# Tag: Business Intelligence

## [Season One: A Broad View of Business Intelligence](/content/season-one/season-one-overview/index.html)

This season was created with the beginner in mind. We introduce the core concepts, assuming no prior professional training. Experienced professionals may be tempted to skip this season, but I would encourage you not to – a complete understanding of the foundations will help you grow in the field, much like the deep roots of a tree will help it… grow in the field. Similes aside, the author highly recommends Season 1.

1.1 [The Post that Started it All: What is Business Intelligence?](/content/season-one/season-one-episode-one/index.html)

In this article we introduce the field of business intelligence, including one way of understanding it as a combination of four broad disciplines. We also introduce the authors and this article series.

1.2 [What is a Data Model?](/content/season-one/season-one-episode-two/index.html)

In this article we cover how data fits into tables, and how those tables evolve into data models. We introduce the simplest model: a single denormalized table. These single-table models could be all you need to answer your questions, but they can also face certain limitations.

1.3 [The Challenges of a Single Table, or: A Star is Born](/content/season-one/season-one-episode-three/index.html)

In this article we address the challenges of a single table, introducing the concepts of columnar storage and cardinality. We propose a solution to the challenges: a star schema.

1.4 [A Key is Key: Making Relationships Between Data](/content/season-one/season-one-episode-four/index.html)

In this article we talk about how relationships connect tables and allow users to filter data. We cover the most common kinds of relationships and how relationships require keys to connect.

1.5 [Upstream Solutions to Downstream Problems](/content/season-one/season-one-episode-five/index.html)

In this article we cover one of the most fundamentally important concepts in business intelligence. We also introduce the practices of analysis and synthesis, and how they’re used in the field.

### Four Broad Disciplines of Business Intelligence

1. **Data Prep**  
     This is usually the first step in any sort of BI process. Data prep usually starts with literally finding the data (it isn’t always in one place), then looking at it to make sure it’s valid. To prepare the data for modeling, oftentimes it will be changed from one format to another, and tables will be combined, split apart, and/or cleaned up. Then the whole slew will be loaded into reporting software.

2. **Data Modeling**  
     This is the practice of organizing data into meaningful groupings (customers, products, sales, etc.) and defining relationships between them. The modeling we engage in is called ‘semantic modeling’, and it enables users to easily locate data (or attributes) and navigate their relationships.

3. **Data Measurement**  
     This is the practice of overlaying computations on the dataset. This could be as simple as summing up sales so we can know just how many widgets we’ve sold (and comparing that to our widget budget), or as complicated as forecasting how many widgets we expect to sell next spring.

4. **Data Visualization**  
     This is an often-overlooked discipline in BI, but VERY important. Not all visualizations are equally useful, and some, like the common pie chart, are as overused as they are ineffective at conveying information accurately. The reports created in this step are what’s used to drive decision-making, and there is both a science and an art to creating useful, beautiful reports.

### Conclusion

This brings us to the end of Season 1! In this season, we’ve taken a broad view of business intelligence in five articles:

1.1 – [The Post that Started it All: What is Business Intelligence?](/content/season-one/season-one-episode-one/index.html)
1.2 – [What is a Data Model?](/content/season-one/season-one-episode-two/index.html)
1.3 – [The Challenges of a Single Table, or: A Star is Born](/content/season-one/season-one-episode-three/index.html)
1.4 – [A Key is Key: Making Relationships Between Data](/content/season-one/season-one-episode-four/index.html)
1.5 – [Upstream Solutions to Downstream Problems](/content/season-one/season-one-episode-five/index.html)

These articles covered the upstream concepts and principles that will remove downstream problems for you as you grow in this field. I’ve included a summary below, telling the story in brief while highlighting the important concepts in bold:

We talked about the **four broad disciplines** of BI: Data Prep, Modeling, Measurement, and Visualization. Then, we learned how data fits into **tables** which evolve into **data models**, starting with basic **denormalized** tables. These single-table models could be all you need to answer your questions, but they can also grow past limitations to become **star schemas** – this process is called **normalization**. The process separates data into **fact tables** and **dimension tables**. In order to understand why tables can grow long (but not wide), we learned about **cardinality** and **columnar storage**. Our new star schemas rely on relationships between tables in order to **filter** the data, and so far we’ve covered **directional relationships** and **many-to-one relationships**. These relationships connect tables using **keys**. We learned the different names for keys we might encounter out in the wild. Finally, we spent some time discussing the **why**, **how**, and **what** of BI, pairing these tools with real-world needs.