Data Analytics as a Service
SOLUTION ARCHITECT, TECH LEAD
What is Data Analytics as a Service (DAaaS)?
Benefits of DAaaS to Business
• The provision of DAaaS analytics and operations offers small and mid
size organizations an alternative to perform business analytics, just in
time, rather than building on premise deployment infrastructure.
• Analytics in Cloud can ease the adoption of advanced analytic
capabilities over the heterogeneous data sources, letting companies
benefit of the insights derived from it.
• Analytics as a Service is becoming a valuable option for businesses to
bypass upfront new capital costs and adopt new business process
Cloud Environment of a DAaaS Solution
Runtime Environment - the execution platform of the DAaaS solution.
Workbench Environment – a set of tools to customize the solutions to the
specific needs of the end-user.
Analytics Cloud for Industry Solution Services
• An industry-leading agile, simple and flexible Analytics Cloud.
• Ingest data flowing in from various sources.
• Form the foundation of smarter solutions services.
• Provide rapid time-to-value, pay-as-you-go model to reduce upfront capital
and operational expense .
Architecture of Smart Analytics Service
• High level architecture of Ficus Analytics Cloud.
• Dynamic large-scale IT infrastructure orchestrator.
• Big Data ingestion and analytics for prediction, optimization and visualization.
Big Data AaaS Business Cases
• In the Oil & Gas sector, companies
could deploy predictive maintenance
solutions for device fleets in remote
installations, without deploying very
complex solutions in-house. The
solution could be rented for short-
term specific analysis.
• In the Electrical Utilities sector, DAaaS
is the basis of a specific solution to
detect Non-Technical Losses, which
cover among others, fraud detection.
The customer can upload Smart
Meter information into the system
where it is processed by specific
analytical services created and
configured by experts in this kind of
• In Smart City solution, the DAaaS
service provides analytic capabilities
for the very different data sources
that are provided by the city, like the
sensor networks deployed in the city.
• In Retail, a DAaaS model can be used
for campaign management and
customer behavior and customer
• In Manufacturing, DAaaS can use the
ever growing data coming from
connected fabrication machines and
when matched with demand it can
allow optimal production with
minimizing scrap and redundancies.
Data Analytics as a Service, as a general analytic solution, has
potential use cases in very different vertical sectors.
Units Sold, Discounts,
and Profit before Tax
Embrace Big Data Across Business
Revenue and Target by
XT2000 Status List
Show Only Problems
Materials and Packaging Review
Book Advertising Slots
Fall Showcase Event Analysis
End User Survey
Technical Review Milestone
50K 60K 70K 80K 90K 100K 110
Product D Product C
0 10 20
Oil & Gas
ure & Web
Big Data Analytics is needed Everywhere
Insurance companies can help
(and some have already
started helping) their
customers with truly
personalized insurance plans
tailored to their needs and
Insurance Companies can collect real-time data from
in-car sensors and combine it with geolocation and
in-house systems. With information such as distance
and speed, provide personalized insurance offers
based on driving amount, risk, and other factors, for a
truly personalized plan that may often save drivers
The vast amount of current and ever-growing
customer purchase, rating and click data can all
be collected and managed with an Hadoop-
based solution, to pinpoint preferences based
on purchase history and demographics, and be
able to serve useful and compelling cross-sell
and up-sell recommendations.
sell and cross-
Retailers can use customer
purchase & rating information
to serve recommendations to
current customers, based on
similarities across many
Retailers – whether large, small, online or in-store –
can improve margins with more detailed pricing
analysis. When a customer is in range of a
transaction (either in the store, online or perhaps
passing by), offer personalized offers, real-time price
quotes, or other frequent-buyer perks to help bring
more customers to the store and improve repeat
Retailers can use customer
past purchase, preference, and
demo-graphic information to
serve real-time custom pricing,
instant discounts when near
for travel from
Improve marketing results by combining public
demographic data, browser site history (or past
store purchases for store or coupon campaigns),
and advertising history into meaningful data
analytics that serves relevant advertisements
and provides tools for analysis and reporting.
Marketers can use current
page information, past
purchase, preference, and
demographic information to
serve real-time, compelling
advertisements that are more
likely to be viewed.
To reduce churn, know each customer
individually to identify warning signs. With a
data analytics solution, demographics and
history data can be reviewed and monitored,
and proactive efforts can be made to avoid
customer churn before it happens.
Customer Churn Analysis
Customers churn happens for
a lot of reasons, including
quality, service, or feature
issues, or new offers from
competitors. Individual analysis
can help reduce each.
Rate of wireless
Legal cases may
of a great number of
documents that must be
stored, processed and
reviewed, then turned
over to opposing counsel
Legal Discovery and Document Archiving
Large organizations and
governments collect a vast
number of documents that
need to be shared internally
or publicly. These need to
be organized, searchable,
and periodically reviewed
Manage documents and
content with a data warehouse
& analytics solution to find the
right content based on
searches, semantics analysis
and pattern matching
not track legal