PPB August 2018

you can performmath, such as adding up revenue, expenses or order count. Dimensions are fields that characterize measures; e.g, products, customers or sales region. Sales revenue displayed in the measure field without any dimension simply displays the total revenue for all the records in the database. But when shown in combination with a dimension, you will see the revenue broken down by whatever dimension you choose. The products dimension shows revenue for each product you sell. The sales region dimension shows revenue for each of your sales regions, and the customer dimension shows revenue by customer. You can also show multiple dimensions together for additional insights. By combining products, customers and sales regions together with the revenue measure, you can see a detailed breakdown of revenue by customer for each region and product. What Can You Do With A BI Program? Now that you know a little about how BI programs work, let’s discuss what you can do with one. When you want to display data in a BI program, you simply drag data fields onto the worksheet. You can display your data in columnar format, like a spreadsheet, or as a graphic. Numeric formats can be very informative, but graphics can help data come alive in ways that help you visualize trends, especially when the underlying data consist of thousands or even millions of records. Most users choose a combination of both, depending of what the worksheet is designed to analyze or communicate. The real magic happens when you combine multiple sheets onto a single dashboard, each in its own window. In multi-window dashboards, as you select something in one window, the other windows can be programmed to automatically show you related underlying data. As an example, say you’re a supplier who sells products in multiple categories and you want a dashboard that enables you to analyze your sales each day by category, by customer, by product and by order. In this case you would create a dashboard with five windows. In window one, you would show your daily revenue, perhaps comparing each day this year with the same day last year. In window two, you show revenue by product. In window three, you show sales by customer. In window four, you show sales by product category. In window five, you show sales by order. Each window shows the same total revenue, but from a different perspective. Start your analysis by looking at your business by customer. Click on a customer in window three, and the other windows will automatically filter to that customer’s records. Window one will still show daily sales filtered to show only the sales from the customer you selected in window three. Window two will filter to show only the products that customer has purchased. Window four will show the product categories that customer is purchasing and window five will show you only that customer’s orders. This customer-centric view is exactly the information you want before making a sales call on this customer, but it also provides much more. It shows you the categories and products the customer has been purchasing, including the number of orders and average order size, and lets you compare these metrics year over year. Are sales increasing? Are orders increasing but revenue is not? Did the customer previously purchase certain categories from your business but is now getting those from a competitor? These and similar analytic questions can form the basis of a strategic approach to selling each account, or group of accounts if you’re evaluating a buying group or a national account. Now, change your perspective to look at your results by product. Click on a product in window two. Window one will now be filtered to show your revenue for that product day by day. Window three will show you which customers are buying that product. If you choose to display two years of data, you’ll be able to see who bought the product last year but didn’t buy this year, so you can more effectively target your sales plan. Window five will show you every order, so you can compare last year’s order quantity to this year’s or any other related metrics to help you understand how the market has changed over time. These examples are just a hint of what Data Analytics | FEATURE | AUGUST 2018 | 55