managed analytics as a service - appshare tech

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Managed Analytics as a Service


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


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


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


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


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


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



§ 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



§ Monitoring§ Service Desk§ Infra, App, Process,

Administration§ Triage Management§ Reporting / Client

Communication

Reporting Factory Scripting Factory Data Science Factory System Management


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


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


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




Review§ SLM

Business Support Migration & Upgrade

Enhancement R&D & Innovation Reporting/Client Communication


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


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




Review§ SLM

Monitoring Service Desk Infra, App, ProcessAdministration

Triage Management Reporting/Client Communication


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


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


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


Making Checks & Balances - Governance Structure

Strategic

Tactical

OperationalClient

AppShare Technologies

• Program Health Check• Resource Utilisation• Risk Management• Approvals• Recommendations

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

17

Why Us?


Why Us? Stats

50Clients

2000Application

1MillionScripts

2MillionReports

10ZBData

500 person years

1000+ reusable Libraries

[email protected] | www.appsharetech.com

Case Studies


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


• 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


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.


• 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


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


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


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


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


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


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

www.appsharetech.com | [email protected]

For further details, please contact us

Thank you

managed analytics as a service - appshare tech

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