Data Analytics and AI

Unlock the infinite potential of your data with STL Digital’s comprehensive Analytics & AI solutions.

Overview

Businesses & customers are currently generating 2.5 quintillion bytes of data each day, it has become more difficult and crucial than ever to collect, organize & create insights from this data. With this growing fabric of data which is highly fluidic in nature, the decisions surrounding it grow more connected, contextual, and continuous. Hence arises the need to incorporate AI, for faster and more flexible decision-making for businesses.

STL Digital provides a portfolio of solutions including Consulting, Application Services & Operations Support in Data and AI services that completely satiate the business requirements and consistently support daily business needs efficiently. We do this while enabling businesses to deliver best-in-class experiences to end consumers.

Data Analytics and AI Trends

Digital Twin
Digital twins are virtual replicas of physical objects, systems, or processes, which are gaining popularity across various industries for real-time simulation, predictive maintenance, and product optimization. They help companies reduce costs, increase efficiency, and improve innovation.
Adaptive AI Systems
As business decisions are becoming more connected, contextual & continuous, it simultaneously becomes increasingly important to re-engineer decision-making. This can be done by using adaptive AI systems, which can offer faster and more flexible decisions by adapting more quickly to changes.
Metadata-driven data fabric
Data fabric listens, learns, and acts on the metadata. It flags and recommends actions for people and systems. Ultimately, it improves trust in, and use of, data in the organization and can reduce by 70% various data management tasks, including design, deployment, and operations.
Sharing Data with Trust
Promoting data sharing and increasing access to the right data aligned to the business case requires collaboration across business and industry lines. By doing so, you can accelerate buy-in for increased budget authority and investment in data sharing.
Context-enriched analysis
A context-enriched analysis approach, builds on graph technologies. The information on the user’s context and needs is held in a graph that enables deeper analysis using the relationships between data points as much as the data points themselves. It helps us identify and create further context based on similarities, constraints, paths, and communities.
Decision-centric D&A
The decision-centric D&A approach is helping organizations rethink their investments in data and analytics capabilities.
AI Risk Management
By supporting AI trust, risk, and security management (TRiSM), organizations can improve AI outcomes in terms of adoption, achieving business goals, and both internal and external user acceptance. Our AI Risk Management solutions can help you achieve this.
AI Risk Management
Analytics is increasingly being used in sustainability to analyze environmental and social data, identify opportunities for improvement, and track progress towards sustainability goals. Companies are adopting analytics to reduce carbon footprint, enhance social responsibility, and report on environmental performance.

Adaptive AI Systems
As business decisions are becoming more connected, contextual & continuous, it simultaneously becomes increasingly important to re-engineer decision-making. This can be done by using adaptive AI systems, which can offer faster and more flexible decisions by adapting more quickly to changes.
Sharing Data with Trust
Promoting data sharing and increasing access to the right data aligned to the business case requires collaboration across business and industry lines. By doing so, you can accelerate buy-in for increased budget authority and investment in data sharing.
Decision-centric D&A
The decision-centric D&A approach is helping organizations rethink their investments in data and analytics capabilities.
AI Risk Management
Analytics is increasingly being used in sustainability to analyze environmental and social data, identify opportunities for improvement, and track progress towards sustainability goals. Companies are adopting analytics to reduce carbon footprint, enhance social responsibility, and report on environmental performance.

1Digital Twin
Digital twins are virtual replicas of physical objects, systems, or processes, which are gaining popularity across various industries for real-time simulation, predictive maintenance, and product optimization. They help companies reduce costs, increase efficiency, and improve innovation.

2Adaptive AI Systems
As business decisions are becoming more connected, contextual & continuous, it simultaneously becomes increasingly important to re-engineer decision-making. This can be done by using adaptive AI systems, which can offer faster and more flexible decisions by adapting more quickly to changes.

3 Metadata-driven data fabric
Data fabric listens, learns, and acts on the metadata. It flags and recommends actions for people and systems. Ultimately, it improves trust in, and use of, data in the organization and can reduce by 70% various data management tasks, including design, deployment, and operations.

4Sharing Data with Trust
Promoting data sharing and increasing access to the right data aligned to the business case requires collaboration across business and industry lines. By doing so, you can accelerate buy-in for increased budget authority and investment in data sharing.

5Context-enriched analysis
A context-enriched analysis approach, builds on graph technologies. The information on the user’s context and needs is held in a graph that enables deeper analysis using the relationships between data points as much as the data points themselves. It helps us identify and create further context based on similarities, constraints, paths, and communities.

6Decision-centric D&A
The decision-centric D&A approach is helping organizations rethink their investments in data and analytics capabilities.

7AI Risk Management
By supporting AI trust, risk, and security management (TRiSM), organizations can improve AI outcomes in terms of adoption, achieving business goals, and both internal and external user acceptance. Our AI Risk Management solutions can help you achieve this.

8Analytics in Sustainability
Analytics is increasingly being used in sustainability to analyze environmental and social data, identify opportunities for improvement, and track progress towards sustainability goals. Companies are adopting analytics to reduce carbon footprint, enhance social responsibility, and report on environmental performance.

Our Offerings

Strategy & Roadmap
Architecture Blueprint
Design thinking & Solution design
Product Evaluation
Data Governance
Cloud Architecture & Migration

Consulting

Implementation Services
Solution & Technology Architecture
Data Design & Modelling
Data Integration
Data Reporting & Visualization
Data Science
Testing
Transition & Landscape Transformation