Analytics and Information Management (AIM) Overview
We organize and visualize to help your company leaders make better decisions.
We’ll work with you to identify your technology needs and resolve your challenges utilizing our innovative processes to analyze, plan, design, and develop the solutions you need. Our big data consultancy allows us to offer the most comprehensive data analytics to our clients.
Let us help you uncover the value of your untapped data, and discover why big data platforms are gaining popularity with enterprises across the world. Our analytics services are divided into three parts: Data Integration, Information Delivery and Advanced Analytics.
Our practices include business intelligence & visualization, performance management and information management including cloud, big data, cognitive learning and machine learning.
Data Integration (DI) Services
Expertise
| Data Architecture and Enterprise Data Model development |
| Conceptualize, Design and Implement Enterprise Data Warehouse across Industry Verticals |
| Health assessment of current BI/DW solutions |
| Data Profiling and quality assessment |
| Evaluation of data integration tools |
| Data Migration from legacy applications to new ERP applications |
| Support and Maintenance of current Data Integration applications |
| Technology oriented COEs |
Information Delivery (ID) Services
Expertise
| End-to-End Business Intelligence solutions across industry verticals |
| Factory model for efficient utilization of resources and lower cost of ownership |
| Guided analytics approach to enable quicker decisions |
| Technology oriented COEs |
| Industry specific point solution / KPIs as a solution accelerator |
| Consulting services helping clients in Tool evaluation, Health assessment of BI Eco-System and BI Strategy / Roadmap definition |
| Support and Maintenance of current Data Integration applications |
| Support and Maintenance of current BI ecosystem |
Advanced Analytics
Expertise
| Identifying data driven decision making needs; creating advanced analytics strategy & roadmap |
| Problem identification & defining solution strategy across business functions & domains |
| Source, cleanse, enrich data from both internal & external sources; effective organization of data |
| Data analysis and treatment; hypothesis testing and recalibration |
| Selecting the right techniques & methodologies for further data analysis & solution development |
| Model development & testing; on-going validation and improvement; execute test & learn strategies |
| Deploying solutions at scale & enabling democratized decision making |
Core Insights Engine
Change
- Problem Identification
- Prioritization
- Detailed Definition
Data
- Identify Requirement
- Source & Prepare
- Analyze & Profile
Math
- Tools & Technique Selection
- Develop & Validate Model
Change
- Generate Actionable Insights
- Assist Adoption
- Plan Scale
- Manage Change
Scale Enablers
Big Data Platform
- Managing Velocity, Variety & Volume
- Handling Complexity
- Scalable Performance
- Cost Effective
- Operational Ease
Automated Insights
- Machine Learning
- Knowledge Extractor
- Insight / Action Plan Training
- Decision Engine
Enabling Software
- Embedded Analytics
- Advanced Visualization
- Location & Device Agnostic
- Seamless User Experience
Level 0 – Experiential
Typical Characteristics
- Reliance of experts
- Decisions from gut or experience
- Commitment w/o experimentation
Infrastructure Needs / Analytical Toolsets
- Reliance of experts
- Decisions from gut or experience
- Commitment w/o experimentation
Level 1 – Ad Hoc Data Driven
Typical Characteristics
- Insights drawn from data
- Decisions still from gut
- Commitment w/o experimentation
Infrastructure Needs / Analytical Toolsets
- Good descriptive reporting
- Enterprise / Departmental data warehouse
Level 2 – Localized Experimentation
Typical Characteristics
- LOB or department sponsored experimentation
- Operate in silos with localized choices
- Rigor tends to be lacking
Infrastructure Needs / Analytical Toolsets
- Localized data analysis tools
- Some inquisitive and predictive capabilities
Level 3 – Widespread Experimentation
Typical Characteristics
- Enterprise mandate with LOB / dept. sponsorship
- Increased rigor in practices
- Some governance and sharing of best practices
Infrastructure Needs / Analytical Toolsets
- Enterprise-wide standardization on tools, practices and capabilities
- Emergence of central collaboration platform
Level 4 – Institutionalized Collaboration
Typical Characteristics
- Executive commitment and sponsorship
- Strong governance and rigor in practices
- Collaboration and coordination in decision making
Infrastructure Needs / Analytical Toolsets
- Central collaboration and experimentation platform with strong governance
Level 5 – Democratized
Typical Characteristics
- Embedded consumption of analytics with real-time delivery of insights
- Empowered frontline and operations personnel
Infrastructure Needs / Analytical Toolsets
- Automated insight generation
- Embedded decision support tools
- Real-time monitoring and feedback tools