The Types of Data That You Can Analyze

Data analysis is the general term used to encompass activities related to the examination, interpretation, synthesis, and summarization of data. In real-world computing, these activities translate to people using software to make sense of—and making smarter business decisions based on—their business’ or organizations’ data. This definition of data analysis frames the rest of this book’s contents.

All About Data

At its core, data is about using groups of letters, numbers, and other characters to represent facts in the real world. For example, the string of characters Seattle 64^oF 10/14/2002 most likely represents the fact that the temperature in the city of Seattle on the date 14 October 2002 was 64 degrees Fahrenheit. Similarly, the string of characters MSFT 45.35 7/26/ 2002 most likely represents the fact that the closing price of one share of Microsoft stock on the date 26 July 2002 was US$45.35.

Each individual component of the fact represented is known as a field. In the previous example, the letters MSFT could constitute the Company field, the numbers 45.35 could constitute the Closing Price field, and the date 7/26/2002 could make up the Date field. A group of related fields are known as a record. To continue this example, two stock records might include the following: MSFT 42.83 7/25/2002 and MSFT 45.35 7/26/2002. Related records that use the same field names are commonly grouped and stored together in electronic data files or databases. The software applications used to create and store these data files and databases usually include features that you can use to perform data analysis tasks.

Organizations use many types of data files and databases to store data, including

• Text files, in which each field is commonly separated by a character such as a comma or a semicolon and each record is commonly separated by a carriage return.

• Spreadsheets, in which each field occupies a single spreadsheet cell and each record is stored in a horizontal row of cells. Spreadsheets are usually discrete collections of individual records, and the records are usually not related to records in other spreadsheets.

• File-based databases or server-based databases, in which groups of records (known in the database world as tables) are commonly related to each other in some way—for example, a table of customer names and a table of those customers’ orders. Server-based databases usually provide more robust data storage and administration features, and they also support more concurrent data analysis tasks than do file-based databases.

Understanding these data formats and how they promote (or prohibit) data analysis will help you to better formulate your approach to analyzing data. This understanding is important because your organization likely has structured data such as tables inside Microsoft Word documents or Microsoft PowerPoint slides, e-mail messages in Microsoft Outlook folder views, or data stored in files created with other software applications that are not primarily designed for data analysis. An explanation of how to use Microsoft data analysis software features with these types of data is outside the scope of this book.

Note

You can still use Microsoft data analysis software to work with data stored in these formats. To do so, you should first export the data to a Microsoft data analysis software application such as Excel or Access. Consult your specific product’s documentation to see whether exporting data for this aim is possible, and if so, how to do it.

The following table lists the Microsoft software applications you should use to analyze data in a specific format. Later in this chapter, I’ll cover specific uses and features of these applications in more detail.

Data Formats	Data Storage Applications	Data Analysis Applications
Delimited text (for example, a comma-separated value file) or single or multiple unrelated row- and-column spreadsheets or data tables	Microsoft Excel Microsoft Access Microsoft SQL Server 2000	Microsoft Excel Microsoft Access Microsoft Office Web Components
Multiple related row-and-column data tables	Microsoft Access Microsoft SQL Server 2000	Microsoft Access Microsoft Office Web Components
Online analytical processing (OLAP) data	Microsoft SQL Server 2000 Analysis Services	Microsoft Excel Microsoft Office Web Components Microsoft Data Analyzer

Note

The concepts defining OLAP data and data analysis strategies are covered in Chapters 7, 8, and 9.