Decision Driven Analytics
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Decision-Driven Analytics
Author: Bart De Langhe
language: en
Publisher: University of Pennsylvania Press
Release Date: 2024-05-14
Companies have more data at their fingertips than ever before. Yet, studies show that many executives and organizations fail to extract real value from it. Challenging the conventional wisdom of data-driven decision-making, marketing professors and behavioral scientists Bart De Langhe and Stefano Puntoni argue that many analytics efforts flounder because data analyses are disconnected from the decisions to be made. In their important book, they offer a new approach they call decision-driven analytics. Counterintuitively, they argue that the key to making good decisions with data is to start by putting data in the background. Drawing from their own research and teaching, as well as real-world business cases, De Langhe and Puntoni offer four pillars of decision-driven analytics and guide you around common mistakes that have held back many organizations from using data for impact. In Decision-Driven Analytics, you will learn how to: + Avoid common pitfalls in data-driven decision-making; + Close the gap between managers and decision-making on one side, and data scientists and data analytics on the other; + Enhance the impact of data analytics on business outcomes; + Think without data to make better decisions; + Prepare for artificial intelligence’s impact on data analytics; and + Evaluate the costs and benefits of decision-driven analytics. A must-read for anyone who wants to harness the power of data for competitive advantage, Decision-Driven Analytics will equip you with the skills and tools you need to more effectively use data for business outcomes and to make better decisions in today’s complex and data-rich world.
Data Driven Decision Making using Analytics
This book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining. Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics. This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.
Big Data for Big Decisions
Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term. Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval. This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs. The emphasis is on the big decisions – the key decisions that influence 90% of business outcomes – starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive. While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics. They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven. As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven. Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects. Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago. The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics. It is a practitioners’ handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization.