Using Neural Networks and OLAP Tools to Make Business Decisions
Neural networks (with and without external optimization) and online analytical processing (OLAP) are two mainstream tools for the efficient discovery of valuable non-obvious information from a large collection of data. Naturally, to be efficient, the information obtained must be worth more than the cost of processing the raw data. This article will focus on some ways these tools can be used to make better business decision. You will apply neural networks and OLAP separately to several business problems using a single data set.
Figure 1 shows possible paths between this raw data (shown at the left) and the tools (shown at the right). Between these extrema lies a data warehouse.
Figure 1: Overview of the system to be discussed throughout this article.
Data warehouses are designed to overcome problems encountered when an organization attempts to perform strategic analysis using the same database that is used for online transaction processing (OLTP).
Data warehousing is the technique of extracting archived operational data and overcoming inconsistencies between different legacy data formats. As well as integrating data throughout an enterprise, regardless of location, format, or communication requirements, a data warehouse can incorporate additional or expert information.
It is the logical link between what the managers see in their decision support applications and the company's operational activities.
After the setup of a data warehouse, attention is usually switched to data mining (a term given many different definitions that I will not attempt to reconcile), which aims to extract new and meaningful information. In other words, a pool of 'useful information' that has been stored in a company data warehouse becomes 'intelligent information.'
Microsoft Excel with Add-Ins from Palisade
For the implementation of the examples discussed throughout this article, I have chosen NeuralTools and Evolver from Palisade, two plug-ins to Microsoft Office Excel. There are a number of commercial alternatives and even some well-regarded and free open-source alternatives to these choices. However, NeuralTools and Evolver are widely used throughout academia and industry, can be combined with other Excel add-ins, and are complemented by the very flexible functionality of Excel itself.
I also cite Microsoft Project Server supported by SQL Server and Analysis Services. The data created by Microsoft Project Professional can be saved in Microsoft Project Server and accessed as user-defined OLAP cubes, as discussed in Appendix 5.
It is not always necessary to create a data warehouse for OLAP analysis. Data stored by operational systems, such as point-of-sales, are in types of databases called OLTPs. OLTP, Online Transaction Process, databases do not have any difference from a structural perspective from any other databases. The main difference is the way in which data is stored.
OLTPs are designed for optimal transaction speed. When a consumer makes a purchase online, they expect the transactions to occur instantaneously. With a database design, called data modeling, optimized for transactions, the record 'Consumer name, Address, Telephone, Order Number, Order Name, Price, Payment Method' is created quickly on the database and the results can be recalled by managers equally quickly if needed.
Examples of OLTPs can include ERP, CRM, SCM, Point-of-Sale applications, and Call Center.
Data are not typically stored for an extended period on OLTPs for storage cost and transaction speed reasons.
OLAPs have a different mandate from OLTPs. OLAPs are designed to give an overview analysis of what happened. Hence, the data storage (in other words, data modeling) has to be set up differently. The most common method is called the star design, as shown in the right-hand side of Figure 2.
Figure 2: Typical organization of data seen in OLTP databases (left) and OLAP data warehouse (right).
The central table in an OLAP start data model is called the fact table. The surrounding tables are called the dimensions. By using the above data model, it is possible to build reports that answer questions such as:
- Which supervisor gave the most discounts?
- What was the quantity shipped on a particular date, month, year, or quarter?
- In which zip code did product A sell the most?
To obtain answers, such as the ones above from a data model, OLAP cubes are created. OLAP cubes are not strictly cuboids—it is the name given to the process of linking data from the different dimensions. The cubes can be developed along business units, such as sales or marketing. Or, a giant cube can be formed with all the dimensions.