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without requiring the data to be summarized or prepared to fit into Excel. Also, if a process ever becomes more crucial to the organization and needs to be tracked in a more enterprise-acceptable environment, it will be easier to upgrade and scale up if that process is already in a relational database system.

      Transparency of analytical processes

      One of Excel’s most attractive features is its flexibility. Each individual cell can contain text, a number, a formula, or practically anything else the customer defines. Indeed, this is one of the fundamental reasons that Excel is an effective tool for data analysis. Customers can use named ranges, formulas, and macros to create an intricate system of interlocking calculations, linked cells, and formatted summaries that work together to create a final analysis.

      So what is the problem? The problem is that there is no transparency of analytical processes. It is extremely difficult to determine what is actually going on in a spreadsheet. Anyone who has had to work with a spreadsheet created by someone else knows all too well the frustration that comes with deciphering the various gyrations of calculations and links being used to perform analysis. Small spreadsheets that are performing modest analysis are painful to decipher, and large, elaborate, multi-worksheet workbooks are virtually impossible to decode, often leaving you to start from scratch.

      Compared to Excel, database systems might seem rigid, strict, and unwavering in their rules. However, all this rigidity comes with a benefit.

      Because only certain actions are allowable, you can more easily come to understand what is being done within structured database objects such as queries or stored procedures. If a dataset is being edited, a number is being calculated, or any portion of the dataset is being affected as part of an analytical process, you can readily see that action by reviewing the query syntax or the stored procedure code. Indeed, in a relational database system, you never encounter hidden formulas, hidden cells, or dead named ranges.

      Separation of data and presentation

      Data should be separate from presentation; you don’t want the data to become too tied into any particular way of presenting it. For example, when you receive an invoice from a company, you don’t assume that the financial data on that invoice is the true source of your data. It is a presentation of your data. It can be presented to you in other manners and styles on charts or on websites, but such representations are never the actual source of the data.

      What exactly does this concept have to do with Excel? People who perform data analysis with Excel tend, more often than not, to fuse the data, the analysis, and the presentation. For example, you often see an Excel workbook that has 12 worksheets, each representing a month. On each worksheet, data for that month is listed along with formulas, pivot tables, and summaries. What happens when you’re asked to provide a summary by quarter? Do you add more formulas and worksheets to consolidate the data on each of the month worksheets? The fundamental problem in this scenario is that the worksheets actually represent data values that are fused into the presentation of the analysis.

      The point being made here is that data should not be tied to a particular presentation, no matter how apparently logical or useful it may be. However, in Excel, it happens all the time.

      In addition, as discussed earlier in this chapter, because all manners and phases of analysis can be done directly within a spreadsheet, Excel cannot effectively provide adequate transparency to the analysis. Each cell has the potential to hold formulas, be hidden, and contain links to other cells. In Excel, this blurs the line between analysis and data, which makes it difficult to determine exactly what is going on in a spreadsheet. Moreover, it takes a great deal of effort in the way of manual maintenance to ensure that edits and unforeseen changes don’t affect previous analyses.

      Relational database systems inherently separate analytical components into tables, queries, and reports. By separating these elements, databases make data less sensitive to changes and create a data analysis environment in which you can easily respond to new requests for analysis without destroying previous analyses.

      You may find that you manipulate Excel’s functionalities to approximate this database behavior. If so, you must consider that if you’re using Excel’s functionality to make it behave like a database application, perhaps the real thing just might have something to offer. Utilizing databases for data storage and analytical needs would enhance overall data analysis and would allow Excel power customers to focus on the presentation in their spreadsheets.

In these days of big data, customers demand more, not less, complex data analysis. Excel analysts will need to add tools to their repertoires to avoid being simply “spreadsheet mechanics.” Excel can be stretched to do just about anything, but maintaining such creative solutions can be a tedious manual task. You can be sure that the sexy aspect of data analysis does not lie in the routine data management within Excel; rather, it lies in leveraging BI Tools such as providing clients with the best solution for any situation.

Getting to Know Database Terminology

      The terms database, table, record, field, and value indicate a hierarchy from largest to smallest. These same terms are used with virtually all database systems, so you should learn them well.

      Databases

      Generally, the word database is a computer term for a collection of information concerning a certain topic or business application. A database helps you organize this related information in a logical fashion for easy access and retrieval. Certain older database systems used the term database to describe individual tables. The current use of database applies to all elements of a database system.

      Databases aren’t only for computers. Manual databases are sometimes referred to as manual filing systems or manual database systems. These filing systems usually consist of people, papers, folders, and filing cabinets — paper is the key to a manual database system. In a real-life manual database system, you probably have in-baskets and out-baskets and some type of formal filing method. You access information manually by opening a file cabinet, removing a file folder, and finding the correct piece of paper. Customers fill out paper forms for input, perhaps by using a keyboard to input information that is printed on forms. You find information by manually sorting the papers or by copying information from many papers to another piece of paper (or even into an Excel spreadsheet). You may use a spreadsheet or calculator to analyze the data or display it in new and interesting ways.

      Tables

A database stores information in a carefully defined structure known as a table. A table is just a container for raw information (called data), similar to a folder in a manual filing system. Each table in a database contains information about a single entity, such as a person or product, and the data in the table is organized into rows and columns. A relational database system stores data in related tables. For example, a table containing employee data (names and addresses) may be related to a table containing payroll information (pay date, pay amount, and check number).

      To use database wording, a table is an object. As you design and work with databases, it’s important to see each table as a unique entity and to see how each table relates to the other objects in the database.

      In most database systems, you can view the contents of a table in a spreadsheet-like form called a datasheet, composed of rows and columns (known as records and fields, respectively — see the following section). Although a datasheet and a spreadsheet are superficially similar, a datasheet is quite a different type of object. You typically cannot make changes or add calculations directly within a table. Your interaction with tables will primarily come in the form of queries or views — see the later section “Queries”.

      Records, fields, and values

      A

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