Inawsidom - Data Journey

© 2023, Inawisdom Ltd.
INTERNAL ENABLEMENT
DATA DRIVEN AND MATURITY
JOURNEY
20th February 2023, v1.0
Data Driven and
Maturity Journey
© 2023 Cognizant | Private
November 2023

2
Cognizant’s UK&I specialist AWS Data & AI Team
INAWISDOM
Found in 2016, an AWS Partner since 2017 and Premier
Partner since 2019. Inawisdom was acquired by Cognizant in
2020 and is part of Cognizant’s UK&I Consulting
Inawisdom lives and breath AWS including holding over 180
AWS certifications and accreditations. Inawisdom maintain a
close relationship with the AWS team, supporting and staying
up-to-date with all the latest developments.
Inawisdom has been awarded in the following
areas:
► ML Partner of the Year 2020
► Differentiation Partner of the Year 2019
► Global Launch Partner – CCI
► Launch Partner – AWS UAE Region
Inawisdom holds 9 competencies and service designations, reflecting business-
wide expertise in key areas:
Our Qualifications
All of our consultants hold at least 1
AWS certification. Including some
consultants with all certifications
Our CTO has been ranked #1 AWS
Ambassador in EMEA in 2021 and
2022

Your Data, AI and Machine Learning partner
WHY COGNIZANT
We offer a rapid,
proven path to
Machine Learning
excellence
We help customers in
a broad range of
industries achieve
their ML goals
We are recognised,
all-in AWS experts,
focused on customer
success
We offer full-stack
services, including AI /
ML, Data & Analytics,
BI and MLOps

Full-stack capability
OUR SERVICES
Business
Differentiation / Value
Data Driven
Business Decisions
Cloud Transformation
Adoption and Scale
Digital
Enablement
AI and Machine Learning
Data & Analytics
Data Foundations
Cloud Infrastructure
Landing Zone, Control Tower, migration

Discover. Deliver. Productionise. Scale.
ACCELERATE AI/ML ADOPTION ON AWS
D
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Scaled AI Productionise
Operationalise
Discovery
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Data Science
AI/ML
Data
Engineering
& Platform
DevOps &
MLOps
Cloud/Data
Architecture

6
A product lead data approach
MODERN DATA ECO SYSTEM
What business
problems are you
trying to solve?
• Performance KPIs
• Analysis
• Visualization
• Publication
• AI/ML
• Automation
• Optimisation
• APIs
What data do you
need and where
will it come from?
• Operational systems
• Streamed events
• Legacy DWH
• IOT
• Database replication
• Real-time or batch
• APIs
• External
• Manual input
What needs to be
done to the data
to make it
useable?
• Business rules and
logic
• Cleaned and
conformed
• Data lake
• Data warehouse
• Data marts
• Business domains
Data Sources
Data Products Data Processing
• Capability
• Recruitment
• Resourcing
• Training
• Ownership
• Agile working
• Infrastructure as code
• CI/CD
• Documentation
• Data mesh
• Trusted & quality assured
• Secure
• Understood
• Managed
• Compliant
• Tool and service choices
• Ingestion patterns
• Pipeline designs
• Transformation methods
• Data modelling
• Timescales
• Prioritisation
• Dependencies
• Progress
• Communication
Supported by
Data Platform Delivery
People & Skills Operating Model Data Governance Technology & Architecture Planning & Roadmap

•Many organizations talk about being data-driven,
but few know what this concept really means
•A data-driven organization identifies the insights it
needs data to inform, effectively manages that
data, and empowers its team to use the data
forrester
”90% percent of the world's data was created
in the last two years. And every two years, the
volume of data across the world doubles in size.”

We want every business to be transformed by insights and use
Data & AI as part of their DNA

ONS : GDP for UK ONS : Consumer Price Index
with Household Costs for UK (last 35 years)
Worse performing G7 Nation & Highest inflation in 35 years
UK ECONOMY
Our clients are hurting right now:
• They are facing high wage demands
• Low grow in revenues
• Higher costs of sale/supplies
• Regulation uncertainty post Brexit
• Lack of subsidies and investment
Predictions:
• IMF : Technical recession of 0.3% in 2023
• IMF : GDP Growth 1% in 2024
• Inflation to drop down 6% in 2023, 2% in
2024
• Next UK General Election Autumn 2024
A Focus on Growth will return:
• Investing in a Data Journey will give our
clients a vehicle for growth
• We should expect to have conversations
with our clients to look at innovation Q3&4
2023 to secure POVs in Q1 2024

Current Benefits to Business
ANALYTIC, AI AND ML BUSINESS OPPORTUNITIES
Bottom line
benefits
Top line
benefits
New revenue
streams
Increase customer
wallet or market share
Brand perception
or USP
Improve customer acquisition,
conversion and retention
Compliance Better assessment
of risk
Intelligent Process
Automation
Optimisation and
Efficiency Gains
2023
(Efficiency)
2024
(Growth)

DATA & ANALYTICS MATURITY
Data & Analytics Maturity
Business
Value
Reliable
Data
Automated
Reporting
Data
Strategy
Sporadic
Reporting
Core
Data
Platform
Ad-hoc
Data
Exploration
Discoverable
Catalogue
Predictive
Modelling
Automated
AI Action
Prescriptive
Insights
Streaming
Analytics
ML + Data
OPs
Data Modernisation
Data Monetisation
Generative
AI

High-fidelity events and perishable insights allow you to drive most value
VALUE VS TIME
Time Since Creation
Business
Value
Descriptive Analytics
Real
Time
Seconds Minutes Hours Days Months
Diagnostic Analytics
Predictive
Analytics
Automated
Action
Prescriptive
Analytics
Data Age

Discover and Prove Value first, then Realise and Maintain value once proven
VALUE VS ACCURACY
Time
Business
Value
Discover
& Prove
Realise
& Maintain
• Value grows with accuracy, however
there is a break even point
• The break even point is different per
use case
• Accuracy requires investment in time,
skills and resources
• Beyond the break even point no
more value can be unlocked for the
ROI
• Maintaining accuracy maintains value
otherwise value will be lost
Unmaintained
Accuracy
Data & ML OPS
Maintained

16
DATA EXPLOITATION ACCELERATION
Ingest, transform, manage and exploit any type of data
Inawisdom recommends a “thin slice”, end to end as the initial use case to provide value quickly. This functional
diagram covers all data platform aspects corresponding to accelerator components that speed up implementation

17
EXAMPLE LOGICAL ZONE ARCHITECTURE
Data Lake
Exploitation
Layer
Sanitised
Data sanitising rules
applied, converted to
efficient Parquet format.
Data structured and
catalogued for Data Lake
analytics use cases
Curated
Data in Normalised
and/or Data Warehouse
model. Designed for
broad consumption and
use cases. Contains
intermediate data
products
Optimised
Data Marts, Views and
optimisations applied for
specific Teams or use
cases. Modelling of this
layer aims for high
performance and contains
data products
Amazon Athena
Landing
Data ingested in either
push or pull model into
Landing zone. Limited
structure and in same
format as source.
Amazon S3
AWS Glue
AWS Lake
Formation
Amazon Redshift
Data
Sources
Amazon
SageMaker
Amazon Redshift
Amazon S3
AWS Glue
REST API
SQL
Amazon QuickSight
AWS
Lambda
AWS IoT Core
AWS Database
Migration Service
Kinesis

Scale BI / AI Capability and Decentralization
➤ Scale analytics by adopting a federated,
mesh style approach
➤ Empower disparate teams
➤ Reuse data products and assets
➤ Drive strategy, governance & training
➤ CoE to provide technical data and analytics
support where needed:
§ Knowledge sharing
§ Best practices
§ Examples
§ Training / upskilling
§ Consulting
§ Evangelists
OPERATING MODEL
Excellence
Manufacturing
HR +
Finance
Supply
Chain &
Logistics
Data
Science
Enablement
CoE
Risk that a centralized BI/Data team become the bottleneck
and unable to keep up with business growth & demand
After Sales
Support

19
All Analytics Solutions follow the six pillars of the AWS Well Architected Framework. We
analyse people, processes, and technology to make informed decisions on design,
technology, and architecture. We also optimize computing services to reduce waste and
monitor performance with KPIs.
AWS WELL-ARCHITECTED APPROACH
Code Efficiency
SUSTAINABILITY
Data Design &
Usage
Software
Application
Design
Platform
Deployments &
Scaling
Data Storage
Utilization
CI/CD
OPERATIONAL
EXCELLENCE
Runbooks
Playbooks
Game Days
Infrastructure
as Code
Root Cause
Analyses (RCAs)
Right AWS
Services
PERFORMANCE
EFFICIENCY
Storage
Architecture
Resource
Utilization
Caching
Latency
Requirements
Planning and
Benchmarking
Service Limits
RELIABILITY
Multi-AZ and
Regions
Scalability
Health Checks
and Monitoring
Networking
Self-healing &
Disaster Recovery
Reserved and
Spot Instances
COST
OPTIMIZATION
Volume Tuning
Service Selection
Consolidated
Billing
Savings Plans
Decommissioning
Identity and Key
Management
SECURITY
Encryption
Service Logging
and Monitoring
Dedicated
Instances and
Outposts
Compliance
Governance

20
MACHINE LEARNING ARCHITECTURE
RAMP (Platform-as-a-Service) provides a ready-made machine learning (ML) accelerator development platform, pre-configured with the
latest ML libraries and scalable training environment, with capability of early access to Bedrock and Generative AI Foundation Models
(FM) from SageMaker.
The platform is able to ingest data from a wide variety of sources, e.g. on-premise SQL Server, remote API sources etc. effectively creating a
Data Lake required for data exploration and exploitation.
Predictive models developed in this environment will be portable to other environments for production deployment using ML Ops or within the
RAMP platform; offering flexibility. Recommendations on the optimal way to train and deploy Generative AI solutions will also be included.
Where models are developed that need to be maintained, this can be automated / productionised via ML Ops best practices.
Why use RAMP in this project
• A platform that has all the tools and
resources to develop the proposed
solution
• Data Security and Privacy: RAMP uses
VPCs to keep data and processing secure.
Extremely important when processing PII
• Flexibility for MVP – scaling up to a
Commercially Ready product is
straightforward
• Access for the customer
• Easy upgrade to Prod

22
Define use cases and drive value by outcome
focused delivery
ML AND DATA VALUE FLYWHEEL
Realise
Maintain
Evolve & Scale
Data Sources
Embed within
business &
visualise
Structured, Semi-Structured
and Unstructured data from
Internal, External, and other
sources
Get stake holder commitment,
build a roadmap around value
and start the first flywheel for
the highest impacting but
deliverable use case
Discover
Business Case
Creation, Exploratory
Data Analysis, &
Target Opportunity
Definition
Use Cases
Prioritise & Value
Business + Data Strategy,
Ideation for descriptive to
predictive to prescriptive use
cases, and Opportunity Scoring
Prove
Experiment and
show potential
value
Improve
model(s),
refine data
products &
create MVP
Deliver value to
the business
Maintain value
to the business
Data & MLOps,
maintain data &
models with
automation and
pipelines
24/7 monitoring,
Incident Response,
& Cost Optimisation
Respond to changes
and detect drift
Scale up and refine
capabilities to accelerate
the delivery of value with
more & faster flywheels
Improve reuse and
collaboration using
tooling such as a
model registry and
a Business Data
Catalogue
Standardise
approaches to
common problems,
provide governance
Change business processes
and refine operating model to
be data-driven with easy
access to insights
POV with initial,
data products,
features creation &
model selection
Measure & Iterate
Measure each iteration of the
flywheel against CSFs / KPIs
and only invest in further
iterations as needed
Roadmap
Value

Value Driven Transformation
OUR BEST-PRACTICE APPROACH
Strategy,
Road
Map
&
Opportunity
Scoring
Experiment
Discover Prove POV
Experiment
Discover Prove POV Realise MVP
Experiment
Predictive Use Case
Data & ML Platform
Diagnostic Use Case
AI in Action Use Case
Maintain
Exploit
X
Business
Lead
Technology
Lead

Delivery your roadmap to unlock value and empowering your team
to become data driven
PATHWAY TO SUCCESS
Inawisdom Lead
Centre of Excellence
Value
Number of use cases
Customer Lead
Enablement & Empowerment
Weeks Months
Foundations
Data & ML Platform
Maturity
ML & Data OPS
Delivery of Data Strategy & ML Roadmap
Literacy
Culture & Transformation

26
CASE STUDY
The Customer:
AI in Action: Enabling key data insights in financial services
The Result:
The Solution:
The Requirement:
Ø Migrated data from SAS to AWS, creating a robust Data Warehouse and
Data Lake
Ø Delivered a Data Warehouse model in just 12 weeks, enabling key
visualisations that were previously not possible
Ø Ensured data was properly sanitised, to meet regulatory requirements
and improve data governance
Ø Deployed an ML Sandbox to enable rapid, secure testing and exploration
Ø A robust, scalable solution that delivers accurate data insights and
enables further exploration of ML use cases
Ø A secure platform that can be managed and built upon by the in-house
data engineering team
Ø Better customer insights including better understanding of risk profiles,
enabling increased loan acceptance whilst minimising potential defaults
The Sector:
Improve data analytics capability, to enable faster,
data-driven decision-making across the business
Financial
services

27
CASE STUDY
The Customer:
AI in Action: Using AI and ML for Predictive Maintenance
The Sector:
The Result:
The Solution:
The Requirement: Predictive Maintenance service offering - to deliver
innovation and increased value to vehicle owners and operators
Construction Equipment
Manufacturing
Ø Build of an IoT-enabled Data Lake to ingest sensor data and alerts from
250,000 vehicles across the EMEA and American markets
Ø Utilised AWS Control Tower to ensure AWS account management, security
and automation for the customers, using Guardrails for governance
Ø Ingested data via AWS Core IoT into the common store
Ø Transformed the data to an efficient, common format within a single logical
database, with querying and reporting capabilities
Ø Driving the evolution of new data-led solutions for vehicle owners, dealers,
manufacturers and operators
Ø Expanded data to include vehicle and devices from the Asian market
Ø Created a roadmap to enable enhanced service offerings including:
§ Implementation of real-time analytics and alerting to customers
§ Manage other devices and manufacturing data sources
§ Machine Learning for accurate predictive maintenance

28
CASE STUDY
The Customer:
AI in Action: Enhanced Analytics and visualisation
The Sector: Global
Manufacturing
Ø Customer Satisfaction – data insights available within minutes instead
of weeks, enabling faster, more accurate decision making
Ø Cost Efficiency and Environmental gains – ML provided
recommendations on how best to optimise plant yield
Ø Operational Efficiency – significant reduction in manually-intensive
analysis, saving time, resources and money
The Solution:
The Challenge: Optimise the plant yield in formaldehyde production
using advance data analytics
Ø Provided a secure and scalable data store, with existing data
migrated into the new platform
Ø Delivered a robust, scalable platform and a secure API to allow the
upload of data; this data is then passed through various data services
Ø Provisioned for the data to be utilised for further insights, through
visualisation or Machine Learning
The Result:

29
CASE STUDY
The Customer:
AI in Action: Solar Plant Optimisation
The Sector:
The Result:
The Solution:
The Requirement:
Ø Using AWS edge device functionality (IoT Greengrass) with modern
industrial protocol (OPC-UA) to stream data from SCADA servers into
AWS, including handling any drops in connectivity & optimising upload
Ø Implementing a multi-tenant Data Lake House to store each portfolio
companies' data in
Ø Creating Machine Learning models to predict the optimum output
based on historic data for each plant
Ø Visuals to show power output vs optimum output from portfolio
company level down to an individual panel level
Ø Plant Managers were able to detect dead panels and replace them, or
adjust the tilt of underperforming panels to increase output
Ø The Investor was able to demonstrate to the portfolio companies value
from their relationship
Investor in
Renewable Energy
To optimize the production of Solar Energy from
plants located around the world

30
CASE STUDY
The Customer: The Sector: Steel
Manufacturing industry
The client will be launching the core service to their customers in April 2022.
Further phases are underway to provide valuable insights into production
statistics and error logs from devices through a search capability. Machine
Learning analytics will then provide predictive maintenance alerts.
The Solution:
The Requirement: Leverage Internet of Things (IoT) functionality to receive
data from machine owners, making the data available to their new Mobile
Apps and Web interface. Foundation for analytics to enable better insights into
the product performance, enhance service and customer experience.
Ø Set up a secure service for connecting devices, ingesting machine status
using IoT Greengrass on the Edge and processing the data
Ø Developed a Data Lake House platform, with transformations and a rich
API so that customers can remotely view machine data and receive
notifications of any issues with individual machines.
Ø The platform is largely based on “Serverless” technologies. This was an
agile delivery, working closely with the customer product owner and front-
end team. The project took 4 months and is designed to quickly scale up to
hundreds of customers and thousands of machines
The Result:
Machine Operator
AI in Action: Connected Machines using AWS IOT

31
CASE STUDY
The Customer:
AI in Action: AWS Platform Optimisation
The Sector: Utilities
The Solution:
The Requirement: Review the organization’s AWS platform to identify
improvements in performance, security and cost
Ø Conducted a Well Architected Review of the client’s AWS-based
connected homes platform, highlighting recommendations for
improvement which were scored by criticality, cost and deliverability
Ø Performed remediation and enhancements on the platform based on the
outcome of the initial review
§ Activity was organised into 3 phases – Cost Optimisation, Security
Hardening and Reliability/Performance Enhancements
Leading energy services
and solutions provider
The Result:
Ø Within two weeks, Inawisdom identified $349k of savings against the
annual AWS costs. By the end of the engagement, this savings grew to
over $500k per annum - more than x10 ROI.
Ø This reduction in costs was enabled by:
§ Identifying unused or duplicate services and dormant accounts
§ Revising the Lambda logging and reviewing Redshift
§ Assessing compute instances and storage, compared to utilisation,
function and alignment to the organisation’s retention policies

32
CASE STUDY
The Customer:
AI in Action: AI Enabled Data Lake
The Sector: Construction
The Result:
Ø Accelerated delivery of strategic AI enabled Data Lake within 3 months
Ø Avoided additional license spend and reduced operational platform costs
Ø Enabled the CEO to have visualisation of key operational data to
provide data driven business decisions.
Ø The Data Platform formed the foundation for driving transformation;
benefiting internal business operations, customers, and JV’s
The Solution:
The Requirement: Data Lake solution
Ø Designed and built the integration of numerous AWS services into a
data warehouse within the customer’s existing AWS environment
Ø Provisioned Matillion ELT for scalable ingestion of data
Ø Produced visualisation dashboards for advanced analytics.
Ø Successfully delivered a productionised, AI enabled, Data Platform
British multinational
infrastructure group

33
CASE STUDY
The Customer:
Supporting a secure, optimised AWS environment
The Result:
The Solution:
The Requirement:
Ø Reviewed the existing AWS platform, looking specifically at security,
governance and workload mapping
Ø Implemented key improvements to enable greater operational
manageability and security, and ensure the platform is fit for purpose
Ø Transitioned platform management and support tasks to Inawisdom’s
Managed Services team – including customer support, event
management and monitoring, and maintenance of the AWS and
Tableau platform components
Ø The organisation now has improved platform security and operational
efficiency
Ø Inawisdom ensures the platform is well-maintained and fit for purpose,
enabling cost optimisation, continual improvement and a reduced
workload for the client’s team
The Sector:
Private Equity Company:
Technology, Healthcare
and Consumer Services
Identify and implement improvements to the
organization’s AWS environment and support the
transition to Inawisdom Managed Services.
Private Equity

34
CASE STUDY
The Customer:
Modern Data Platform: Enable key insights in financial services
The Result:
The Solution:
The Requirement:
Ø Following a successful 5-week Proof of Value to prove the technology,
business value and ability to work together
Ø Build cloud-native Data Platform foundation on AWS
Ø Data pipelines, landing and serving curated data with first critical data
sources (including core banking transaction data)
Ø Run an AI/ML Opportunity Scoring to identify and prioritise ML use case
Ø Implement first ML use (customer cross-selling) starting with PoV
Ø A robust, scalable solution that delivers accurate data insights
Ø A secure foundation platform that can be managed and built upon
Ø First customer cross-selling insights to drive business growth
Ø Moving forward, Inawisdom and the customer are shaping a roadmap
for 2024 to drive further adoption with the Data platform and deliver
additional AI/ML use cases supported by strong business cases
The Sector:
Build data platform foundation and improve data
analytics capability, to unlock business value via better
and faster data-driven decision-making
Financial services
Bank in Bahrain

35
CASE STUDY
The Customer:
Modern Data Platform: Enabling performance analytics
The Sector: Financial Services
Ø Automated framework for ingesting and sanitising data, enabling 100s of tables to
be ingested seamlessly
Ø Ability to onboard and empower analytics teams to self-serve and build their own
data products, significantly improving speed to market
Ø Reduced bottlenecks and constraints on Core Data Platform team
Ø End-to-end data lineage, governance and controls
The Solution:
The Requirement: Design and implementation of a scalable federated Data Mesh
platform to support the development of Investment and Performance Analytics
solutions that will improve customer experience and retention
Ø Implemented Inawisdom’s unique Capability Service approach, building a flexible
team to progress multiple workstreams simultaneously, accelerating project delivery
Ø Advised and helped shape client’s data strategy and roadmap, including establishing
an operating model and design for their Core Data Service centre of excellence
Ø Designed and built an AWS hosted data platform and data mesh architecture,
utilising a combination of AWS native services, Snowflake and dbt
Ø Leveraged Inawisdom’s proprietary accelerator tools to automate and expedite
ingestion of data sources
The Result:
UK-based financial services

36
CASE STUDY
The Customer:
AI in Action: Health and Safety
The Sector: Construction
The Result:
The Solution:
The Requirement:
Ø Data Lake using AWS Data Warehouse tool Redshift for construction
projects, containing past project data that includes budget, hours and
health and safety incident data
Ø Our exploration of the data found the number of H&S incidents was a
strong indicator of poorly run sites, and poorly run sites historically
incurred higher overspend
Ø Machine Learning model was built in Amazon SageMaker (cloud
Machine Learning platform) to predict likely number of incidents
Multinational
infrastructure group
Increase the profitability of projects by predicting
likely overspend
Ø Full Productionisation of the Data Lake including ingesting Field360
and unstructured data from Project Portal
Ø Data Lakes are now judged to be essential and are used on joint
ventures for some of the largest UK Government infrastructure
projects to provide deep insights

37
CASE STUDY
The Customer:
AI in Action: Transforming energy retail with Machine Learning
The Sector: Utilities
The Result:
Ø Automatic detection and classification of anomalous events as soon as
they occur e.g. faulty meters, energy theft, etc.
Ø Reduction in the hours spent by the operations teams investigating
Ø Significantly higher chance of debt recovery
Ø Improved customer service, retention and sales
The Solution:
The Requirement: A rapid and efficient way to detect anomalies in
customer energy consumption
Ø Inawisdom deployed its four-week Discovery-as-a-Service (DaaS)
accelerator, profiling the data to ensure predictive and anomaly
detection models could be used
Ø A data science platform was rapidly created within Drax’s existing
AWS environment
Ø The Inawisdom expert data scientists then built machine learning
models to identify anomalies in consumption

Columba House,
Adastral Park, Martlesham Heath
Ipswich, Suffolk, IP5 3RE
www.inawisdom.com
@inawisdom Inawisdom
phil@Inawisdom.com
+44 20 8133 8349
Thank you
© 2023 Cognizant | Private
Phil Basford
Senior Director – Consulting / Inawisdom CTO
Philip.basford@cognizant.com
23

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