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16 Best Business Intelligence Books To Get You Off the Ground With BI

Data Pine

But with so many business analytics books out there and so little time, how do you decide which ones are worth your time? But before we unveil our definitive rundown of intelligence and business analytics books, let’s explore some facts, figures, and insights that will inspire you while steering your success in the exciting world of BI.

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The Four Pillars of a Data Fluent Organization

Juice Analytics

The symptoms we see are varied: lack of management support, lack of end-user adoption; poorly defined requirements; data warehouse projects that never seem to finish. And for each of these problems, the data industry has crafted different “solutions” or technologies to try to address them. We wrote a book about it.

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Introduction To The Basic Business Intelligence Concepts

Data Pine

Business intelligence concepts refer to the usage of digital computing technologies in the form of data warehouses, analytics and visualization with the aim of identifying and analyzing essential business-based data to generate new, actionable corporate insights. The data warehouse. 1) The raw data.

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Where Dark Data Lives and How to Mine It for Business Gain

Domo

The same goes for Word documents, forms, PDFs, and presentations. Data repositories. Lots of data—structured and unstructured—gets dumped into data warehouses, lakes, and non-relational databases. CIO Jarrod Phipps said his team never would have thought that PBX data had any value until they saw it in action.

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Keynote Takeaways From Gartner Data & Analytics Summit

Sisense

Every year there’s high anticipation to see what key message Gartner will present in the yearly Data & Analytics Summits. This is why I liked that James Richardson, Sr Director Analyst at Gartner, chose to share a slide with the titles of the three books written by one of my favorite authors – Yuval Noah Harari.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Analytics acts as the source for data visualization and contributes to the health of any organization by identifying underlying models and patterns and predicting needs. Visualizations: past, present, and future. This data is gathered into either on-premises servers or increasingly into cloud data warehouses and data lakes.

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Data Model Development Using Jinja

Sisense

Every aspect of analytics is powered by a data model. A data model presents a “single source of truth” that all analytics queries are based on, from internal reports and insights embedded into applications to the data underlying AI algorithms and much more. OLTP does not hold historical data, only current data.