Data Driven Business Intelligence
In today’s world, with so much of data trail left behind with every transaction, it’s hard to ignore this data and not base future decisions on them. Businesses are now finding new ways to become data-driven organizations so that they can anticipate market changes and adapt quickly by leveraging the data insights to introduce new products and services for their customers
To be a data driven organization, companies need a comprehensive data strategy and solid platform based implementation to glean a 360 degree view of the data and the customer. Despite the hype, businesses struggle to make use of the data they collate due to a huge gap between the theoretical knowledge and actually putting this theory into practice. A few data problems include – data complexity, data silos, inaccurate data, advanced technology and lack of talent.
Our expertise in data management will help you focus on the insights and realize full value of your AI and ML investments across every business function.
Data Analytics Approach
Offerings & Solutions
Data Strategy, Governance and Compliances
Data strategy has to be aligned to the specific business needs and the expected insights and outcome. The choice of platform, tools and technologies is also driven around these decision points. Our offerings include:
- Data Discovery, Identifying data hubs, data ingestion, Orchestration, Automation and managing data quality
Data at rest, in-motion, relational and non-relational schemas, Data governance, access management, warehouse decisions
Enterprise Data Integration
API led integration with external entities for cohesive and synchronous meaningful view. Handle data security and access authorization
Insights into Data, Data Visualization, Predictive Analytics
Artificial Intelligence and Machine Learning
Predictions using supervised learning, patterns using unsupervised learning, Auto ML, NLP for text analysis, model building for classification, regression and clustering
- Data Ingestion – Data Discovery, Orchestration and Ingestion, Data Quality, Automation.
- Data Management at scale – Data at rest, in-motion, relational and non-relational schemas, Data governance, access management.
- Data Modeling – Insights into Data, Data Visualization, Predictive Analytics.
- Machine Learning – Predictions using supervised learning, patterns using unsupervised learning, Auto ML, NLP for text analysis, model building for classification, regression, and clustering.