Lecture 8

Building a competitive advantage through analytics

Winning is “being successful in achieving or getting something that others also wanted” (Cambridge Dictionary). The desire to win is embedded in our nature and has been an important driver in human evolution. For companies, winning is critical to survive and remain relevant in the competitive environment. Companies are able to win in the marketplace if they “provide a better consumer and customer value equation than competitors, and can do so on a sustainable basis” (Playing To Win, Roger Martin). If companies do not make conscious choices to enable themselves to win, they are unlikely to establish a sustainable competitive advantage. Without a sustainable competitive advantage, they are left to the mercy of market forces – resulting in mediocre performance at best, and bankruptcy at worst.
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Selecting the Numbers of Clusters with Silhoutte Analysis on K-Means Clustering

Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette plot displays a measure of how close each point in one cluster is to points in the neighboring clusters and thus provides a way to assess parameters like number of clusters visually. This measure has a range of [-1, 1].

Silhouette coefficients (as these values are referred to as) near +1 indicate that the sample is far away from the neighboring clusters. A value of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters and negative values indicate that those samples might have been assigned to the wrong cluster.

Introduction to K-Means Clustering

In the field of machine learning, one of the key objectives is to achieve accurate predictions. These predictions can be achieved through various learning algorithms, categorized as supervised and unsupervised.

K-means clustering is one such unsupervised algorithm that groups data points based on their similarity. In this blog, we will explore the use cases, advantages, and working principles of the K-means clustering algorithm.

How to Run Cluster Analysis in Excel

This is a step by step guide on how to run k-means cluster analysis on an Excel spreadsheet from start to finish. Please note that there is an Excel template that automatically runs cluster analysis available for free download on this website. But if you want to know how to run a k-means clustering on Excel yourself, then this article is for you.

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Using advanced analytics to make Big Decisions
Over the last few years, digital acceleration, coupled with global shifts in economic power, has altered the playing field for the companies around the world. Advanced analytics and machine learning— technologies that once seemed like beacons from a distant future—are increasingly available and accessible to businesses. The ability to make intelligent decisions that drive growth, disrupt the market and capitalise on emerging opportunities is now linked less to gut feeling and more to predictive and prescriptive analytics, artificial intelligence and data-driven insights. Gaining a competitive advantage is no longer about accepting the status quo or relying on age-old traditions. It’s about mustering the courage and commitment to harness both mind and machine. Our most recent Global Data and Analytics Survey (2016) involved the participation of more than 2,100 global executives (globally) and more than 100 Indian executives, ranging from C-suite leaders and departmental heads to managers and analysts.

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Modern Risk Management for AI Models

As we enter a new era of unprecedented customer experiences, the demand for faster and higher-quality financial analysis and forecasting has increased significantly. The clear benefits of AI in finance, banking, and business analytics are easy to gauge. It is difficult to define AI, but broadly speaking it is the theory and technology around development of smart and intelligent computer systems or algorithms that are not explicitly programmed for, but “self-learn” to perform tasks which otherwise would require human intelligence. Machine Learning, Deep Learning (DL), Speech Recognition, Natural Language Processing (NLP) and visual recognition technologies all belong to this class. Even though we use the terms AI and ML interchangeably, there are a few differences among them, but at the heart of it, the primary aim of these algorithms is to create intelligent systems. For example, ML applications like chat-bots have now enabled banks to automate time-consuming, repetitive processes offering a far more streamlined and personalized customer experience. They have also allowed banks to work more productively with large databases, unstructured information, significantly improving the quality of asset valuation, forecasting financial performance, and solving key issues around data security.
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