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Volume 3

The Hitchhiker's Guide to Data Analytics

There are two types of organizations – ones that just don’t have enough data to deliver insights and second organizations that are inundated with data but have no idea what to do with it. Even when organizations have adequate amounts of data they are challenged to recognize which data is important, where it exists and which data they have access to. Then there are organizations that have beautiful dashboards but like in Douglas Adams’ novel Dirk Gently’s Holistic Detective Agency, the dashboards act as a computer program called Reason which retroactively justifies any action!

table of contents

Chapter 1: Introduction

Chapter 2: Data Ingestion Concepts

Chapter 3: Data Integration Concepts

Chapter 4: Event and App Integration

Chapter 5: Data Ingestion and Data Integration Products

Chapter 6: Data Analysis Concepts

Chapter 7: Query Engines and BI Products

Chapter 8: Advanced Analytics Concepts

Chapter 9: Advanced Analytics Products

The point is that analytics is harder than it looks and making decisions is complex. Many organizations believe that any decision is better than no decision but it is hard to determine how good is the decision? And who should be involved in the decision making? Sometimes there are too many cooks and it undermines the data and analytics architecture. It resembles Douglas Adams’ “Reason” program.

Business needs data to make decisions, provide competitive advantage and insights. Data Analytics’ goal is to extract intelligence and hidden signals from vast amounts of data – most of it as yet untapped within an organization. 

This book is meant for the data leaders who are responsible for evaluating technologies and approaches to design an optimal modern analytics architecture for current needs and foreseeable future needs. 

This book covers the following topics:

  • Analytics value chain
  • Key characteristics of data and analytics 
  • Data Delivery options including data as a service and data sharing
  • Analytics query engines and accelerators
  • Machine Learning conceptual framework and reference architecture
  • Integration of AI techniques with database and data management
  • Taxonomy of business intelligence, data science and advanced analytical products 

The advanced analytics space is riddled with a huge dose of identity crisis. Luckily, it is not an existential crisis because most experts agree that AI is the most significant development in IT. However, what constitutes AI is often debatable. This book connects the docs.

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