Data & Artificial Intelligence

Data & AI as a Service (DAaaS): Reliable and efficient application management to optimize performance and ensure thus no business disruptions. Thus can be detailed as following capabilities

• Data Landscape Assessment is knowledge of what data lies where and in what state is key for evaluating an organization's data infrastructure, capabilities, and processes. This assessment helps identify strengths, weaknesses, and opportunities for improvement. Some of the key aspects of the assessment are – Identification of Data Sources, Data Collection & Ingestion, Data Storage & Management, Data Quality, etc.

• Data Governance is the framework of policies, standards, and practices that organizations use to manage their data assets effectively. It ensures that data is accurate, consistent, secure, and used responsibly. Some of the key aspects of a robust data governance framework – Policies Standards & Process, Roles & Responsibilities, Data Quality Management, Data Security & Privacy etc.

• Data Architecture refers to the structure and organization of an organization's data assets and data management resources. It defines how data is collected, stored, processed, integrated, and accessed to support business processes and decision-making. Here are the key aspects of data architecture – Principles focussing on scalability, interoperability, flexibility & performance, Meta-Data Management, Manage variety, volumes and veracity of data.

• Data Democratization is the process of making data accessible to all employees within an organization, regardless of their technical skills or roles. It empowers individuals at all levels to make data-driven decisions, fostering a culture of data literacy and innovation. Some of the key components and benefits of data democratization – Accessibility, Data Literacy, Data driven culture within the Org, shorter time to Insights etc.

• Operational Reporting involves generating reports that provide detailed, real-time insights into the daily operations of a business. These reports are crucial for monitoring performance, identifying issues, and making informed decisions. Here are a few examples of operational reporting – daily performance reports, financial reports, customer excellence reports, Op Efficiency tracking reports, etc.

• Analytics & insights are critical components of data-driven decision-making. They involve analyzing data to uncover patterns, trends, and actionable insights that can drive strategic and operational decisions. Here's an overview of the key concepts and practices in analytics and insights – Data Visualization, Descriptive, Predictive, Diagnostic & Prescriptive Analytics, etc.

• Data Science & Artificial Intelligence (AI) are closely related fields that leverage data to create intelligent systems, uncover insights, and drive decision-making. Here's an overview of their key components, techniques, and applications:

Data Science is the interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves several steps like data collection, modelling, processing, deployment etc.

Artificial Intelligence (AI) is the broader field that involves creating machines capable of performing tasks that typically require human intelligence. AI encompasses various subfields – Machine Learning, NLP, Deep Learning, Neural Network, Computer vision & Cognitive Computing.

• Digital Products - data and digitalization of data are the cornerstone of innovation. In today’s world simply accessing data is not enough a lot of emphasis is put in user experience, personalization & efficiency. We help to customize and curate digital products in several domains like Finance, Risk, Sustainability, Supply chain, etc.