© Abiba Systems | · 2016-06-02 · • Analytics is the science of examining raw data with the...
Transcript of © Abiba Systems | · 2016-06-02 · • Analytics is the science of examining raw data with the...
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© Abiba Systems | www.abibasystems.com
ABIBA Systems
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• Technology firm focused on being a Business Intelligence partner of choice for
Telecom operators
• We have a suite of advanced BI and analytics tools for telcos.
Our solution approach
• Domain specific pre-built solutions
• Rapid deployment for quick wins
• User friendly solutions to improve end user product utilization
• Lower TCO to ensure higher ROI
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• Lower TCO to ensure higher ROI
ABIBA Management
• Promoter team have several decades of experience globally in companies in
Europe, North America, Asia and Africa
• Senior management team is highly qualified with experience in management and
technology
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• Analytics is the science of examining raw data with the purpose of
drawing conclusions about that information. Analytics is used in many
industries to allow companies and organization to make better business
decisions and in the sciences to verify or disprove existing models or
theories. – Techtarget.com
Classification of Analytics
• Descriptive - Descriptive analytics extracting information from the data
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• Descriptive - Descriptive analytics extracting information from the data
for making decisions and inferences.
E.g. Which store is the most profitable one ?
• Predictive - Predictive analytics is an area of statistical analysis that deals
with extracting information from data and using it to predict future trends
and behavior patterns.
E.g. How many customers will leave my network in the next 3 months ?
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• Risk Analytics – Which customer is going to default ?
• Marketing Analytics – Which customer will buy my new
product ?
• Collection Analytics – What should I do to get my customer
pay back the loan ?
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pay back the loan ?
• Web Analytics – How many hits do I get ? Who visits my
website ? Which content is boring ?
• Supply Chain Analytics – What is the most ideal location for
choosing a hub so that I gain max profit ?
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There are basically 2 approaches to analytics
• Product Approach – Buy an analytics tool and execute projects in house
• Service Approach – Source the projects to analytics companies who
execute them using analytics tools
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150
120
90
60
$in mn
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Y = ax1+bx2+cx3+dx4……
0 20 40 60 80 100
60
30
$ in mn
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Data
Integration
Hardware
Intensive
Hardware
Intensive
Hardware
Intensive
Hardware
Intensive
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Data
understanding
Data
Preprocessing
Data
ModelingValidation Scoring
Needs specialized
software
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Analytics
SoftwareSoftware as a Service
‘GoogleAPPS’
Analytics as a Service‘Birst’
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Platform
InfrastructureInfra. as a Service‘Amazon EC2’
Platform as a Service‘DW as a Service’
‘GoogleAPPS’
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Cloud Offerings IT Admin Developers Business Users Business
Analysts
AaaS
SaaS
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PaaS
IaaS
Heavy usageLight usage
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Data
Integration
IaaS
IaaS
IaaS
IaaS
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Data
understanding
Data
Preprocessing
Data
ModelingValidation Scoring
Can subscribe for
modeling software
and scoring
engines (SaaS/
PaaS)
• BI/Analytics Applications – TIBCO Spotfire
• High Processing Speed - Amazon EC2
• Huge data storage – Amazon EC2
• Analytics platform – Adapa
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Scalability with on-demand, ready-to-use
platforms that adapt readily to growth.
Elasticity with platforms that
expand and contract when needed
Agility of technical
infrastructure & ProjectsProductivity gains
Faster time-to-paybackfor analytic applications
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Infrastructure cost
reduction
Device and location independencemaking applications web accessible
Reliability through
multiplicity, redundancy thereby reducing data disasters
Operational cost reductionespecially for heavy-lifting applications such as very large data warehouses.
Productivity gainsAs time &resources Is focused on business results instead of infras. mgmt.
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• Are my business activities and metrics exposed on the Internet?
• Are they intrusion-proof and hacker-proof?SecuritySecurity
• Is proprietary data at risk of becoming publicly exposed information?
• What about anonymity of my customers, business partners?PrivacyPrivacy
• Does it meet the regularity constraints ?ComplianceCompliance
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• Does it meet the regularity constraints ?
• What should be done to meet the constraintsComplianceCompliance
• Is control diminished with cloud computing?
• Can the responsibility be fulfilled if the infrastructure is remote and virtual?
Infra. ControlInfra. Control
• Can the IT infrastructure be bypassed?
• Will business units deploy their own applications in the cloud without enterprise-level integration?
GovernanceGovernance
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• More Analytics Users - Many companies might shift to BI & Analytics
because of the subscriber based SW and HW
• Paradigm shift -Might bring a change in the analytics methodology.
• Reduced Project Timelines -Project Duration might come down drastically
• More to gain - Overall ROI on Analytics will increase
• Product Vs Service - Product based Analytics companies have an
advantage over service based companies
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advantage over service based companies
• Domain Knowledge – Domain specific BI & Analytics Software companies
have an advantage over generic players
• Big = Small - Size advantage will diminish if not removed
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Higher Gain
from cloud
delivery
Web Serving
CollaborationNumerical
ComputeSME ERP/
SCM/CRM
DWH
Analytics &
DataminingApplication Dev’t
& Testing
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Lower pain to
cloud deliveryHigher pain to
cloud delivery
Lower Gain from
cloud delivery
Large Enterprise
SME ERP/
SCM/CRM
& Testing
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Model
Producer
PMML
DataData Pre-
processingfeatures
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Model• The Predictive Model Markup Language (PMML)
is an XML language for statistical and data mining
models (www.dmg.org).
• With PMML, it is easy to move models between
applications and platforms.
• PMML also supports XML elements to describe
data preprocessing
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Model
ProducerData
Data Pre-
processingPMML
Model
Modeling Environment
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Model
ConsumerPMML Model Post Processing
Data Data
Deployment Environment
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Step 1: Enter credentialsStep 2 : View the management consoleStep 3 : Choose the model for scoringStep 4 : Evaluate the model
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Scored Models
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Less than a dollar/ hr one can
score 300 million records in 22
min .
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• Client – A North American Financial institution providing loans to SME’s
• Business Issues –
– Increased defaulters during the economic crises of 2008.
– increased demand and higher risk made it increasingly difficult to
accurately assess credit worthiness.
– threat to its profitability and its plans to launch new distribution
channels
• Technical Issues
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• Technical Issues
– Cost of analytics packages for building risk models were costly
– Most of the package were generic and time consuming
• Solution – Went for SaaS Model
• Result
– Project was completed in 6 weeks
– 14% increase in productivity
– 900% was ROI in the 1st year
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• Client – European Space Agency
• Business Issues –
– To make the largest and the most precise 3 dimensional map for the
entire galaxy
• Technical Issues
– 42 GB of data was transferred everyday. Processing one pic/milli sec
will take 30 yrs.
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– Data load came in spikes
• Solution – Amazon IaaS /PaaS
• Result
– 100 mn stars X 6 yrs data X 16,200 hrs X 344,000 Euros
– Saved more than 400,000 Euros
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