Anurag Sinha talk about: Leverage Data Strategy as a Catalyst for Innovation

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Leverage Data Strategy as a
Catalyst for Innovation!
Jan 2023
Anurag Sinha, Director, Data & Analytics

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CONTENT
01 INNOVATION
02 WINNING DATA STRATEGY
03 USE CASES
January 2023

3. 3
20+ 350+
Years of experience
15+
Industries
580+
Customers Projects delivered
Global Perspective
We have delivered 100s
of millions of $$ of value.
Outcomes & Commercial Value
• Build a successful strategy
• Develop a great product
• Adopt an Agile approach to execution
Consulting Firm / Product Development
Focus on Innovation

4. Innovation
Jan 2023
INNOV A T ION
Why innovate?
What is Innovation ?
Use Cases
How is innovation linked to
Data ?
Top deterrents &
challenges.

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Innovation
[inəˈvāSH(ə)n] noun.
‘Something (process, product or service)
original or improved which creates value’
‘Execute an idea which address a specific
challenge and that produces value for the
enterprise and the client’
What is Innovation?
Substantial.
Scalable.
Repeatable.
Incremental.
Disruptive.
Sustainable.

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Innovation with data has always been
essential to resilience & value creation.
Innovation has always been essential to long-term resilience as it creates countercyclical and noncyclical benefits (e.g. revenue streams) however in times
of deep uncertainty, companies must carefully balance short-term innovation (aimed at cost reductions) and potential breakthrough innovation bets.

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Breakthrough innovation use cases (aimed at transformation) proved to be lower
risk as opposed maintaining status-quo projects.
Pervasive uncertainty is a good opportunity for companies to look for diversification or expansion opportunities. Structural supply-chain issues, rising
interest rates, and sustainability challenges are just a few conditions that have become the new norm and hold critical implications for business models.

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In a nutshell..
• Lack of cohesive data strategy to anchor
outcomes.
• Innovation is perceived more-risky, especially as
it pertains to AI.
• Multiple data sources, lacking proper
governance.
• Architectural surrender & history - incumbents
and lock-ins.
• Sustainability and retention of high-value talent.
Source – CIO ROUNDTABLE Summer 2020
8%
8%
8%
23%
23%
31%
38%
54%
54%
69%
0% 20% 40% 60% 80%
Lack of technological infrastructure to support…
Lack of available data
Limited usefulness of data
Lack of Data ownership and governance
Under resourcing for AI in line organization
Lack of quality data
COVID-19 disruption & post-affect
Lack of a clear Data strategy
Lack of talent required for Advanced Analytics…
Uncertainty of return on AI investments
Incumbent Challenges to Innovation
Top deterrents, cost of uncertainty and why most innovation efforts fall short.
KPI Digital – All Rights Reserved

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Resetting aspirations
based on current viability.
To improve operations &
supply chain
Discovering ways to differentiate
move into adjacencies.
Choosing the right portfolio
of initiatives
Comply with regulations
and maintain reputation
and trust
Evolving business models
To achieve environmental
sustainability
KPI Digital – All Rights Reserved
Data strategy helps you drill down to your core business
needs and create an achievable plan (roadmap).
Data is embedded in every aspect of innovation. By 2025, every decision, interaction & process will come from data and most employees will use data to
optimize nearly every aspect of their work.
Extending efforts to
include external partners.

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How do build a winning data strategy
A repeatable framework for Innovation with focus on business transformation through data.

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1.
Understanding your vision,
current state & your
stakeholders.
DISCOVERY
We believe every organization today is trying to find a
balance between core business objectives AND data
challenges which need to be mitigated in order to deliver
and innovate.
At KPI, our method takes current state, challenges and
business objectives (vision) as input and produces
categorical deliverables across strategy, execution and
Roadmap (next slides).

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2.
Developing a global data
strategy and
implementation plan.
STRATEGY
Using Business strategy & existing infrastructure as drivers,
Data strategy outlines the long-term vision for collecting,
storing, sharing, usage and monetization of your data.
Our accelerated approach to data strategy works with
your vision and describes business value, architecture
(platform), benefits & ROI of a data-centric approach.
This Data Strategy further informs a commercially sustainable Roadmap & specific initiatives
to improve customer experience, attain analytical maturity & create an org-wide data culture.

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Executing use cases &
delivering capabilities,
iteratively.
EXECUTION
Our execution method is based on Agile 4.0/SAFE and
prioritizes delivery of use cases over longer-term incubation
projects. (Deliver high-er priority soon-er)
Velocity & priority of individual sprints is adjusted per sprint
to ensure there’s consistent focus on ROI & longevity of the
solution.
USE CASE JOURNEY MAP
3.

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Execute on Governance
strategy (Part 1) & establish
COE.
GOVERNANCE
An effective governance program enforces Policies,
Compliance, Principles and best practices within your
organization.
Its described in 5 subject areas:
- Data Quality Framework
- Data Policies & Standards
- Data Security, compliance & Privacy
- Information Architecture (Data Catalog)
- Reporting & Analytics (Standards, Patterns)
Develop the ownership, stewardship, and operational
structure needed to ensure that corporate data is
managed as a critical asset and monetized in an
effective and sustainable manner.
4.

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MVP
Minimum Viable Product
- Wireframes & Dashboards
- AI Use Cases
MVC
Minimum Viable Product
- Conceptual Enterprise Data
Model (CEDM)
- CEDM Taxonomy
- CRUD matrix
- Business Requirements
Document (BRD) / Stories
MVM
Minimum Viable
Capabilities Map
- Capability Map
- ‘To Be’ Process Model
(BPMN)
- SIPOC
MVG
Minimum Viable Product
- Data Governance
- Master Data Management
(MDM)
MVF
Minimum Viable
Foundation
- Modern Data & Analytics
Platform on Cloud
- Templates
- DevOps
- Data Pipelines
- Cloud Configurations
- Secure Cloud environment
(landing zone)
MVA
Minimum Viable Agile
- Project planning, project
reporting, Agile and
forecasting
MVR
Minimum Viable Roadmap
MVS
Minimum Viable Solution
Architecture
Key Deliverables
and the MVx…
The MVx
(Minimum
Viable x)
No MVx on these, since
they depend
on MVC
- Dimensional Enterprise
Data Model (DEDM)
- Messages
- Specifications
- Tests Cases
KPI Digital – All Rights Reserved

16. OUR MODERN & TRUSTED DNA PLATFORM
Governed, Real-time, Bi-Modal, Cloud based (elastic) and support ALL types of data and analytics.
Reference Architecture

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Technology
Partnerships
Strategic and purposeful partnerships across
all layers of Data Supply Chain to deliver
award-winning solutions with seamless
execution.
We also help our customers with alliances
and joint ventures that enable companies to
rapidly test and scale new business
models/offerings that would take a long time
to develop organically.

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The KPI Data & Analytics Offerings.β

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Vertically Integrate
A.I. Solutions
Cost and accuracy INCREASE with
sophistication of models, also
increasing impact/sensitivity to errors.
The approach must remain modular
and adhering to varying degrees of
sophistication, from simple POC’s to
fully-monitored production-grade
models.
E X A M P L E P A T T E R N
9.1.1

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Simplification of ETL
processes &
Modernization of BI.
Lakehouse
Metrics
Store / API
U S E C A S E S :
ETL Footprint Reduction
Realtime Events
Secure Mission critical pipelines
Re-platforming for BI
Interactive self serve for users.
Chatbots & other microservices.
Foundations for AI
Planning, Budgeting, Forecasting
Analytics Driven Accounting
Churn Management
6.4.0
E X A M P L E P A T T E R N

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By
Anurag
Sinha
for
KPI
A word on effective facilitation through workshops
Eg. Virtually Facilitated
with
Dynamic Whiteboarding
Real time Interactivity
Global Participation
Stakeholder alignments, early and often – remains key to critical momentum required for Innovation at scale.
PROBLEM SOLVING & INNOVATION WITH DATA.
Discover – Strategy – Build – Implement – Govern.
o Focus on specific use cases and/or capabilities with tangible outcomes.
o Initial stages may include quick prototyping against specific goals established by the
business owner.
o Produces clear/concise sets of use cases and in some cases followed up by a
working proof of concept.
o Focus on building a MVP strategy framework which enables ALL types of use cases.
o Initial stages generally includes discovery, prioritization and stakeholder alignment
centric goals.
o Produces tangible artifacts to support internal stakeholder alignment, RFP’s,
implementation roadmaps and playbooks.
BUILD FOUNDATIONAL DATA STRATEGY.
Discover – Strategy – Align – Document.
Workshops
A clear path to viable outcomes.
C I T A T I O N
Phaal, Kerr, Ilevabre,Farrukh,Routley, M., & Athanassopoulou ON 'SELF-FACILITATING' TEMPLATES FOR TECHNOLOGY AND INNOVATION STRATEGY WORKSHOPS.

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Presentation Title
Header – Title.
Use Cases (in active development)
Jan 2023

23. Upsell and cross-sell
of seasonal products,
which includes:
Customers segmentation
Campaigns (promoting
products) for specific
customers segments
Seasonal Products Bundle
Offerings
Product Recommendations
at time of ordering
Sophisticated Sales
Dashboards
Complete view of the
Customers, the
Products & Services,
which includes:
DG & MDM on Customers
DG & MDM on Products &
Services
DG & MDM on relevant
reference concepts such as
Market, Territory,
BU, and so on
Turnkey A.I. &
Continuous
optimization which
includes:
Sales Forecasts
Pricing Optimization
per Market
Expanding Service
Offerings Catalog by
combining Seasonal
Services and Seasonal
Products
Amongst others..
How to
champion
innovation
internally ?
Use Cases of interest
(an example)
KPI Digital – All Rights Reserved 23

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• A consumer products company
wanted to use consumer data to
make intelligent decisions for product
development and marketing.
• Their data architecture did not
support gathering, orchestrating and
managing the consumer data
available from internal and external
sources
• They also lacked real-time access to
insights at the correct granularity to
support decision-making for key
stakeholders.
CHALLENGE
• Built a robust data mesh foundation
using agile cloud data warehousing
practices.
• Developed scalable, fault tolerant data
pipelines to collect, ingest and
transform structured and unstructured
consumer data from all sources
• Fed data into real-time dashboards and
reports as well as self-service
analytical tools.
• Established governed platform to
support data exchange between
partners, producers and consumers
SOLUTION
• Better alignment of corporate
strategies and business models with
current consumer trends
• Created organizational resilience and
agility to respond to evolving consumer
demands
• Data mesh architecture can morph into
a framework for domain-specific use
cases as the company’s data maturity
increases
• Governance layer creates regulatory
compliance for expansion to global
markets.
RESULTS
Data Mesh for Consumer Insights
C P G

25. • A major retailer sought to improve the
customer shopping experience and
increase customer loyalty through data-
driven insights.
• Lack of governance for data assets that
crossed multiple systems and channels
and put the company at risk of data
breach and financial penalties.
• Complexity of data platform, left
business users unsure of which data to
use for financial and business analysis
slowing decision-making across the
company.
CHALLENGE
• Created a searchable data catalog with
a data dictionary and business glossary
to help business users understand and
find the right data for analysis.
• Identified and cataloged all customer
personally identifiable information (PII)
/ financial data and established policies
and procedures.
• Created an accountability structure that
defined the access and acceptable use
for data.
SOLUTION RESULTS
Governance for Data-Driven Decisions
R E T A I L
• Finding the right data led to better
customer insights that improved
customer experience and increased
customer loyalty.
• Knowing where PII is stored in across
all systems ensured compliance to
privacy regulations and reduced
financial and reputational risk for the
company.
• Accountability structure strengthened
data security and provided an extra
layer of protection against data breach.

26. • A healthcare organization wanted to
use data and AI to improve patient
outcomes and reduce the cost of
care.
• Their current data architecture
contained data that was siloed and of
questionable quality.
• Their data platform could not scale to
store and process data sets due to
size and complexity.
• It was difficult to make insights
available and actionable in the clinical
setting.
CHALLENGE
• Built a unified cloud data analytics and
AI platform to ingest, validate, cleanse
and integrate diverse data at scale.
• Helped to train and mentor an in-house
data analytics team to provide data
science, analytics, visualization and
monitor data quality.
• Created standards and reusable
templates for presenting insights to
clinicians.
• Established robust governance and
audit processes to improve data
quality, identify algorithm bias and
validate models.
SOLUTION
• Combined data from multiple sources
such as EHRs, genome sequencers and
medical imaging devices and presented
insights to clinicians as intuitive reports
and interactive dashboards.
• Allowed clinicians to make earlier,
better diagnoses and identify targeted
treatments, reducing the cost of care
while improving patient outcomes.
• Identified opportunities to streamline
administration and create operational
efficiencies.
RESULTS
Data Architecture for AI Success
H E A L T H C A R E

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Planning, Budgeting,
Forecasting
Fraud Detection
Billing & Debt
Collection
Revenue Growth
Cash Flow
Management
Risk Scoring &
Valuation
Credit Card & Loan
Origination
Analytics-driven
Accounting
Driving Strategic Business
Objectives & Value.
Use cases across the enterprise.
FINANCE & RISK
MANAGEMENT
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Driving Strategic Business
Objectives & Value.
Use cases across the enterprise.
Product Management
Predictive Sales
Scoring
Increase Upsell / Cross-sell
Dynamic Pricing Optimization
Churn Management
Predictive Lead Scoring
Find Look-alikes
Realtime
Personalization
Content Generation
Sales Forecasting
Customer
Segmentation
Customer Journey Analysis
MARKETING &
SALES
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Driving Strategic Business
Objectives & Value.
Use cases across the enterprise.
Procurement &
Spend Control
Smart Vehicle Routing
& Fleet Management
Demand
Forecasting
Supply Chain Optimization /
Mgmt. (incl. Alternate SP)
Autonomous
Transportation
Inventory
Optimization
SUPPLY
CHAIN
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Driving Strategic Business
Objectives & Value.
Use cases across the enterprise.
Yield Optimization and Simulation
Production Environmental
impacts Minimization
Predictive Maintenance
Quality Assurance
Intelligent Accident Prevention
Production Planning
Production Optimization
via Industry 4.0
Overall Equipment
Effectiveness (OEE)
Energy & Throughput
Efficiency
Production Monitoring
Intelligent Quality Control Product Development Cycle
Optimization
PRODUCTION
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Driving Strategic Business
Objectives & Value.
Use cases across the enterprise.
Training
Performance & Risk Management
Analytics-driven Hiring
HR Retention Management
HUMAN
RESOURCES
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Performance Appraisal Career Management

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Driving Strategic Business
Objectives & Value.
Use cases across the enterprise.
Intelligent Chatbots integrated
to Intelligent Call Routing
Customer Service
Satisfaction Management
Call Intent Discovery and
Customer Service
Response Suggestions
Social Listening
Customer Identification
(voice authentication …)
Customer Ticket
Management
CUSTOMER
SERVICE
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33. Confidential & Proprietary
Identifying where you are in the Data & AI maturity curve will help inform your path forward
We help build a
commercially sustainable
data strategy and
accompany our customers
in their transformation
journey

34. Now is the time to choose an innovation portfolio,
discover fresh insights and opportunities, and evolve
your business models.
Thank you!
Please reach out to discuss further.
Anurag Sinha – Director, Data & Analytics
Anurag.Sinha@kpidigital.com