Strategy Execution

Strategy helps an organization position itself uniquely in order to create value and exploit the unfolding opportunities , hedge against threats, leverage strengths and remove weaknesses. Strategy execution has two components namely the action planning to operationalize the core strategy and its physical implementation. Strategy execution is one integrated in which action planning and physical implementation are integrated in real time basis. High quality execution presupposes high quality action planning , assigning the right people for the right jobs, adequate follow through and robust MIS and review processes. Strategy execution involves the optimal balancing of seven key variables namely Strategy, Structure, Systems , Staff, Skills, Style and Shared values. Strategy execution is the key role of the business leader. This execution is a discipline and must be learned specifically. The primary areas are ...

February 11, 2013 · (updated January 16, 2022) · 2 min · Pradeep Loganathan

Concept of Strategy and Strategy Process

In the new business environment customers are not just demanding but they also have infinite options. Competition is not just intensifying but there are new sources of competition. Resources too are not just limited but they are fluid and move fast. Investors are impatient and look at above average returns consistently and constantly. Huge opportunities coexist with massive risks. An organization must think and act differently and smartly to face the new environmental context. Strategy derived through the strategy process is what makes the organization stand apart from ”also rans” and perform differently. ...

January 30, 2013 · (updated January 16, 2022) · 2 min · Pradeep Loganathan

Introduction The Business Challenge: Traditional loan approval processes are slow, error-prone, and rely on outdated data. Manual reviews and data silos create bottlenecks and hinder decision-making. Businesses need a way to quickly analyze vast amounts of loan data and make accurate predictions in real-time. The In-Database ML Solution: In-database machine learning (ML) with Greenplum leverages the power of your existing data warehouse to build and deploy ML models. This eliminates the need to move data out of the database, resulting in faster model training and scoring. Greenplum’s MPP architecture enables parallel processing, accelerating ML tasks and providing real-time insights. Why Greenplum for In-Database Machine Learning? ...

(updated December 21, 2024) · 2 min · Pradeep Loganathan

Blog Post Series: Building a Logistic Regression Model with Greenplum, MADlib, and pgvector Part 1: Introduction to In-Database Machine Learning with Greenplum, MADlib, and pgvector Title: “Introduction to In-Database Machine Learning with Greenplum, MADlib, and pgvector” Summary: Explore the benefits of in-database machine learning with Greenplum. Learn about Greenplum, MADlib, and pgvector, and understand why in-database machine learning is a game-changer. Sections: Introduction Overview of in-database machine learning Benefits of in-database machine learning Key Technologies ...

(updated August 1, 2024) · 4 min · Pradeep Loganathan