Sumyag profile deck

Sumyag Insights

Private & Confidential Transformation: Analytics, IOT, Digital 1

SUMYAG InsightsPrescient Data and Data Science Services

Sumyag Insights


Sumyag InsightsSumyag Insights – Solutions

Data Streaming, IOT Sensors, Data pipeline,

Data Wrangling, Data Quality

Pattern Analysis, Anomaly Detection

Prescience – Extract Value from data before

Model & Insights

Text, NLP and knowledge Graphs - 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 Mobile Applications

RPA, Automation solutions

User Experience and Design

Design Sprint Workshops

Hack-a-thon events for Rapid Development

Product Management and Agile delivery

IOT Sensors and location based Controllers

Cloud based Web Services and API

ML & AI based intelligence on audio, Images

and log –streams

Web based Bi – Dashboards

Digital playouts at location for user interaction

and nudge propagation

Sumyag Insights


Sumyag InsightsData 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 ClientsThe following capabilities and opportunities can be addressed for retail clients through the SUMYAG 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 –

Invisible customer [<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. ScaleMultiple domains, solve fast,

and nimbly

Lea d

Skill A Skil l B

Skil l C Skill D

Peers & Reports

Public Data

Standards

Knowledge Base

Lea d

Skill A Skil l B

Skil l C Skill D

Peers & Reports

Public Data

Standards

Knowledge Base

Banking- FinanceRisk Analytics, Co-Research,

Customer Management

INSURANCEClaims, Customer

Management, Risk & Pricing

RETAILSmart 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, discount targeting. Life time Risk Value, Actuarial Risk and

compliance

Process

OptimizationTriaging 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. Inventory and Logistics

optimization. Capacity planning & 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 InsightsSandbox & Architecture

Sumyag Insights


Sumyag InsightsTypical 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 processing between systems, stateless

» Data Driven Intelligence > use Data based

configuration to make system adapt 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 tech obsolescence through use 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 InsightsTeam and profiles

Sumyag Insights


Sumyag Insights

Milind [Data Sci]Technologist with 16 years of experience in

Infrastructure and firmware design. Milind

has been leading Data science initiatives for

the past 3 years in AIG and currently in the

startups that he has founded. Milind has

worked with brands like AIG, VMWare.

Milind consults on data center design,

Model Engineering and Validation and

designing insights for the enterprise.

Manish [ Org Change]Manish brings 20+ years of experience in

Agile, Lean transformation having worked

with WIPRO and other organizations.

Consults on Self Development

workshops, Change Management,

Disruptive innovation

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 value driven innovation

and transformation through Digital and

emerging technologies.

Data Science

Sumyag Insights our sister firm

forms the talent pool and services

arm for Data Science, Digital and IOT.

The team is currently 7 resources

with a network pool of 10+

resources

Digital –Web Apps

Sumyag Insights has tied up

companies that bring Digital we b

development capabilities at scale,

and this will be led by Venky and

Sudip

DEVOPS

Sunyag Insights has signed up 2

companies with 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.

Consulted CIOs and CXOs on IT

operations and business service

management.

Graduate in Mechanical Engineering.

Sandeep [Process Change ]12 Yrs. of experience in developing and

implementing value based strategic

initiatives in various industries to improve

human and process performance. Played

COO role in e-commerce and IP licensing

industries.

Post Graduate in Management with

graduation in Engineering.

Venkat [ IO T – Firmware ] 21 years experience in deep embedded

and firmware engineering 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 , Insurance Claims 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 InsightsProposal 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

ResearchLow ~ 0 Cost

Discover solution

Propose / Go-NoGo

Define commercials

Design DevCore Technology

Agile – iterate

2 Week Sprints

New and Change

Capacity based Price

Maintain Operate Reduce Cost

Reduce Capacity

Ensure Upkeep

Time

Co

st /

Bu

rn R

ate

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

• Formal and quantitative methods

• Modelling , Simulation, Solution Discovery

• Showcase possibilities & Skills of the team

• Mashup outcomes

Data & Scrambling• Get Sample Data Sets, & Wrangling

• Gauge Business context & Issues

• Outcome Expectations

Client – Workshops – Finalization • Risks and Costs Definition

• Deeper insights and options for decisioning

• Client Workshops and finalise Engagement Model

• Define Commercials & Benefits

Rapid Setup

Short timeframe

1-2 weeks

Low Cost Multiple Pathways

Understand Risk Control Go 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 Models No 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

� Product Management

� Back log, Scrum and 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

Infrastructure Data Science Infrastructure

Design Deploy and Deliver

for Education Services

Robotic Process

Automation in Insurance

Policy Management

Document extraction and

Intelligence in Insurance

IoT Sensor Pipeline Design

Deploy and Intelligence –

Large Indian Customer

Smart Spaces – IOT

Intelligence solutions

Responsive Digital

Application – Insurance

Sumyag Insights


Sumyag InsightsShowcase: 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 YOUR DATA

• 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

GENERATE INSIGHTS

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 InsightsShowcase: Science on the Cloud – Science Research Sandbox

• Node on the Cloud – Standard Large 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 InsightsShowcase: Document – Text Analytics In Insurance

20%

80%

80% enterprise data is

unstructured, In the

form of Documents,

Email, Text, Logs

FORMATS1. Legal - Contracts

2. Communication

3. Information

4. Research – Reports

5. Machine Logs

6. Interactions – Chats

Media Not even considered for Business Information

Complex nature of the

data and the challenges ,

achieving accuracy and

costs in processing has

lead Enterprise to 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 finally Deriving 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 InsightsShowcase: 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 Retail Store

• Sensors & Controllers

• LED Panels for playout

• Cloud Services

• Data.Ai - Intelligence

• Web Applications

• Presentation BI

• Control Admin

Sumyag Insights


Insurance Value Chain – Our Experience

Customer Management Claims Management Actuarial Pricing / RiskCustomer Retention, CLV, Market Basket Analysis,

Segmentation & Targeting, Digital marketingClaims Triaging – STP, Claims Fraud,

Claims litigation, Subrogation,

Loss models, Catastrophe Risk, Price optimization

Surrender / Lapsation model to understand

customer propensity to surrender based on

demographics, Psychographics,

US – Personal 5 MM Customers, 4 Bn USD Portfolio

Logistic Regression with Splines on Python R Spark

on Hadoop Clusters

Customer retention Model for P&C, life multiple

regions

Indian Insurer 4 Regions, 500 Mn GWP and 10 Mn

customers. Complete end to end utility, paid based

on premium collected.

Clustering and logistic regressions

Claims Litigation – Propensity to litigate and

sensitivity to claims value

US – Worker compensation services, TPA.

Logistic Regressions developed on R and Python

Claims Allocation and straight through processing –

lining up claims to agents based on skills

Proximity based on linear Clustering

Telematics, Auto Insurance Scoring of Drivers for

Risk valuation and thereby pricing and customer

segmentation

2 projects, in USA on AWS and Python / Java and

the other with Partner MyDrive on Hadoop

Year to date Loss prediction function for P&C

insurance on Hadoop

Simple calculator to summarize YTD loss by various

products, regions, clients , speeded up using

Hadoop / HIVE

Finance / IR � Investor Reports Dashboard with

sentiment and entity Extraction

130 Analysts, per Quarter, , sifting through ~ 1500

Documents. Extract Entities & sentiment using NLP

to understand analyst views for CFO perusal

Marketing Spend Optimization and Market Channel

Management – Market basket Analysis

Time Series, Linear Correlation between Spend and

Sales

Claims Subrogation –what is the possibility of

counter party Insurance claims based on

Time Series, Linear Correlation between Spend and

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 � Data quality 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. InnoservicesSister firm. Consulting & Operations

Unmithy InnoservicesHYDERABAD | BENGALURU

27

Unmithy Services

virtualCOECOE 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

virtualLeadershipVirtual 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 business decisions / /

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

TransformationTransformation 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]

» Proven transformation framework and agile

execution methodology

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

SIGNTH!NK!NG

ED

Unmithy Services

InsightsBig 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


[email protected]

Sumyag profile deck

Business

Transcript of Sumyag profile deck