managed analytics as a service - appshare tech
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www.appsharetech.com 1
Managed Analytics as a Service
www.appsharetech.com 2
Appshare Technologies
200+Employees
30+Customers
3 StrategicLocations
$23 MillionRevenues
20+ YearsTrack-record
Industry focusFintech Transportation & Logistics Public Sector TelecomRetail eCommerce Healthcare
Digital Transformation Testing CoEAdvanced Analytics Logistics & Supply Chain Solutions Cloud EnablementIoT Solutions RPA Telehealth
Innovation
Customer focus
Openness
Respect
Enterprising
www.appsharetech.com 3
Analytics Group - Brief
200+Employees
60+Projects
NA, IndiaLocations
90% CertifiedConsultants
20+ YearsTrack-record
Industry focus
Fintech
Transportation & Logistics
Public Sector
Telecom
Retail eCommerce
Specialization Advanced Analytics Digital Dashboard Cloud EnablementAI Solutions Robotic ProcessBig DataEnterprise Data management
ScopeEngagement Models SLA T&M Fixed bid Outcome Managed Services Factory model
Healthcare
www.appsharetech.com 4
What We Provide
Process & Advisory
Implementations
Infrastructure
Upgrades & Migrations
Managed Services
Training
Predictive Analytics
BI to AI Journey Crafting
Data Science & AI
Master Data Management
Big Data
Data Integration, Governance
Services Solutions
www.appsharetech.com 5
What You will Get ?
Domain know how
Context know how
Analytics know how
Installed Products, systems, processes and sensors
§ Improved Performance§ Cost Reductions§ Risk Minimization§ Quality improvement
Digital Services
Data Data Analysis Information Options for Actions Customer Value
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Specialization
• Multi-Dimensional
Data modelling
• Extraction,
Transformation &
Loading, ODS and
Data Marts
• Data Quality
Management
• Data Migration
Enterprise Data Management
• Publish Insights
• Real time and
Aggregated data in
the form of CUBES
• Configurable Dash
boards and Ad-hoc
Query Builder and Ad-
hoc Reporting
Business Insights
• Interpretation and
analysis of structured,
semi-structured and
unstructured data
• Massively Parallel
Processing
• Real time data
streaming
• Data curation,
storage, transfer,
visualization and data
fishingBig Data
• Quantitative and
Behavioral
Modelling,
• Predictive,
Prescriptive and
Cognitive Analytics
• Machine Learning,
Forecasting &
Optimization
Data Science
• Data Warehousing / Data mart• Data Lake• Data discovery/visualization• Master data/Data quality
management• Self-service BI• Data integration• Data Governance• Mobile BI• Real-time analytics• Big data analytics• Data storytelling• Spatial/location intelligence• Data as a product/open data• Cloud BI• Analytical databases• Data labs/science
www.appsharetech.com 7
Our Managed Services Factories
§ Dedicated ‘Shared Service’ Report Factory
§ Catalogue of reports customized to specific roles and user needs
§ ITO/ BPO integrated model for efficient report generation & technology integration
§ Leveraging global delivery model to provide cost efficiencies
§ Leveraging industry standard templates and data models
§ Flexibility in prioritization, based on business needs
§ Dedicated ‘Shared Service’ ETL and ELT Factory
§ Catalogue of Data Lineage and effective integration
§ Leveraging global delivery model to provide cost efficiencies
§ Leveraging industry standard templates and data models
§ Flexibility in prioritization, based on business needs
§ Dedicated ‘Shared Service’ DS and ML Factory
§ Assist you in§ Business Problem use case§ Data Analysis§ Data Preparation§ Modeling§ Results Evaluation§ Productionize (supervised or
unsupervised) § Leveraging global delivery
model to provide cost efficiencies
§ Leveraging industry standard templates and data models
§ Flexibility in prioritization, based on business needs
§ Monitoring§ Service Desk§ Infra, App, Process,
Administration§ Triage Management§ Reporting / Client
Communication
Reporting Factory Scripting Factory Data Science Factory System Management
www.appsharetech.com 8
Why Managed Services
Speed to MarketTypically can start the projects in
10 to 12 weeks
Better ROIsee ROI in < 90 days
Insights Let your data tell the story
Custom dashboard & reportingFrom data to decisions
Increased Customer Satisfaction
Better Customer Engagement
Team of experts will be able to provide the
answers to your biggest challenges
www.appsharetech.com 9
Reporting Factory
§ 16/5 support§ Proactive health check§ Scheduling, monitoring
and administration§ Model refresh§ Rationalization
§ Upgrade assessments§ Model / scripts tests§ Dress rehearsals§ Deployment
§ Report definitions, blue print and rationalization
§ Report development / enhancements
§ ad-hoc and piecemeal report requests
§ Future proofing§ Value engineering§ KPI Bench Marking§ Automation§ Joint Innovation
§ Managed Reporting§ Parameters &
frequency§ Tool Integration Real
Time § Reporting§ Periodic / Quarterly
Review§ SLM
Business Support Migration & Upgrade
Enhancement R&D & Innovation Reporting/Client Communication
www.appsharetech.com 10
Scripting Factory
§ 16/5 support§ Proactive health check§ Scheduling, monitoring
and administration§ ETL/ELT Landscape
refresh§ Source dependent
refresh§ Rationalization
§ Upgrade assessments§ Scripts tests§ Dress rehearsals§ Deployment
§ ETL/ELT definitions, blue print and rationalization
§ Script development / enhancements
§ ad-hoc and piecemeal Data sourcing requests and integration
§ Future proofing§ Value engineering§ Automation§ Joint Innovation
§ Managed Reporting§ Parameters &
frequency§ Tool Integration Real
Time § Reporting§ Periodic / Quarterly
Review§ SLM
Business Support Migration & Upgrade
Enhancement R&D & Innovation Reporting/Client Communication
www.appsharetech.com 11
Data Science Factory
Business Problem Evaluation
Data Preparation Results Evaluation
Data Analysis Modeling Productionize
§ Determine business objective
§ Set criteria of success
§ Assess Constraints
§ Filter data§ Clean data§ Feature engineering
§ Select§ Validate§ Create a Story
(Explain)
§ Understand data situation
§ Obtain access to data
§ Explore Data
§ Select Model approach
§ Build Model(s)
§ Deploy§ Monitor & Maintain§ Terminate
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Systems Management
§ Applications§ Transaction Monitoring§ Log Monitoring§ Session Monitoring§ Process Monitoring § Usage Patterns§ System Performance§ Availability
§ Common Ops Helpdesk§ Trouble Ticketing§ SOP based resolution§ Ticket Assignment§ Escalation Management§ End User Interface§ SLM
§ User Account Mgmt. § Infra Admin§ App Admin§ Archival/Backups etc.§ Master Data Config§ Patch Mgmt.§ Interface Mgmt. § Production Support
§ Interface Issues§ Performance§ Diagnostics§ Problem Identification§ Escalation Path Defined§ Root Cause analysis§ Problem resolution
§ Managed Reporting§ Parameters &
frequency§ Tool Integration Real
Time § Reporting§ Periodic / Quarterly
Review§ SLM
Monitoring Service Desk Infra, App, ProcessAdministration
Triage Management Reporting/Client Communication
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Your Return on Investments
Curr
ent a
naly
tics s
pend
Cons
olid
atio
n of
BI o
pera
tions
Syne
rgizi
ng
Impr
oved
Dat
a qu
ality
Auto
mat
ion
effic
ienc
ies
100%
90%
80%
70%
Translated into ~35% reduction in the overall TCO
Multiple streams of BI Strategy consolidated under a common CoE umbrella to ensure process consistency and efficiency
Improved utilization via centralized demand management, Fail-Fast , reduce effort wastage
Resource cross utilization. Convergence, Customer Experience
Year-on-year productivity gains through ??
Benefits
§ Business : Achieve agility and speed-to-market leveraging reliable business solutions
§ Technology: Deliver high quality “zero defect” business solutions on time and on budget
§ Project Management Office: Establish a “Scalable Global Delivery Model”
§ Cost Management
Increased quality
Cost reduction
Enhanced UX
Process Maturity
Speed to market
Outcomes
www.appsharetech.com 14
Knowledge Management
§ Knowledge Repository for CoE Operations
§ Templates for all Process (including procedures, techniques, methods)
§ Guidelines for processes to be followed
§ Templates for all completed test artefacts
§ Knowledge documents for reference
§ Governance artefacts
§ Processes
§ Techniques
§ Domain
§ Technical
§ Soft Skills
Train-The-Trainer approach to empower Client resources
Knowledge about Skills Knowledge about Operations
Train-The-Trainer approach to empower Client resources
www.appsharetech.com 15
You are in Safe Hands - Security & Compliance
§ Audits conducted internal audit teams
§ Audits conducted by client audit team to ensure compliance
§ Background Verification checks
§ Individual NDA’s for every resources
Busi
ness
Val
ue
Security & Compliance
Audit Methods § Identification of data: a documented approach to identify and collect the data
§ Assessment of its criticality: Data assessed for its risk and criticality
§ Classification of data: Documented approach followed for classification
§ Documented process to handle the critical data
§ Data security awareness: Periodic awareness sessions
§ Regular awareness program are conducted
§ 24*7 monitoring of all security infrastructure
§ Dedicated team to perform security monitoring
§ Products like McAfee EPO, Web sense reporter, ISS/CISCO Network and host intrusion detection system are used for monitoring
§ Physical Security - Not allowed to carry physical devices
§ Network Access- Only client network can be accessed through a VPN tunnel. ACUMA network cannot be accessed from a ODC/CoE.
§ Access Control - Controlled application access
§ Internet Access Security -Restricted Access
Data Security Monitoring & AwarenessOther Security Measures
www.appsharetech.com 16
Making Checks & Balances - Governance Structure
Strategic
Tactical
OperationalClient
AppShare Technologies
• Program Health Check• Resource Utilisation• Risk Management• Approvals• Recommendations
Appshare Executive Steering Committee
Appshare Executive Steering Committee
Appshare Executive Steering Committee
• Engagement Status Reports• Performance Reports
• Strategic Direction• Program Health Check• Contracts & MSA• New Opportunity
• Risk Mitigation Plans• Issues log• Reports on •Monthly Status• Resource utilisation• Performance • Time
• Risk Mitigation Plans• Issue & resolution log• Reports on • Monthly Status• Resource utilisation• Performance • Time • Knowledge documents
• Apps. Dev / QA• Knowledge Mgmt. • Resource
Utilisation• Issue resolution• Quality Mgmt. • Status Reporting
Key Functions Key Artefacts
Key Functions Key Artefacts
Key Functions Key ArtefactsClient Program Manager
Client Program Director
Client Executive Management•Meetings on•Project Initiation •Quarterly /Half yearly /
Annual•Steering Committee •Ad-hoc issues
•Meetings on•Project Initiation •Monthly / Quarterly •Project Team•Status Review •Ad-hoc issues
•Meetings on•Weekly•Status Review•Daily Calls •Email Communications•Ad-hoc issues
Esca
latio
ns
Exec
utio
n
www.appsharetech.com
• Leadership in New Data/ Digitization/ ML Solutions• Delivered and managing Mission Critical applications• Leadership in Business Data Platform and Integration technologies
– Enablers: App infrastructure and middleware, Platform as a service, API management
• Value Engineering Heritage & DNA• Expertise in Disruptive/ Emerging Technologies• Relevant Partnership Ecosystem• Relevant Labs/ Frameworks/ Solution Accelerators
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Why Us?
www.appsharetech.com 18
Why Us? Stats
50Clients
2000Application
1MillionScripts
2MillionReports
10ZBData
500 person years
1000+ reusable Libraries
[email protected] | www.appsharetech.com
Case Studies
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COLT – Factory Model Engagement
BI Factory
• Supply chain data warehousing and Reporting system – Telecom standard subject oriented Enterprise data warehouse leveraged for metrics and attributes
• New Generation Incentive Management System – EDW built on Netezza, ETL designed using Data Stage
• Revenue and Usage Reporting, Analysis and Dashboards – Enterprise BI Data Warehouse on Netezza, ETL through Data Stage and BusinessObjects as reporting tool
• Order jeopardy analysis and alerting system – Analysis, consulting, solution, architecture, ETL Development and Report Development
• Application Support and Tool Support for the Colt Business Intelligence environment using a dedicated support service team working in accordance with agreed SLAs
• Product support covering IBM Information Server ETL, BusinessObjects
• Application support covering Order Monitoring Tool, CBIC & Insight, CSIM –Customer Sales Incentive Management System, NGB Operational reporting, NGB Progressor Reporting, ICE 360 Business Objects, SCM - Supply Chain Management Reporting, SharePoint
Data Science Factory
• Customer churn prediction – predicting churn and analyzing casual agents for churn and augment churn mitigation
• Exploratory analysis to determine the effect of data fields on the overall churn
• Adopted two-step method, identified predictors for churn and recommended interventions to augment retention
• Predictive models built using Logistic Regression (Model 1 – all variables, Model 2- statistically significant variables, Model 3 – active circuits)
• Result – Actionable indicators to reduce churn by 14%
BI Support
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Nottingham County Council – Factory Model Engagement
• IOT Smart Cities• Developed a Pot hole
application based on machine learning and facial recognition from IOT data feeds
• Enabled mobile and home working (change in physical and cultural policies) via technologies
BI Transformation
• Digital Transformation Project – Adult and Children
• Three year project to identify and implement interventions to keep people safe, cut costs and improve peoples lives
• Used data from multiple sources to identify a customer journey (cradle to grave) & service user journey
• EDW & BI– BRMI Phase -1, BRMI Phase -2, BRMI Phase -3, and BRMI Phase -4
• Automated council’s regulatory compliance reporting
• Empowered case workers with near real time information at their fingertips via mobile devices and tablets to assist them with decision making whilst in the field.
Data Science Factory
• Management decisions at service/operational, and strategic levels and in partnership are informed by robust and timely information
• Data supporting decisions on change and savings
• Collect once use multiple times• Bring together data from multiple
systems• Real-time option• Forecasting future demand• Modelling change• Graphical analysis and mapping• Ad-hoc analysis• Data sharing with partners• Data mining
Data-driven Transformation
www.appsharetech.com 22
IndiaLends– Increase in approved loan applications with AI-based approval
Client
Business Challenge
Solution
Result
• Automate loan approval process• Make decisions quickly to secure a deal• Segment customers to accelerate the process based on priority• Target the right demographics with the right messages• Anticipate loan defaults
• Azure based system utilizing Data Factory & Event Hub for data loading, Azure ML for modelling (Naive Bayes Classifier & Linear Support Vector Machine), Azure Functions and Azure SQL Server
• Adopted two-step method, segment customers into Good, Moderate and Bad, and automate loan approval process
• Model created for segmenting customer/evaluating customer data and providing loan approval suggestion
Approved loan applications increased from 14% to 32%
A Fintech start-up working with the objective to make financial products available and easily accessible to the common man
www.appsharetech.com 23
GroupFIO – Text analytics for product categorization
Client
Business Challenge
Solution
Result
• Categorize products based on user comments, description and reviews on various sites
• Find relevant words connecting the product• Unearth complex relations between different verbatim comments and reviews• Map product to product category codes
• Text pre-processing and removal of special characters• Word Embedding using a python package called Gensim• Created Word2Vec models• Recurrent Neural Networks (RNN) for mapping product and product category codes,
with Tensorflow Backend
• Enhanced predictive accuracy by leveraging the power of Reinforcement Learning
• Acquired actionable metrics from customer reviews and comments
• Word Embedding coupled with RNN helped handle unseen data
A leading provider of Innovative Business Solutions specializing in cloud based CRM solutions, Multi-channel Marketing, Omni Channel Order management, Business Intelligence, and ERP application
www.appsharetech.com 24
MogoPlus – Improved data categorization accuracy and critical behavioral insights to better serve customersClient
Business Challenge
Solution
Result
• Use consumer transaction data for insights into a consumer’s credit behavior and earning, and spending behavior
• Generate behavioral insights from customer data on customers about their earning, spending & credit patterns
• Build predictive analytics around Propensity to spend, Propensity to pay a loan, Propensity to default, Suburb level income and spending levels and patterns
• Facilitate better results than the current categorization engine • Use ML and AI techniques for categorizing Financial Transaction data of its customers
achieve maximum accuracy level (match rate, match accuracy and false positives)
• Performed exploratory analysis on data• Utilized ML and AI to come up with Behavioral Insights/ KPIs • Used clustering, Regression / Time Series and Association Mining for model building,
based on KPIs ( such as Propensity to Pay)• Leveraged classification technique using RNN, Naive Bayes Classifier and logistic
regression for data categorization• Created visualizations for exploratory data as well as inferences
• Generated behavioral Insights based on KPIs such as insights covering Customer’s Propensity to Spend or Customer Segmentation
• Identified main channels of transactions (ATM withdrawal, credit/debit card payments, using Classification algorithms)
• Rolled out personalized marketing; product cross-selling based on customer segmentation
• Unearthed correlations in data and produced accurate output as to the categorisation group of data; improved accuracy levels
A global expert in data capture, categorisation and structure sectors
www.appsharetech.com 25
CSL – Predict the unpredictable device failure
Client
Business Challenge
Solution
Result
• Ambiguity in the pattern of devices ceasing to operate, resulting in chaos and indefinite downtime of service in the client's ecosystem
• Eliminate uncertainties in device failure• Unearth causal agents for failure• Implement proactive measures to mitigate maintenance cost, reduce downtime and prevent
monetary and physical loss
• Established cause and effect relationship between correlated events by performing exploratory data analysis • Used the optimal combination of parameters through hyper-parameter optimization • Adopted classification approach to use the input variables in classifying devices as active or inactive• Built the preventive maintenance model using Boosted trees to gain from minimal hyper-parameter tuning and optimal
performance• Initiated trialing with other algorithms including XGBoost, Neural Networks, AdaBoost, Random Forest, Bayesian Networks
and K-Nearest Neighbors
41% reduction in downtime
24% reduction in maintenance costs
29% increase in maintenance productivity
- Over a period of 2 years
A market leader in providing secure connectivity solutions to the Fire, Security and Telecare Sectors globally. One of largest suppliers of signaling solution for intruder alarms in the UK, managing the signaling of hundreds of thousands of residential and commercial installations
www.appsharetech.com 26
GlobalTranz - Increased enquiry-to-sales conversions and revenue
Client
Business Challenge
Solution
Result
• Identify factors leading to order loss• Evaluate if quotes were sent to the right agents• Understand why enquiries are not being converted to sales • Devise a roadmap for qualified sales opportunities• Increase conversion rates
• Data preparation for transforming data• Identified attributes critical for tracking order loss• Performed Exploratory data analysis to determine the effect of data fields on order
loss• Model building using Decision Trees, built a classification tree for order loss analysis
92% conversions from identified genuine leads
26% increase in revenue
A leading full-service transportation and logistics provider
www.appsharetech.com 27
Regatta Outdoor Clothing – Improved demand forecasting leads to increase in salesClient
Business Challenge
Solution
Result
• Manage demand changes while running promotions/challenges in measuring effectiveness of promotions
• Existing forecasts led to inadequate buying• Strengthen demand forecasting to support replenishment cycle• Keep a check on stock-outs as well as overstocking across SKUs• Use/integrate data from disparate systems
• Created a data-lake leveraging Hadoop enabling multiple data access options including batch, real-time • and in-memory processing • Identified key demand drivers by analyzing variables and sales• Combined internal (historical sales data, promotions, events and SKU clusters and external (weather forecasts, competitor
activities to build a model• Leveraged Neural networks, dynamic regression, Bayesian dynamic models for building model
5-17% range of Forecasts improvements across different SKU-clusters
11% increase in sales through stock-out reduction
Promotes a range of outdoor clothing products catering to more than 1 million adventurers