What is the fundamental challenge of dashboard design

Chapter 3 • Nature of Data, Statistical Modeling, and Visualization 185

of thousands of BI dashboards, scorecards, and BI interfaces used by businesses of all sizes and industries, nonprofits, and government agencies.

According to Eckerson (2006), a well-known expert on BI in general and dash- boards in particular, the most distinctive feature of a dashboard is its three layers of information:

1. Monitoring: Graphical, abstracted data to monitor key performance metrics. 2. Analysis: Summarized dimensional data to analyze the root cause of problems. 3. Management: Detailed operational data that identify what actions to take to re-

solve a problem.

Because of these layers, dashboards pack a large amount of information into a sin- gle screen. According to Few (2005), “The fundamental challenge of dashboard design is to display all the required information on a single screen, clearly and without distraction, in a manner that can be assimilated quickly.” To speed assimilation of the numbers, they need to be placed in context. This can be done by comparing the numbers of interest to other baseline or target numbers, by indicating whether the numbers are good or bad, by denoting whether a trend is better or worse, and by using specialized display widgets or components to set the comparative and evaluative context. Some of the common comparisons that are typically made in BI systems include comparisons against past val- ues, forecasted values, targeted values, benchmark or average values, multiple instances of the same measure, and the values of other measures (e.g., revenues versus costs).

Even with comparative measures, it is important to specifically point out whether a particular number is good or bad and whether it is trending in the right direction. Without these types of evaluative designations, it can be time consuming to determine the status of a particular number or result. Typically, either specialized visual objects (e.g., traffic lights, dials, and gauges) or visual attributes (e.g., color coding) are used to set the evalu- ative context. An interactive dashboard-driven reporting data exploration solution built by an energy company is featured in Application Case 3.8.

Energy markets all around the world are going through a significant change and transformation, creating ample opportunities along with significant challenges. As is the case in any industry, oppor- tunities are attracting more players in the market- place, increasing the competition, and reducing the tolerances for less-than-optimal business decision making. Success requires creating and disseminat- ing accurate and timely information to whomever whenever it is needed. For instance, if you need to easily track marketing budgets, balance employee workloads, and target customers with tailored mar- keting messages, you would need three different reporting solutions. Electrabel GDF SUEZ is doing all of that for its marketing and sales business unit with SAS'Analytics Visual Analytics platform.

The one-solution approach is a great time-saver for marketing professionals in an industry that is undergoing tremendous change. “It is a huge chal- lenge to stabilize our market position in the energy market. That includes volume, prices, and margins for both retail and business customers,” notes Danny Noppe, manager of Reporting Architecture and Development in the Electrabel Marketing and Sales business unit. The company is the largest supplier of electricity in Belgium and the largest producer of elec- tricity for Belgium and the Netherlands. Noppe says it is critical that Electrabel increase the efficiency of its customer communications as it explores new digital channels and develops new energy-related services.

“The better we know the customer, the bet- ter our likelihood of success,” he says. “That is why

Application Case 3.8 Visual Analytics Helps Energy Supplier Make Better Connections

(Continued )

186 Part I • Introduction to Analytics and AI

What to Look for in a Dashboard

Although performance dashboards and other information visualization frameworks differ, they all share some common design characteristics. First, they all fit within the larger BI and/or performance measurement system. This means that their underlying architecture is the BI or performance management architecture of the larger system. Second, all well- designed dashboards and other information visualizations possess the following charac- teristics (Novell, 2009):

we combine information from various sources— phone traffic with the customer, online questions, text messages, and mail campaigns. This enhanced knowledge of our customer and prospect base will be an additional advantage within our competitive market.”

One Version of the Truth

Electrabel was using various platforms and tools for reporting purposes. This sometimes led to ambigu- ity in the reported figures. The utility also had per- formance issues in processing large data volumes. SAS Visual Analytics with in-memory technology removes the ambiguity and the performance issues. “We have the autonomy and flexibility to respond to the need for customer insight and data visualization internally,” Noppe says. “After all, fast reporting is an essential requirement for action-oriented depart- ments such as sales and marketing.”

Working More Efficiently at a Lower Cost

SAS Visual Analytics automates the process of updating information in reports. Instead of building a report that is out of date by the time it is com- pleted, the data are refreshed for all the reports once a week and is available on dashboards. In deploying the solution, Electrabel chose a phased approach, starting with simple reports and moving on to more complex ones. The first report took a few weeks to build, and the rest came quickly. The successes include the following:

• Reduction of data preparation from two days to only two hours.

• Clear graphic insight into the invoicing and composition of invoices for business-to-busi- ness (B2B) customers.

• A workload management report by the op- erational teams. Managers can evaluate team

workloads on a weekly or long-term basis and can make adjustments accordingly.

“We have significantly improved our effi- ciency and can deliver quality data and reports more frequently, and at a significantly lower cost,” says Noppe. And if the company needs to combine data from multiple sources, the process is equally easy. “Building visual reports, based on these data marts, can be achieved in a few days, or even a few hours.”

Noppe says the company plans to continue broadening its insight into the digital behavior of its customers, combining data from Web analytics, e-mail, and social media with data from back-end systems. “Eventually, we want to replace all labor- intensive reporting with SAS Visual Analytics,” he says, adding that the flexibility of SAS Visual Analytics is critical for his department. “This will give us more time to tackle other challenges. We also want to make this tool available on our mobile devices. This will allow our account managers to use up-to-date, insightful, and adaptable reports when visiting cus- tomers. We’ve got a future-oriented reporting plat- form to do all we need.”

Questions for Case 3.8

1. Why do you think energy supply companies are among the prime users of information visualiza- tion tools?

2. How did Electrabel use information visualization for the single version of the truth?

3. What were their challenges, the proposed solu- tion, and the obtained results?

Source: SAS Customer Story, “Visual Analytics Helps Energy Supplier Make Better Connections.” http://www.sas.com/ en_us/customers/electrabel-be.html (accessed July 2018). Copyright © 2018 SAS Institute Inc., Cary, NC, United States. Reprinted with permission. All rights reserved.

Application Case 3.8 (Continued)