Data Analytics as a Service

STANLEY WANG SOLUTION ARCHITECT, TECH LEAD @SWANG68 http://www.linkedin.com/in/stanley-wang-a2b143b

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 requirements easily.

DAaaS Concept

Functional Elements of a DAaaS Solution

Analytics in Cloud Back End Components

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 business analysis.

• 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 activities.

• 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.

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Build deeper

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Recommenda-tion engines

Smart meter monitoring

Equipment monitoring

Advertising analysis

Life sciences research

Fraud detection

Healthcare outcomes

Weather forecasting for business planning

Oil & Gas exploration

Social network analysis

Churn analysis

Traffic flow optimization

IT infrastructure & Web App optimization

Legal discovery and document archiving

Big Data Analytics is needed Everywhere

Intelligence Gathering

Location-based tracking & services

Pricing Analysis

Personalized Insurance

Insurance companies can help (and some have already

started helping) their customers with truly

personalized insurance plans tailored to their needs and

risks

Personalized Insurance

$1,600/yr.

US national avg. car

insurance premium

Personalized policies can reduce costs

& better meet

customer needs

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

money

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.

Recommendation Engines

Significantly improve up-

sell and cross-sell

opportunities

Retailers can use customer purchase & rating information to serve recommendations to current customers, based on

similarities across many dimensions

158 Items

sold/second by Amazon.com on 11/29/2010

(Cyber Monday)

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 business.

Pricing Analysis

Significantly improve sales and customer

satisfaction

Retailers can use customer past purchase, preference, and demo-graphic information to

serve real-time custom pricing, instant discounts when near

the store.

up to 30%

Additional price Mac

users accepted for travel from

Orbitz

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.

Advertising Analysis

Improve return on marketing

with improved

advertisement response

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.

8% Click through

rate with targeted

Hotmail ads

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

Reduce churn with

proactive customer

campaigns

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.

9% Rate of wireless

subscribers switching services in Europe and USA, 2009

Legal cases may necessitate management

of a great number of documents that must be

identified, collected, 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

Find docu-ments more

quickly; don’t miss needed information

Manage documents and content with a data warehouse & analytics solution to find the

right content based on searches, semantics analysis

and pattern matching

>50% Of organizations do

not track legal hold processes

(US, 2012)