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Inawsidom - Data Journey

Product lead data approach to modern data ecosystems. It outlines some key questions to consider such as what business problems need to be solved, what data is needed and where it will come from, and what needs to be done to the data to make it usable. It then provides examples of potential data sources, such as operational systems, streamed events, legacy data warehouses, IoT, database replication, APIs, and external or manual input. Some examples of potential data products are listed as performance KPIs, analysis, visualization, publication, AI/ML, automation, and optimization. Data data processing may involve applying business rules and logic, cleaning and conforming the data, storing it in a data lake, data warehouse or data marts. It also mentions organizing the data into business domains. Finally, it lists some important aspects that need to be supported to enable a product lead data approach, such as people & skills, operating model, data governance, technology & architecture, and planning & roadmap. This includes considerations around capabilities, recruitment, infrastructure, documentation, data security, understandability and management.

Inawsidom - Data Journey

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© 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
e
p
l
o
y
Scaled AI Productionise
Operationalise
Discovery
D
i
f
f
e
r
e
n
t
i
a
t
e
I
n
n
o
v
a
t
e
P
r
o
v
e
V
a
l
u
e
B
u
s
i
n
e
s
s
C
a
s
e
D
e
p
l
o
y
E
m
b
e
d
A
u
t
o
m
a
t
e
E
n
a
b
l
e
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

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Inawsidom - Data Journey

  • 1. © 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. 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
  • 3. 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
  • 4. 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
  • 5. Discover. Deliver. Productionise. Scale. ACCELERATE AI/ML ADOPTION ON AWS D e p l o y Scaled AI Productionise Operationalise Discovery D i f f e r e n t i a t e I n n o v a t e P r o v e V a l u e B u s i n e s s C a s e D e p l o y E m b e d A u t o m a t e E n a b l e Data Science AI/ML Data Engineering & Platform DevOps & MLOps Cloud/Data Architecture
  • 6. 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
  • 8. •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.”
  • 9. We want every business to be transformed by insights and use Data & AI as part of their DNA
  • 10. 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
  • 11. 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)
  • 12. 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
  • 13. 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
  • 14. 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. 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. 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
  • 18. 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. 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. 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. 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
  • 23. Value Driven Transformation OUR BEST-PRACTICE APPROACH Strategy, Road Map & Opportunity Scoring Experiment Discover Prove POV Experiment Discover Prove POV Realise MVP Experiment Discover Prove POV Realise MVP Discover Prove POV Realise MVP Predictive Use Case Data & ML Platform Diagnostic Use Case AI in Action Use Case Maintain Exploit X Business Lead Technology Lead
  • 24. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  • 38. 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