Solving today’s challenges
The backbone of Infocepts success rests with its globally recognized, innovative support model, which is concentrated on aggressively building depth & breadth of expertise within the key components of Data & AI success, which we refer to as ‘Foundational Components’.
Infocepts Foundational Components are supported by 4 world class Centers of excellence, whose sole focus is to serve as a ‘lighthouse’, guiding both our Clients & our Foundational Components teams. This unique system of balance ensures that – at all times – our clients & our associates have available to them, the most well-versed understanding of today, while also being able to ‘see into the future’ and plan for what’s coming tomorrow.
Centers of Excellence
Cloud & Data
Engineering
Create large scale data platforms and applications using modern cloud-native tools combined with engineering skills
Business
Consulting
Connect your transformative goals using consultative, domain, digital, data science & advisory capabilities
Analytics & Data
Management
End-to-end expertise to build next-gen data & analytics solutions using proven best of breed platforms
Service
Management
Transform large scale D&A initiatives using strong program governance practices & agile-at-scale principles
Foundational Components

Business Value Discovery
Measure the business value realised from your data & analytics programs
How it helps?
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Best practices to estimate and articulate business value from D&A initiatives
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Pre-built assessment questionnaire & templates to identify value drivers
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Business Analysis framework templatized by domain & business functions that help unearth the real problems
Why?
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Measure & communicate ROI from D&A initiatives
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Help prioritize use cases objectively
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Empower stakeholders to take better, informed, & faster investment decisions

Modern Architectures
Leverage modern Data Architectures and design paradigms to implement future-ready Data and Analytics platforms that power next-gen business competency
How it helps?
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Architectural templates for large transformation & modernization initiatives
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Component architecture approach to drive consistency across multiple D&A initiatives within the Enterprise
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Facilitate transition from old to new while maintaining interoperability between old and new systems where required
Why?
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Reduce time to insights
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Ensure consistency in D&A Platforms
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Ease of interfacing with external and internal systems

Modern Data Platforms
Next-gen data platforms that go beyond the incremental improvements to eliminate all-too-common silos in data, applications, and teams—and facilitate organizational change
How it helps?
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Assessment & roadmap definition leveraging proven frameworks and pre-built reference architectures
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Automations such as CTL, RTDS and best practices to implement the platform at speed
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Automated migration for a wide variety of data formats & sources
Why?
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Delivers an agile, flexible & scalable data platform
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Unified & simple platform governance
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Cost effective & security compliant
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Reduced management overhead & increased productivity of D&A teams

Data Modeling
Combine knowledge of business context, data characteristics & modern design paradigms to create scalable data models that optimizes performance & minimizes maintenance efforts
How it helps?
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Reusable Logical Data Model (LDM) templates for common domains and functions
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LDM for non-functional requirements such as traceability, auditability, security, quality
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Performance benchmarks for data access and data ingestion
Why?
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Scales with data volume and velocity
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Minimizes maintenance efforts with data variety and variability
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Optimizes performance for data access and ingestion
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Supports data trust – quality, timeliness, traceability, auditability and security

Product Design
Bring stakeholders together to imagine, create, & iterate data products that solve business problems or address specific unmet needs
How it helps?
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Design practices for personalized & immersive user experience
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Visual and narrative storytelling concepts to accelerate decisions
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Human centric approach to uncover actionable insights
Why?
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Prepares users to interpret data uniformly, understand insights unambiguously & react faster to evolving market situations
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Enables definition of customer journeys that are most conducive to the purpose, thereby maximizing insights adoption
Modern Data Pipelines
Unify & convert data from different sources into usable format in the fastest way possible
How it helps?
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Makes data available as events happen (in real-time)
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Comprehensive support for common file & databases
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Easily extendable
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Supports CDC, Query & API-based ingestion approaches
Why?
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Rapid on-boarding of new data source
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Quick Insights – Maximize returns on your data through automated data pipelines
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Enhanced re-usability through established data ingestion patterns
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One stop solution for homogenous and heterogenous data ingestion

Business Analytics
Convert data into user-friendly insights to deliver greater value–for customers, employees, partners, or shareholders–with greater efficiency
How it helps?
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Repeatable discovery-to-delivery approach
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Fully automated suite of tools to enable enterprise-grade apps
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Domain, digital and analytics expertise to create products that meet your business objectives
Why?
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Intelligent business insights apps in days, not weeks
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Responsive, purpose-built solutions that satisfies unmet needs
Data FinOps
Maximize Biz value by enabling cross-functional teams in making data-driven spending decisions while evolving financial management discipline and cultural practices
How it helps?
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Framework to manage Opex efficiently for D&A Initiatives
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Strike a balance between idle resources and over provisioning
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Provide Insights that help alleviate stakeholder fears on unexpected cost overruns
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Make faster decisions on retirement of Technical Debt (services that do not matter the most)
Why?
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Drives the financial accountability culture by enabling D&A teams to make the right choices considering speed, cost, and quality
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Maximizes value from varying cloud spends – It’s not just about saving costs, as these spends can drive significant incremental revenue
Data Migration
Convert and migrate data from legacy databases to cloud-native modern data platforms in an accelerated & efficient manner
How it helps?
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Data landscape assessment and roadmap advisory
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Multi-year TCO and ROI view to aid your funding decisions
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Automated conversion and migration for most of your legacy sources
Why?
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Balances Speed with Accuracy
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Reduces migration risks
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Minimizes cost
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Zero business disruption
Analytics Migration
Migrate BI/Analytics platforms quickly and efficiently to modern platforms using our robust methodology, accelerators, and pre-built toolkits
How it helps?
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Structured assessments to make right migration choices
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Automated tools for migrating most of your assets from source to target
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Automated regression tests
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User education on new tool capabilities
Why?
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Accelerates and de-risks migration programs – Get it right the first time!
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Retains benefits from existing apps
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Zero business disruption during migration
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Happier Users!

Data Security & Privacy
Deliver trusted customer experience with a holistic and adaptive approach to data security and privacy based on zero trust principles of data protection
How it helps?
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Best practice frameworks & methods to validate security at every stage of data handshake
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Support for local data regulations such as PCI, HIPAA, GDPR etc.
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Supports security in multi cloud/platform scenarios
Why?
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Improves trust amongst partners, customers & other stakeholders
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Stops any type of misuse of PII Data
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Safeguards organizations from regulatory penalties

Augmented Self-Service
Empower users with AI-driven self-service capabilities to derive faster & deeper insights from data
How it helps?
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Assess needs, choices and define self-service roadmap
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Roll-out self-service tools & capabilities to ensure trusted data delivery
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Train your team, establish support and on-going enablement
Why?
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Reduces IT dependency – Users can explore, curate & analyse data on their own
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Eases consumption & improves user experience

Machine Learning
Seamless automation across the life cycle of a Data Science program – from advisory, to model development and deployment across platforms
How it helps?
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Adopt mining techniques & feature engineering processes that can automatically engineer new features
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Leverage repository of diverse algorithms that best fits needs
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Build a platform that explains model decisions in human interpretable format
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Automate repetitive, tedious and time-consuming tasks across the model lifecycle
Why?
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Integrate your existing data science capabilities with new-age capabilities
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Free-up data scientists’ time to discover & solve new business problems

Business Automation
Transform your business and customer experience using smarter technologies to drive hyper-productivity, enhance service quality & accelerate digital transformation
How it helps?
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Prioritize and streamline workflows through RPA/ automation techniques
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Standardize to drive efficiency across multiple business processes
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Leverage smarter technologies that are purpose built for automation and can handle scale & variety
Why?
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Drives hyper productivity leading to significant cost savings
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Enhances agility and flexibility with the automated process always ready to perform a task on-Demand 24 x 7
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Improves overall governance with the audit trails driving insights, promoting accountability & demonstrating compliance

Data Sharing
Realize better value of data, create new or better products, through secure sharing of data (both internally and externally) without replicating data assets
How it helps?
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Share live data without creating data copies while complying with privacy & cyber standards
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Enable data sharing in a hybrid multi-cloud/platform
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Automate monitoring & audits
Why?
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Accelerates time to value
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Enables net new revenue creation through enabling new use cases
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Standardizes data sharing & governance practices
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Lowers cost of data sharing

DataOps Automation
Continuous improvement to reduce time & effort to convert data into insights in a secure & reliable way
How it helps?
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Automated data profiling, quality checks and discoverability
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Automated data pipeline development
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Framework to authenticate & authorize data access
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Support for data-as-a-service
Why?
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Reduces Manual QA Efforts
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Accelerates Time to Insights
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Reduces Data Silos
Data Quality
Guarantee access to trustworthy data for consumption across entire community of users
How it helps?
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Automated DQ issue detection from source to insights
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Smart data validation rules
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Real time alerting and tracking of data quality issues
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Pre-built dashboards for DQ visibility and to drive improvement actions
Why?
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Improve trust on the data to drive meaningful adoption
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Reduce time to conduct diagnostics and root-cause-analysis
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Present reliable information for downstream analytical applications

Platform Support
Optimize D&A investments through continuous improvement, automation, and reuse in platform administration and support
How it helps?
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Accelerated platform set-up leveraging automations & best practices
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Automated release management & repeatable maintenance tasks
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Trouble-free upgrades with automated regression testing
Why?
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Maximizes value from existing D&A investments
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Optimizes TCO while ensuring that D&A platforms are highly available & performant
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Creates savings from ‘run’ operations to fund modernization initiatives
Cloud Infrastructure Automation
Use code to automate provision, comply, & manage cloud infrastructure to reduce effort, improve security, control spend, & enable innovation
How it helps?
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Reusable configuration templates that leverage auditable, scalable and automated Infrastructure-as-a-code practices
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Enables enterprise grade cloud platform, complete with devops, security and performance
Why?
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Reduces time to turnaround environments
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Enables CI/CD automation with self-service
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Reduces mean time to repair and recover

D&A Adoption
Measure, sustain & improve qualitative and quantitative adoption of data and analytics assets of the enterprise to maximize business value from D&A investments
How it helps?
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Quantitative and qualitative measurement of user adoption
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Identification and resolution of adoption bottlenecks using process alignment, self-help, concierge services & more
Why?
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Business alignment & action orientation
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Improved data understanding & trust
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Ease-of-use – user experience and persona-orientation