Excel® Dashboards & Reports For Dummies®, 3rd Edition
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The term business intelligence (BI), coined by Howard Dresner of Gartner, Inc., describes the set of concepts and methods to improve business decision-making by using fact-based support systems. Practically speaking, BI is what you get when you analyze raw data and turn that analysis into knowledge. BI can help an organization identify cost-cutting opportunities, uncover new business opportunities, recognize changing business environments, identify data anomalies, and create widely accessible reports.
Over the past few years, the BI concept has overtaken corporate executives who are eager to turn impossible amounts of data into knowledge. As a result of this trend, whole industries have been created. Software vendors that focus on BI and dashboarding are coming out of the woodwork. New consulting firms touting their BI knowledge are popping up virtually every week. And even the traditional enterprise solution providers, like Business Objects and SAP, are offering new BI capabilities.
This need for BI has manifested itself in many forms. Most recently, it has come in the form of dashboard fever. Dashboards are reporting mechanisms that deliver business intelligence in a graphical form.
Maybe you’ve been hit with dashboard fever. Or maybe your manager is hitting you with dashboard fever. Nevertheless, you’re probably holding this book because you’re being asked to create BI solutions (that is, dashboards) in Excel.
Although many IT managers would scoff at the thought of using Excel as a BI tool, Excel is inherently part of the enterprise BI tool portfolio. Whether or not IT managers are keen to acknowledge it, most of the data analysis and reporting done in business today is done by using a spreadsheet. You have several significant reasons to use Excel as the platform for your dashboards and reports, including
All that being said, it’s true that Excel has so many reporting functions and tools that it’s difficult to know where to start. Enter your humble author, spirited into your hands via this book. Here, I show you how you can turn Excel into your own, personal BI tool. Using a few fundamentals and some of the new BI functionality that Microsoft has included in this latest version of Excel, you can go from reporting data with simple tables to creating meaningful reporting components that are sure to wow management.
The goal of this book is to show you how to leverage Excel functionality to build and manage better reporting mechanisms. Each chapter in this book provides a comprehensive review of the technical and analytical concepts that help you create better reporting components — components that can be used for both dashboards and reports. It’s important to note that this book is not a guide to visualizations or dashboarding best practices — although those subjects are worthy of their own book. This book is focused on the technical aspects of using Excel’s various tools and functionality and applying them to reporting.
The chapters in this book are designed to be stand-alone chapters that you can selectively refer to as needed. As you move through this book, you’ll be able to create increasingly sophisticated dashboard and report components. After reading this book, you’ll be able to
I make three assumptions about you as the reader. I assume that you
The chapters in this book are organized into six parts. Each of these parts includes chapters that build on the previous chapters’ instruction. The idea is that as you go through each part, you will be able to build dashboards of increasing complexity until you’re an Excel reporting guru.
Part I is all about helping you think about your data in terms of creating effective dashboards and reports. Chapter 1 introduces you to the topic of dashboards and reports, giving you some of the fundamentals and basic ground rules for creating effective dashboards and reports. Chapter 2 shows you a few concepts around data structure and layout. In this chapter, you will learn the impact of a poorly planned data set and will discover the best practices for setting up the source data for your dashboards and reports.
In Part II, you take an in-depth look at some of the basic dashboard components you can create using Excel. Chapter 3 starts you off with some fundamentals around designing effective data tables. Chapter 4 shows you how you can leverage the Sparkline functionality found in Excel. Chapter 5 provides a look at the various techniques that you can use to visualize data without the use of charts or graphs. Chapter 6 rounds out this section of the book by introducing you to pivot tables and discussing how a pivot table can play an integral role in Excel-based dashboards.
In Part III you go beyond the basics to take a look at some of the advanced chart components you can create with Excel. This part consists of three chapters, starting with Chapter 7, where I demonstrate how to represent time trending, seasonal trending, moving averages and other types of trending in dashboards. In Chapter 8, you explore the many methods used to bucket data — putting data into groups for reporting, in other words. Finally, Chapter 9 demonstrates some of charting techniques that can help you display and measure values versus goals.
Part IV focuses on techniques that can help you automate your reporting processes, and give your users an interactive user interface. Chapter 10 provides a clear understanding of how macros can be leveraged to supercharge and automate your reporting systems. Chapter 11 illustrates how you can provide your clients with a simple interface, allowing them to easily navigate through (and interact with) their reporting systems. Chapter 12 shows you how pivot slicers can add interactive filtering capabilities to your pivot reporting.
The theme in Part V is importing and exporting information to and from Excel. Chapter 13 explores some of the ways to incorporate data that does not originate in Excel. In this chapter, you find out how to import data from external sources as well as create systems that allow for dynamic refreshing of external data sources. Chapter 14 wraps up this book on Excel dashboards and reports by showing you the various ways to distribute and present your work.
Part VI is the classic Part of Tens section found in Dummies series titles. The chapters found here each present ten or more pearls of wisdom, delivered in bite sized pieces. In Chapter 15, I share with you ten or so chart-building best practices, helping you design more effective charts. Chapter 16 offers a run-down of the ten most commonly used chart types, along with advice on when to use each one.
As you read this book, you’ll see icons in the margins that indicate material of interest (or not, as the case may be). This section briefly describes each icon in this book.
Tips are nice because they help you save time or perform a task without having to do a lot of extra work. The tips in this book are time-saving techniques or pointers to resources that you should try in order to get the maximum benefit from Excel.
Try to avoid doing anything marked with a Warning icon, which (as you might expect) represents a danger of one sort or another.
Whenever you see this icon, think advanced tip or technique. You might find these tidbits of useful information too boring for words, or they could contain the solution you need to get a program running. Skip these bits of information whenever you like.
If you don’t get anything else out of a particular chapter or section, remember the material marked by this icon. This text usually contains an essential process or a bit of information you ought to remember.
A lot of extra content that you won’t find in this book is available at www.dummies.com. Go online to find the following:
www.dummies.com/extras/exceldashboardsreports
This book contains a lot of exercises in which you create and modify tables and Excel workbook files. If you want to follow the exercise but don’t have time to, say, create your own data table, just download the data from the Dummies.com website at www.dummies.com/extras/exceldashboardsreports. The files are organized by chapter.
www.dummies.com/extras/exceldashboardsreports
At this page, you’ll find out how to use conditional formatting to build annotations into your charts, add an extra dynamic layer of analysis to your charts, and create dynamic labels, among other details to aid you in your Excel dashboards journey.
www.dummies.com/cheatsheet/exceldashboardsreports
Here, you’ll find an extra look at how you can use fancy fonts like Wingdings and Webdings to add visualizations to your dashboards and reports. You’ll also find a list of websites you can visit to get ideas and fresh new perspectives on building dashboards.
www.dummies.com/extras/exceldashboardsreports
It’s time to start your Excel dashboarding adventure! If you’re a complete dashboard novice, start with Chapter 1 and progress through the book at a pace that allows you to absorb as much of the material as possible. If you’re an Excel whiz, skip to Part III, which covers advanced topics.
Part I
Go to www.dummies.com for great Dummies content online.
In this part …
Discover how to think about your data in terms of creating effective dashboards and reports.
Get a solid understanding of the fundamentals and basic ground rules for creating effective dashboards and reports.
Uncover the best practices for setting up the source data for your dashboards and reports.
Explore the key Excel functions that help you build effective dashboard models.
Chapter 1
In This Chapter
Comparing dashboards to reports
Getting started on the right foot
Dashboarding best practices
In his song “New York State of Mind,” Billy Joel laments the differences between California and New York. In this homage to the Big Apple, he implies a mood and a feeling that come with thinking about New York. I admit it’s a stretch, but I’ll extend this analogy to Excel — don’t laugh.
In Excel, the differences between building a dashboard and creating standard table-driven analyses are as great as the differences between California and New York. To approach a dashboarding project, you truly have to get into the dashboard state of mind. As you’ll come to realize in the next few chapters, dashboarding requires far more preparation than standard Excel analyses. It calls for closer communication with business leaders, stricter data modeling techniques, and the following of certain best practices. It’s beneficial to have a base familiarity with fundamental dashboarding concepts before venturing off into the mechanics of building a dashboard.
In this chapter, you get a solid understanding of these basic dashboard concepts and design principles as well as what it takes to prepare for a dashboarding project.
It isn’t difficult to use report and dashboard interchangeably. In fact, the line between reports and dashboards frequently gets muddied. I’ve seen countless reports referred to as dashboards just because they included a few charts. Likewise, I’ve seen many examples of what could be considered dashboards but have been called reports.
Now, this may all seem like semantics to you, but it’s helpful to clear the air and understand the core attributes of what are considered to be reports and dashboards.
The report is probably the most common application of business intelligence. A report can be described as a document that contains data used for reading or viewing. It can be as simple as a data table or as complex as a subtotaled view with interactive drill-downs, similar to Excel’s Subtotal or Pivot Table functionality.
The key attribute of a report is that it doesn’t lead a reader to a predefined conclusion. Although reports can include analysis, aggregations, and even charts, reports often allow for the end users to apply their own judgment and analysis to the data.
To clarify this concept, Figure 1-1 shows an example of a report. This report shows the National Park overnight visitor statistics by period. Although this data can be useful, it’s clear this report isn’t steering the reader toward any predefined judgment or analysis; it’s simply presenting the aggregated data.
Figure 1-1: Reports present data for viewing but don’t lead readers to conclusions.
A dashboard is a visual interface that provides at-a-glance views into key measures relevant to a particular objective or business process. Dashboards have three main attributes:
Figure 1-2 illustrates a dashboard that uses the same data shown in Figure 1-1. This dashboard displays key information about the national park overnight-visitor stats. As you can see, this presentation has all the main attributes that define a dashboard. First, it’s a visual display that allows you to quickly recognize the overall trending of the overnight-visitor stats. Second, you can see that not all the detailed data is shown here — you see only the key pieces of information relevant to support the goal of this dashboard, which in this case would be to get some insights on which parks would need some additional resources to increase visitor rates. Finally, by virtue of its objective, this dashboard effectively presents you with analysis and conclusions about the trending of overnight visitors.
Figure 1-2: Dashboards provide at-a-glance views into key measures relevant to a particular objective or business process.
Imagine that your manager asks you to create a dashboard that tells him everything he should know about monthly service subscriptions. Do you jump to action and slap together whatever comes to mind? Do you take a guess at what he wants to see and hope it’s useful? These questions sound ridiculous, but these types of situations happen more than you think. I’m continually called to create the next great reporting tool but am rarely provided the time to gather the true requirements for it. Between limited information and unrealistic deadlines, the end product often ends up being unused or having little value.
This brings me to one of the key steps in preparing for dashboarding: collecting user requirements.
In the non-IT world of the Excel analyst, user requirements are practically useless because of sudden changes in project scope, constantly changing priorities, and shifting deadlines. The gathering of user requirements is viewed to be a lot of work and a waste of valuable time in the ever-changing business environment. But as I mention at the start of this chapter, it’s time to get into the dashboard state of mind.
Consider how many times a manager has asked you for an analysis and then said “No, I meant this.” Or “Now that I see it, I realize I need this.” As frustrating as this can be for a single analysis, imagine running into it again and again during the creation of a complex dashboard with several data integration processes. The question is, would you rather spend your time on the front end gathering user requirements or spend time painstakingly redesigning the dashboard you’ll surely come to hate?
The process of gathering user requirements doesn’t have to be an overly complicated or formal one. Here are some simple things you can do to ensure you have a solid idea of the purpose of the dashboard.
Chances are your manager has been asked to create the reporting mechanism and he has passed the task to you. Don’t be afraid to ask about the source of the initial request. Talk to the requesters about what they’re asking for. Discuss the purpose of the dashboard and the triggers that caused them to ask for a dashboard in the first place. You may find, after discussing the matter, that a simple Excel report meets their needs, foregoing the need for a full-on dashboard.
If a dashboard is indeed warranted, talk about who the end users are. Take some time to meet with a few of the end users to talk about how they’d use the dashboard. Will the dashboard be used as a performance tool for regional managers? Will the dashboard be used to share data with external customers? Talking through these fundamentals with the right people helps align your thoughts and avoids the creation of a dashboard that doesn’t fulfill the necessary requirements.
Most dashboards are designed around a set of measures, or key performance indicators (KPIs). A KPI is an indicator of the performance of a task deemed to be essential to daily operations or processes. The idea is that a KPI reveals performance that is outside the normal range for a particular measure, so it therefore often signals the need for attention and intervention. Although the measures you place into your dashboards may not officially be called KPIs, they undoubtedly serve the same purpose — to draw attention to problem areas.
The topic of creating effective KPIs for your organization is a subject worthy of its own book and is out of the scope of this endeavor. For a detailed guide on KPI development strategies, pick up David Parmenter’s Key Performance Indicators: Developing, Implementing, and Using Winning KPIs (Wiley Publishing, Inc.). That book provides an excellent step-by-step approach to developing and implementing KPIs.
The measures used on a dashboard should absolutely support the initial purpose of that dashboard. For example, if you’re creating a dashboard focused on supply chain processes, it may not make sense to have human resources head-count data incorporated. It’s generally good practice to avoid nice-to-know data in your dashboards simply to fill white space or because the data is available. If the data doesn’t support the core purpose of the dashboard, leave it out.
Here’s another tip: When gathering the measures required for the dashboard, I find that it often helps to write a sentence to describe the measure needed. For example, rather than simply add the word Revenue into my user requirements, I write what I call a component question, such as “What is the overall revenue trend for the past two years?” I call it a component question because I intend to create a single component, such as a chart or a table, to answer the question. For instance, if the component question is “What is the overall revenue trend for the past two years?” you can imagine a chart component answering this question by showing the two-year revenue trend.
I sometimes take this a step further and actually incorporate the component questions into a mock layout of the dashboard to get a high-level sense of the data the dashboard will require. Figure 1-3 illustrates an example.
Figure 1-3: Each box in this dashboard layout mockup represents a component and the type of data required to create the measures.
Each box in this dashboard layout mockup represents a component on the dashboard and its approximate position. The questions within each box provide a sense of the types of data required to create the measures for the dashboard.
When you have the list of measures that need to be included on the dashboard, it’s important to take a tally of the available systems to determine whether the data required to produce those measures is available. Ask yourself the following questions:
These are all questions you need answered when negotiating dashboard development time, data refresh intervals, and change management.
Conventional wisdom says that the measures on your dashboard shouldn’t be governed by the availability of data. Instead, you should let dashboard KPIs and measures govern the data sources in your organization. Although I agree with the spirit of that statement, I’ve been involved in too many dashboard projects that have fallen apart because of lack of data. Real-world experience has taught me the difference between the ideal and the ordeal.
If your organizational strategy requires that you collect and measure data that is nonexistent or not available, press Pause on the dashboard project and turn your attention to creating a data collection mechanism that will get the data you need.
In the context of reporting, a dimension is a data category used to organize business data. Examples of dimensions are Region, Market, Branch, Manager, or Employee. When you define a dimension in the user requirements stage of development, you’re determining how the measures should be grouped or distributed. For example, if your dashboard should report data by employee, you need to ensure that your data collection and aggregation processes include employee detail. As you can imagine, adding a new dimension after the dashboard is built can get complicated, especially when your processes require many aggregations across multiple data sources. The bottom line is that locking down the dimensions for a dashboard early in the process definitely saves you headaches.
Along those same lines, you want to get a clear sense of the types of filters that are required. In the context of dashboards, filters are mechanisms that allow you to narrow the scope of the data to a single dimension. For example, you can filter on Year, Employee, or Region. Again, if you don’t account for a particular filter while building your dashboarding process, you’ll likely be forced into an unpleasant redesign of both your data collection processes and your dashboard.
If you’re confused by the difference between dimensions and filters, think about a simple Excel table. A dimension is like a column of data (such as a column containing employee names) in an Excel table. A filter, then, is the mechanism that allows you to narrow your table to show only the data for a particular employee. For example, if you apply Excel’s AutoFilter to the Employee column, you are building a filter mechanism into your table.
Many dashboards provide drill-down features that allow users to “drill” into the details of a specific measure. You want to get a clear understanding of the types of drill-downs your users have in mind.
To most users, drill-down feature means the ability to get a raw data table supporting the measures shown on the dashboard. Although getting raw data isn’t always practical or possible, discussing these requests will, at minimum, allow you to talk to your users about additional reporting, links to other data sources, and other solutions that may help them get the data they need.
A refresh schedule refers to the schedule by which a dashboard is updated to show the latest information available. Because you’re the one responsible for building and maintaining the dashboard, you should have a say in the refresh schedules — your manager may not know what it takes to refresh the dashboard in question.
While you’re determining the refresh schedule, keep in mind the refresh rates of the different data sources whose measures you need to get. You can’t refresh your dashboard any faster than your data sources. Also, negotiate enough development time to build macros that aid in automation of redundant and time-consuming refresh tasks.