broad range
of industries.
Infrastructure
Advanced
analytics
Business
intelligence
Integration
Management
Put the right data in
the right place at the
right time
Predict and optimize
the future
Understand
historical events
Real-time data
movement on and
off premises
Improve
performance of the
data platforms
Dell portfolio
(hardware and software)
Statistica
Boomi
Flexible data connectors to cloud,
cloud/on-premises, integration
Toad Data Point & Toad Intelligence Central
Heterogeneous data sources, complex joins,
staging repository
Analytics portfolioBenefits
Keycomponentstocompletethe
DataPredictionROIvaluechain
• Predictive analytics
• Machine learning
• Data mining
Statistica
• Monitoring and alerting
• Validated and auditable
• Automated and repeatable
• Test analytics
• Forecasting
• Optimization
Figure 4. Statistica is the advanced analytics engine of Dell’s broader portfolio.
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Conclusion
For decades, advanced analytics has
been helping organizations around
the world optimize processes, reduce
costs, predict the future and increase
revenue. Today, those organizations
need to extend analytics to a much
more diverse range of users and much
larger volumes of structured and
unstructured data.
Combining today’s advanced and
user-friendly analytics solutions with
cloud technologies is a powerful
option. By taking analytics to the cloud,
organizations can enhance decision
making and business agility while
improving availability and controlling
costs. AnesthesiaOS, for example, is
doing exactly that in the healthcare
space, improving patient care by
combining the Statistica predictive
analytics solution with the Azure cloud
platform and Azure Machine Learning.
To continue your exploration of the
power of cloud-based analytics,
we invite you to learn more about
AnesthesiaOS, Statistica, Azure and
Azure Machine Learning.
About the authors
Shawn Rogers is the chief research
officer in the information management
group at Dell Software, as well as an
internationally recognized thought
leader, speaker, author and instructor
in big data analytics, cloud data
management, data warehousing and
social analytics. Prior to joining Dell, he
served as vice president for Enterprise
Management Associates and was a
partner at DM Review Magazine. He
also co-founded BeyeNETWORK, a
global publication covering BI, data
warehousing and analytics.
David Sweenor is the global analytics
product marketing manager for
Dell Software. He has more than
15 years of experience in advanced
analytics, business intelligence and
data warehousing and holds a B.S.
in applied physics from Rensselaer
Polytechnic Institute in New York and
an MBA from the University of Vermont.
Jacob Spoelstra is director of data
science for Azure Machine Learning
at Microsoft. He has more than two
decades of experience in machine
learning and predictive analytics, with
a particular focus on neural networks.
He holds B.S. and M.S. degrees in
electrical engineering from the
University of Pretoria and a Ph.D. in
computer science from the University
of Southern California.
Dr. Chris Ray is a practicing
anesthesiologist and the CTO and
founder of AnesthesiaOS. His goals
include improving the point of care
experience with a smarter and more
intuitive user interface and providing
clinical insight that was previously
not available. He earned a bachelor’s
degree in biology and chemistry at
Texas Southern University and his
medical degree from the University of
Texas Medical School.
By taking analytics
to the cloud,
organizations can
enhance decision
making and business
agility while
improving availability
and controlling costs.
