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.