Sumyag profile deck

Sumyag Insights
Private & ConfidentialTransformation: Analytics, IOT, Digital 1
SUMYAGInsights
Prescient Data and Data Science Services

Sumyag Insights
Sumyag Insights
Sumyag Insights – Solutions
DataStreaming,IOTSensors,Datapipeline,
DataWrangling,DataQuality
PatternAnalysis,AnomalyDetection
Prescience– ExtractValuefrom databefore
Model & Insights
Text, NLP and knowledgeGraphs- Sentiment
Sumyag has a diverse set of products which can be applied to a wide range of businesses with very specific outcomes. Our products
help customers get maximum value out of structured and unstructured data
Insight
generation
Data
Prescience
Text
Processing
Advanced
Models
DATA SCIENCE – BIG DATA DIGITAL – AUTOMATION IOT – SOLUTIONS
Web and MobileApplications
RPA, Automationsolutions
User Experience andDesign
DesignSprintWorkshops
Hack-a-thonevents for Rapid Development
ProductManagementandAgiledelivery
IOT SensorsandlocationbasedControllers
Cloudbased Web Services and API
ML & AI basedintelligenceonaudio,Images
and log –streams
Web based Bi – Dashboards
Digital playoutsatlocationfor user interaction
and nudgepropagation

Sumyag Insights
Sumyag Insights
Data Science – Use Case Retail Smart Store IOT
Business First: Prescience + Framework + Curation
Analytics As-A-Service
Data + Wrangling +Modeling + ML
R, R-Studio, Python, Python-
notebook, Spark , Hadoop –MR,
HIVE, Hbase, Postgres-SQL, Talend,
OpenRefine
Agile – Iteration
India Advantage =Skill , Thrift, Cost

Sumyag Insights
Sumyag Insights
Analytics Services Model
Capabilities, Applications Mapping & Target Clients
The following capabilities and opportunities can be addressed for retail clients through theSUMYAG association
Data Prescience Sand-box.
Engineer, Wrangle, Characterize
Text /Doc Processing pdf, eml,
Doc, vectors, Entity models, Apps
Image Classification CNN, GAN X
Facial. Emotions. attention
Specialty Applications IOT –
Invisiblecustomer [<10% footfalls
convert into sales ]
Entity Research public
information ++generate insights on
entities like companies.
Business analytics Customer
Analytics , Market basket, Next Best
Actions, Segmentation and affinity
Capabilities Application Areas Potential Targets
Prescience Automation
Data Curation & Pre-Science
Technology Familiriaty
Spark,Kafka, Hadoop, Hive,
TensorFlow, Hbase, Python, R,
D3, Node.js, Storm,
ML. Advanced models
UnStructured data NLP, Text,
Images, Video, Voice
Agile Delivery
Outcomes at speed & Cost
Iterate. Co-Create. Outcome
Virtual COE
Confluence of strategy, Change,
DevOps, Cloud, Digital,
Analytics in the right Quanta
Startup Mindset
Build. Measure. Learn. Scale
Multiple domains, solve fast,
and nimbly
Lea d
Skill A Skill B
Skill C Skill D
Peers & Reports
Public Data
Standards
Knowledge Base
Lea d
Skill A Skill B
Skill C Skill D
Peers & Reports
Public Data
Standards
Knowledge Base
Banking-Finance
Risk Analytics, Co-Research,
Customer Management
INSURANCE
Claims, Customer
Management, Risk & Pricing
RETAIL
Smart store networks,
Content. Interact. Insights

Sumyag Insights
Sumyag Insights
Understand what makes Customer Tick,
Profile - customer behaviour and demographics
Complex web of data –mobile, social media, transaction data
Deliver - Customer Retention, Customer profiling and Segmentation,
Cross Sell and Upsell to Customers, & Customer lifetime value
Customer
Management
Pricing & Risk
Management
Targeted pricing based on segments, Customer credit risk analysis, fraud
protection, discounttargeting. Life time Risk Value, Actuarial Riskand
compliance
Process
Optimization
Triaging and STP in Process, Skill based allocation, Understanding
Machines, Devices and Human Interactions
Marketing &
Brand Mgmt.
Single view of the customer, Market Basket & Mix Analysis, Brand Spend
Management, Web clickstream Analysis, leading to better positioning ,
targeting and brand Spend and thereby next best action for the customer
Supply Chain
Optimization
, Industrial Monitoring and Failure Prediction. Inventoryand Logistics
optimization. Capacityplanning & Demand mgmt
Banking Insurance Retail MFG - CG
Use Cases & Business Application

Sumyag Insights
Why Sumyag?
THOUGHT PARTNER
• Digital – IOT – Data Science
• Insurance. Banking, Retail
• Operations Management
• Right Technology Mix
• Data Pipeline / prescience
• Text – Document Structuring
• NLP / Semantics
• IOT – Digital – Insights
• Network / eco-system of skills
• Work on all leading platforms
• Strong Data Science
• Insights thru Code
• Research / POC
• Agile Iterative Organization &
Delivery
• Relationship Driven Execution
• Offshore vCOE delivery Model
Experienced
Leadership
Product
Innovation
Technology
Networks
Flexibility

Sumyag Insights
SUMYAG Insights
Sandbox & Architecture

Sumyag Insights
Sumyag Insights
Typical Data Science SandBox - Technical Architecture

Sumyag Insights
High Level Architecture
Principles of Design
9
Business Domain
» Simplicity > Driven by Business Needs , not over
featuring. Prioritise the most relevant
» Traceability > Transparency > Intuitive Reporting[
logging, traces, interim states ] [ LHR = RHR ]
» Interactive > Focus on intuitive design for users
minimal effort / learning interfaces using digital
Engineering
» Abstraction: Avoid Hardcoding enhance Flexibility ,
De-Couple processingbetweensystems,stateless
» Data Driven Intelligence > use Data based
configuration to make systemadapt to new
requirements
» Algorithmic Process > Noise Cancellation, [eg]
» Re-usability > modular, re-use within and for outside
use-cases
» Interoperability > standards based data and state
interchange
» Maintainability > easy to manage and sustain
Environment
» Cloud > Prefer cloud deployment
» Cost Optimization > long term low cost, through
standard simple infrastructure,re-use
» Tech-Debt > Avoid techobsolescence throughuse of
prevalent, non-proprietary frameworks
Execution
» Agile –Framework. Fixed time Frame, Manifesto
» Use Case - Business driven , focus on requirements
» Iterative > Feedback Driven , quick and learn fast
» Balanced Term > Short Term + long Term

Sumyag Insights
SUMYAG Insights
Team and profiles

Sumyag Insights
Sumyag Insights
Milind [Data Sci]
Technologist with 16 years of experience in
Infrastructureand firmwaredesign. Milind
has been leading Data science initiatives for
the past 3 years in AIG and currently in the
startups that hehas founded. Milind has
worked with brands like AIG, VMWare.
Milind consults on data center design,
Model Engineering and Validation and
designing insights for theenterprise.
Manish [ Org Change]
Manish brings 20+years of experience in
Agile, Lean transformationhaving worked
with WIPRO and other organizations.
Consults on Self Development
workshops, ChangeManagement,
Disruptiveinnovation
Sudip [Digital – Java]
21 Yrs. Of experience in Management
Consulting, Delivery and Innovation
expertise. Proficiency in Retail, Airlines,
Hospitality and Capital Markets.
Consults Strategy, Optimizations, Product
Management and Digital Transformation.
Aims at bringing valuedriven innovation
and transformation through Digital and
emerging technologies.
Data Science
Sumyag Insights our sister firm
forms the talent pool andservices
arm for Data Science,Digital and IOT.
The team is currently7resources
with a networkpool of10+
resources
Digital –Web Apps
SumyagInsights has tiedup
companiesthat bringDigital we b
development capabilities at scale,
and this will be led by Venkyand
Sudip
DEVOPS
SunyagInsightshas signed up 2
companieswith deep resource pools
for DevOps Maintenance and
Infrastructure Management
Sanjay [ Process Change]
18 Yrs. of experience in IT consulting, ITIL
implementation, program management,
transition management, and quality
assurance.
Worked in Defense,BFSI,Auto,and
Energy& Climate Change areas.
ConsultedCIOs and CXOs on IT
operations andbusinessservice
management.
Graduatein Mechanical Engineering.
Sandeep [Process Change ]
12 Yrs. of experience in developing and
implementing valuebased strategic
initiatives in various industries to improve
human and process performance. Played
COO role in e-commerce and IP licensing
industries.
Post Graduatein Management with
graduation in Engineering.
Venkat [ IO T – Firmware ]
21 years experience in deep embedded
and firmwareengineering for hardware
and electronics solutions for IOT and
industrial purposes. Venkat has worked as
the Lead product engineering at Ingersoll
Rand for the past 13 years.
Venkat has been a technology evangelist
and consults on Agile, Product Innovation,
IoT – Technology Strategy, Coaching
Our Team of Senior Leaders
Cumulatively bring 150 Y of experience across the top leaders in the organization

Sumyag Insights
Sumyag Insights
Data Science , Technologists
Abhishek K
Lead Data Scientist with 4 years
Experience
Work: Facial Detection & Emotional
Analysis,NLP Framework, PDF
Vectorisation, Deep Learning frameworks
Areas: Collibra, REST services , Text
Processing & Mining
Skills: Python, Unix, Collibra, Statistics,
Groovy, JavaScript, HTML5, CSS
Clients: Walmart, AIG, Icicle
Mukesh K
8 years experience in Java, Hadoop, Data
Work: Engineering, & Microservices.
Engineer data scientist. Currently
working on Master Data Management,
NLP Platform using Tika, UIMA. GLobal
Data Sync. for Best Buy. Big Data -
marketplace in AIG
Areas: NLP - Text processing, TIKA -
UIMA, Auto data Characterization
Skills: Web Software using Java, Micro-
services, Restful - bootstrap, Agile,
DevOps, hadoop - Eco System
Clients: Sapient, AIG, IBM, Bestbuy,
Nestle
Umesh K
7 years Data Science & Advanced
Analytics
Work: Insurance Customer Insights, Text
Processing Engine ,Enterprise Swipe
Analysis , InsuranceClaims NPS Analysis ,
Fraud Analytics, Market Basket Analysis,
Areas: Hypothesis Testing,
ARIMA/ARIMAX, Fixed effects model,
Linear/Logistic Regression, CHAID, ML
(KNN, Naïve Bayes, Random forest), Tf-
idf, Association Mining
Skills: R, Python,SAS -eMiner, Hive,,
Teradata, SQL Tools
Clients: AIG, WNS, MuSigma, Retail,
Accenture

Sumyag Insights
Sumyag Insights
Data Science , Analysts
Milind K
Data Science with ~2 years experience
Work: Individual surrender Analytics -
porting to Spark, Data Quality & profiling
large Insurance data-sets. Used regex and
algorithms to characterize data-sets
automatically
Areas: regression models, Random
forest, Regression and Classification
algorithms using ML, Regular expressions
based parsing, active on kaggle
Skills: Python,hadoop - Hive,SQOOP,
Spark, Web Apps using Spring, Oracle
PL/SQL, MySQL, SQL Server, SQLite, JSON
Clients: TCS, AIG
Herat K
Data science with ~2 years experience
Work: document similarity, document
classification, clustering, string matching,
web scraping, text cleaning
Areas: Apriori, Linear Regression,
Decision trees, K-means clustering
String, text matching - Levenshtein ,
prediction using Interpolation, Linear
regression, text cleaning
Skills: Python, C, Java, , postgresql, My
Sql, Java script, Ajax
Clients : Embibe , IBM, OpenStream
Suraj J
Work: 5 years python, analytics Modeling
Areas: Python development,
Implementing & Automating Data
Science frameworks , Data warehousing,
big data, data mining, text analytics,
Reporting using Python Django platform,
Model Automation
Skills: Python, Linux, SQL,HTML, GITHUB
Client: Anheuser Busch, Big Basket, AIG ,
Hitachi

Sumyag Insights
Showcase and work Areas
The Work underway

Sumyag Insights
Sumyag Insights
Proposal for Setting up Analytics
• Three modes of play, Research, Develop, Maintain
• Seamless transition to dynamically optimize budget,
• Transparency on capacity , utilization and transfer benefits
• Drive ,Deliver or Support in all the outcome areas – End to End
Tri-modal delivery
Research
Low ~ 0 Cost
Discover solution
Propose / Go-NoGo
Define commercials
Design Dev
Core Technology
Agile – iterate
2 Week Sprints
New and Change
Capacity based Price
Maintain Operate
Reduce Cost
Reduce Capacity
Ensure Upkeep
Time
Cost/BurnRate
Cost & Effort
Data Prescience
1. Data Wrangling - readiness
for modeling
2. Univariate – Bivariate models
and Reports
3. Data Characterization
Model Development
1. Use-Case development of
models
2. Affinity & Association
3. Regression /Classify
4. Clustering – KNN
Business Application
1. Reports and end user design
2. Need for BI or Integration
3. Need for API or Mobile App?
Cloud Analytics Infrastructure
1. Provision Setup Cloud
Analytics Infrastructure
2. Configure and Setup all the
relevant applications and
frameworks for Data Science
Document Retrieval
1. Share-point Policy system
document Retrieval
2. Setup Manual or Automated
Retrieval for future periods
Doc to Data Extraction
1. Extract Data into Tables
2. Parsing document to fields
and tables
3. Validate and cleanse data
Outcomes and Deliverable Areas
1 2 3
4 5 6
Engagement Duration [W] Resources Cost
Develop/Design 2 weeks 3
Maintain/Op 2 weeks 2
Research 2 weeks 1
Onsite 1 week 1

Sumyag Insights
Approach for Research & Discovery - POC
Research Funnel What Clients get?What we do?
Data &
Business Problems
Solution / Model
Framing
Business
Outomes
Engagement
Preliminary Research / Screening
• Define Metrics, Analytics Pathways
• Formaland quantitative methods
• Modelling , Simulation, Solution Discovery
• Showcasepossibilities & Skills of the team
• Mashup outcomes
Data & Scrambling
• Get Sample Data Sets, & Wrangling
• Gauge Business context& Issues
• OutcomeExpectations
Client – Workshops – Finalization
• Risks and Costs Definition
• Deeper insights and options for decisioning
• Client Workshops and finaliseEngagementModel
• Define Commercials & Benefits
Rapid Setup
Short timeframe
1-2 weeks
Low Cost Multiple Pathways
Understand Risk ControlGo or No

Sumyag Insights
Approach – Agile Scrums
17
Onsite
Connect
(discovery)
Sprints – Bi-weekly timeframe
T+1wT T + 13w T + 14w
Onsite
Connect
Operations
& Maintenance
T + x mo
Product Planning &
Agreements
User Experience and
Connect
Systems and Tech
Teams Connect
Understand the
Business Case
Back log development
Maintain outputs emerging from
development
Iterative Agile – Sprints
India Skill and price Advantage
On-location as required
Technical excellence, across Data
Science, Digital, DevOps
Lean, Virtual , Flexible
Back-logs, work to max potential
Onsite presence 1W/Q
Product Development
Review
Customer feedback
Solution & Business
requirements
Further Agile – plan &
Backlog
Technical excellence, across
Analytics, Digital, DevOps
Lean, Virtual , Flexible
Back-log, team executes to
maximum potential
India Skill & price
On-location as required
Low cost – 0 Cost research in
parallel to Development
Research is a Funnel for new
ideas and areas of pursuit
Tech feasibility & research
Separate backlog from Dev
Proposal, Sign off
Research and
Solution Dev
Build and Develop solutions and ModelsNo Deliverable Sprint
R Research – Activities 1
(1) Cloud Infrastructure 1
(2) Document Retrieval 2-4
(3) Document Extraction 2-3
(4) Data Prescience 3-5
(5) Model Development 4-6
(6) Business applications 5-7

Sumyag Insights
Proposed – Relationship Structure
DATA &BI TEAM
Data ingestion, Storage
Data Wrangling – Engineering
Data Characterization –metadata
BI - Reporting
SCIENCE– APPLICATION TEAM
Model Development & Validation
ML & AI
API – Apps
COE LEAD
ProductManagement
Back log, Scrumand Delivery
Manage client Expectation and
Relationship
Bridge between business and
Technology
Consult and Transform
THB –STAKEHOLDERS
Business Heads and Leads
Business Direction, Strategy
Requirement and priorities
Controls , Authorization, Approvals
Coordination with other entities
THB SPONSOR/LEAD
Delivery Model
vCOE Plug & Play delivery model { <12 weeks commitments }
Pricing per Sprint, [burn rate per Sprint ] with options to book sprints in
advance
Communication Channels
Sprint based allocation – implementation & innovation
Measure outcomes
Review “Actionability” – fine tune

Sumyag Insights
Our Current Book of Work
InfrastructureData Science Infrastructure
Design Deploy and Deliver
for Education Services
Robotic Process
Automation in Insurance
Policy Management
Document extraction and
Intelligencein Insurance
IoT Sensor PipelineDesign
Deploy and Intelligence–
Large Indian Customer
Smart Spaces – IOT
Intelligencesolutions
Responsive Digital
Application – Insurance

Sumyag Insights
Sumyag Insights
Showcase: Automating- Data & Prescience
BUILD
MODELS
STREAMING ALGOS
CLASSIFICATION
SIMULATION
ASSOCIATION
REGRESSION
Visual Outputs to
analyse and generate
insights
INGEST DATA TYPE CHARACTER QUALITY TRANSFORM INTERACTIONS
UNDERSTAND YOURDATA
• Connect to a DB
• File , read and
store
• API to pull
streaming data
• Metadata and data
detection
• Most logical join
between tables
• Distribution / Uni-
variate
• Pattern Detection
• Missing and Junk
• Outlier Analysis –
Uni-variate
• Missing at Random
• Data Quality
Recommendation
• Log , Expo ,
Standardise ,
Normalise
• Primary key
summary / Pivots
and Filters
• Categorical to
Numeric
• Data Sampling
• Correlation -
Numerical and
Categorical
• Variable Reduction
• Feature
Importance
GENERATEINSIGHTS
Insight
generation
Data
Prescience
Text
Processing
Advanced
Models
• Outcomes First
– Practical Business Insights
– Deep support Where Operations
meets Business
– Agile – Iterate – Innovate
• Automate First
– Pre-built code /modules that
eliminate manual efforts
– Code Driven Analytics
• Platform First
– Standard Deployment
– Open Architecture / Inter-
Operability
– Configurable,Flexible
• Virtual – COE
– Flexible Operations
– Flexible Skills
– Flexible Capacity–PAYG

Sumyag Insights
Sumyag Insights
Showcase: Science on the Cloud – Science Research Sandbox
• Node on the Cloud
– StandardLarge vendors like GCE,
AWS,Azure
– Rapid Lab Setup
• Technology Stack
– Scrape
– Ingest – kafka, SQOOP, Hive
– Process – MR, SPARK
– Model – R, Python,
TensorFlow, H2O
• Leverage the Ecosystem
– ML-API
• Flexible Scale
– Deploy as you grow
– DevOps
• Consumable Insights
– BI
– API – Web Mobile, Hybrid
– Apps

Sumyag Insights
Sumyag Insights
Showcase: Document – Text Analytics In Insurance
20%
80%
80% enterprisedata is
unstructured, In the
form of Documents,
Email, Text, Logs
FORMATS
1. Legal - Contracts
2. Communication
3. Information
4. Research – Reports
5. Machine Logs
6. Interactions –Chats
Media
Not even considered for Business Information
Complex natureof the
data and the challenges ,
achieving accuracy and
costs in processing has
lead Enterpriseto under
leverage unstructured
Data for the enterprise
Our framework, we a collection of documents and extracts content
retaining the original information, structure and context. A pipeline of
frameworks then extract, objects, Data- Types, meta-data & dictionary
driven Entities finallyDeriving Key-Value pairs. These individual text
fragments are then processed for NLP – for sentiment and Association.
These then can be useful for intelligence or downstream models.
1 Content. Entity Extraction
Document Classification
Document Comparison
Time Series – Sequence
Sentiment Analysis
2
3
4
5

Sumyag Insights
Sumyag Insights
Showcase: IOT . Digital . Insights in Retail
Invisible Customer
Ghost Customer - >90% of customers Who
Don’t Buy
Currently retail have no way of tracking
these customers
Understanding customers Better Where
they walk, Attention, Expression,
Demographics
Applying Technology + Nudges towards
purchase
Smart RetailStore
• Sensors & Controllers
• LED Panels for playout
• Cloud Services
• Data.Ai -Intelligence
• Web Applications
• PresentationBI
• ControlAdmin

Sumyag Insights
Insurance Value Chain – Our Experience
Customer Management Claims Management Actuarial Pricing / Risk
Customer Retention, CLV, Market Basket Analysis,
Segmentation & Targeting, Digital marketing
Claims Triaging – STP, Claims Fraud,
Claims litigation, Subrogation,
Loss models, Catastrophe Risk, Price optimization
Surrender/Lapsationmodel tounderstand
customerpropensitytosurrenderbasedon
demographics, Psychographics,
US – Personal 5 MM Customers,4 Bn USD Portfolio
LogisticRegressionwithSplinesonPythonRSpark
on HadoopClusters
CustomerretentionModel forP&C,life multiple
regions
IndianInsurer4 Regions,500 Mn GWP and 10 Mn
customers.Complete endtoendutility,paidbased
on premiumcollected.
Clusteringandlogisticregressions
ClaimsLitigation– Propensitytolitigate and
sensitivitytoclaimsvalue
US – Worker compensationservices,TPA.
LogisticRegressionsdevelopedonR andPython
ClaimsAllocationandstraightthroughprocessing–
liningupclaimstoagentsbasedon skills
ProximitybasedonlinearClustering
Telematics,AutoInsurance Scoringof Driversfor
Risk valuationandtherebypricingandcustomer
segmentation
2 projects,inUSA on AWS andPython/ Java and
the otherwithPartnerMyDrive on Hadoop
Year to date Loss prediction functionforP&C
insurance onHadoop
Simple calculatortosummarize YTDlossby various
products,regions,clients,speededupusing
Hadoop/ HIVE
Finance / IR InvestorReportsDashboardwith
sentimentandentityExtraction
130 Analysts,perQuarter,,siftingthrough~ 1500
Documents.ExtractEntities& sentimentusingNLP
to understandanalystviewsforCFOperusal
MarketingSpendOptimizationandMarketChannel
Management– Market basketAnalysis
Time Series, LinearCorrelationbetweenSpendand
Sales
ClaimsSubrogation–what isthe possibilityof
counterparty Insurance claimsbasedon
Time Series,LinearCorrelationbetweenSpendand
Sales
24

Sumyag Insights
Other Verticals & Solutions worked on..
• Banking : HSBC :2014 AML / FCC compliance, built a scoring framework for Correspondent banking
transactions passing through USA, Hong Kong, SNG, UK covering ~ 60T USD / 70 Mn Transactions volume through
the bank networks. The objective was to red flag suspect transactions based on value, frequency, transaction
details and source and destination banks
• Insurance: AIG :2015 model Engineering and deployment of 23 Insurance Models built by actuarial teams
on R and Python on to production with execution automation. Worked on worker compensation, lapsation and
other use case
• Insurance: AIG :2016 Mobile Visual Quote: Mobile application front end for visual policy generation using
Image deep learning in the back end to recognize objects through smartphone camera and then responding with a
Amazon like offer to the customer. The solution would snag an image, recognize the object, and provide pricing
options to the customer
• Insurance: AIG: 2015 IT Security Blue-coat analysis to index and score based on red Flagged logs from
global blue coat devices to identify frequent offenders, outbound destination and content type
• Insurance: AIG :2016 Dataquality platform on Hadoop to replace IBM Infosphere and pilot the execution
of Talend DQ on Hadoop .. This was very successful in automating a lot of the DQ processing at large scale.
Consolidated 7000 Data Marts on Hbase

Unmithy. Innoservices
Sister firm. Consulting & Operations
Unmithy Innoservices
HYDERABAD | BENGALURU

27
Unmithy Services
virtualCOE
COE As A Service | Virtual COE without Scale or Large Investment or Long-term Commitment |
Flexible Engagement Options
Key Features
» Get a standard Pod with ﬁxed number of
resources having required skills to support
your initiatives
» Lead resource plays Product Mgr. or
Project Mgr. or Scrum Master
» Flexibility to add niche skills or other skills
as required
Value to Clients
» Access to COE without scale or major
investment or commitment
» ‘Engage-as-you-need’ mode
» A mix of related and complimentary skills
in a box as a service
» Reduced workforce sourcing and mgmt.
costs
Engagement Options Pricing
» Fixed for a given duration and for a given
resource mix (or)
» Per Pod-sprint. A sprint is around 4wks (or)
» Utility based [measured in terms of volume
of output]virtualCOE Pod
Lead
Skill A Skill B
Skill C Skill D
Inputs
Peers & Reports
Public Data
Standards
Knowledge Base
Deliverables
» Source virtual COE Pods – base pod with
4 ~ 6 resources with a mix of skill sets –
for as minimum duration as 3 months
» Engage virtual COE, but measure delivery
in terms of output

Unmithy Services
virtualLeadership
Virtual and Part-time Staffing of Leadership Roles | On-demand Skills | Advisory | Virtual BOD
for SMBs | Independent Review and Validation of Strategies and Execution Plans
Key Features
» Business change for a Network Economy
» Neutral perspective on businessdecisions/ /
plans& strategy
Value to Clients
» Access to high quality leadership skills on
demand without a need to hire a full time
employee or contractor
Engagement Options
» ‘Hire-as-you-need’ – Senior leadership and
SME capabilities to support transformation
programs
» Retainership model for continued
leadership support
» Co-create strategy realization [Ideate,
Mentor, Skill, Execute]
Pricing Options
» Per-hour pricing
» Retainership fee
virtualLeadership

Unmithy Services
Transformation
Transformation Programs Design | Solution Design Workshops | Program Execution | Value
Delivery | SME Support | Innovation Workshops | Independent Reviews
Key Features
» SMEs with over 20 years of avg.
experience in delivering large
transformation initiatives
» Unique confluence of skills [domain,
functional, and change management]
» Proventransformationframeworkandagile
executionmethodology
Value to Clients
» Accelerate value from transformation
programs
Engagement Options
» Full ownership for end-to-end
engagement
» ‘Internal start-ups’ model
» Time and material model
» On-demand engagement of required skills
Pricing
» Per-hour based
» Fixed for engagement
» Outcome-based
» Hybrid of above
TR!Z
Lean
Practices
SIGN
TH!NK!NG
ED

Unmithy Services
Insights
Big Data Science | Code-centred Insights | Pre-built Data & Insight Framework | Open Source
Applications and API | DevOps | Modelling & Simulation | Prediction | Machine Learning
Key Features
» Singled-mined focus on delivering relevant
and right insights
» Code-driven analytics combined with
SMEs with deep domain and functional
knowledge to build narratives
» Leverage Open Source software,
platforms, and knowledge base
Value to Clients
» From numbers to narratives as well for
faster, timely, and effective decisions
» Extract insights fast wide variety of data
[structured / unstructured] in short timed
iterations / sprints
» Reduced TCO [cost of ownership] with
code-driven big data science
Engagement Options
» On-going capability within a “virtualCOE”
wrapper
» Project-based engagement
Pricing
» Mixed pricing on Resources & outcomes
» Baselined on a combination of [data type,
volume, complexity, and use cases]
Impact
Decisions
Narrative
Numbers
Context

Sumyag Insights
info@sumyag.com

Sumyag profile deck

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Sumyag profile deck

Similar to Sumyag profile deck (20)

More from Vishwanath Ramdas

More from Vishwanath Ramdas (20)

Recently uploaded

Recently uploaded (20)

Sumyag profile deck