Microsoft Excel has proved itself to be a powerful development platform, and the need for material on advanced Excel development has never been greater. In recent years, however, few books have afforded developers the instruction and depth required to create advanced Excel applications. Two titles in particular, Professional Excel Development by Bullen, Bovey, and Green and Excel Dashboard and Reports for Dummies by Michael Alexander, have given serious focus to these subjects; yet a dearth of quality material still remains. Where printed material has failed to fill this gap, thriving communities and blogs have spawned on the Internet over the last few years devoted to Excel development. Naturally, the last few years have also seen growing interest in data visualization, business intelligence, and data analytics.

The Data Problem

We are so immersed in data that it’s hard to make sense of it all. So far, our solution has been to rely on products and people who purportedly transform this data into something we can digest; vendors, smartphones, computers, and even the news media slice and dice this information to make it palatable for us to consume. But even these devices and people can, and often do, fail to deliver; the institutions we’ve entrusted for guidance through this mess have not always had our interests in mind. Where we should have met their work with skepticism, they have imbued in us a false sense of security. Intentional or not, they have led us astray.

Thus, we exist at an important and exciting time for professional Excel developers. With knowledge of Microsoft Excel spreadsheets, formulas and Visual Basic for Applications, and the desire to learn, developers can create powerful data and visualization platforms that rival the work performed by vendors. In other words, with the right tools—and the right mind-set—we can take control of our data. We can take the power back from the vendors.

However, the desire for more data visualization and analytics and the ability to create these products in Excel has not always translated into better products, especially in the last few years. Data visualization is often misunderstood by the vendors that sell it. This misunderstanding has crept its way into Excel. Some vendors and even Excel blogs sell products or instruct users on how to create fancy but useless widgets that sparkle with metallic finishes in three dimensions. But all that glitters is not gold. Just because we can add these things to our Excel spreadsheets does not mean we should.

At its core, the problem stems from a lack of clarity. At every turn we are confounded by information. When products and research fail to make us smarter, they add to our confusion. If we are to see clearly through the data storm, we must stick to what we know—what the research tells us is true. This book will not teach you how to make the newest flashy gadgets; instead, it will teach you established principles based on proven research. We will continue down the path so rarely taken by vendors: the path of research and best practices. Think of it as an adventure.

Enter Excel: The Most Dangerous Program in the World

Worstall is referring to JPMorgan’s use of Microsoft Excel and its potential contribution to the global recession that began in 2008. The story is a familiar one. JPMorgan, one of the big banks, required a new way for their chief investment officer to model the risk associated with their portfolio of collateralized debt obligations (CDOs) . CDOs, as you might recall, are those complex financial instruments that are comprised of a bundle of mortgages used to speculate and manage the risk for default. In the end, many homeowners eventually defaulted on their mortgages. Yet, JPMorgan’s model had not captured this at all. According to an internal report by JPMorgan, their model contained an error that “likely had the effect of muting volatility by a factor of two and of lowering the VaR [value at risk] .”

What had happened? How could one of the biggest banks employ a model that was so absent from reality? According to the report, their model was “operated through a series of Excel spreadsheets, which had to be completed manually, by a process of copying and pasting data from one spreadsheet to another.” The error was generated when “After subtracting the old rate from the new rate, the spreadsheet divided by their sum instead of their average, as the modeler had intended.”

Worstall, like many others, blames Excel. It’s too insecure, and there’s no audit trail, he contends. Its flexibility is its greatest strength and Achilles heel. His argument isn’t a new one, of course. Without proper internal governance and procedures regarding archiving, getting lost in a nightmare of error-ridden spreadsheets and mixed-up versions is a real possibility.

But is that really what happened in the case of JPMorgan? Are more audit controls needed? I’m not so sure.

Not Realizing How Far Spreadsheets Have Come

Many organizations still operate with the mind-set that Excel is an incredibly hard tool to use. Early versions of Excel may have required manual updates, but these days Excel provides a lot of efficient and easy ways to pull in information. As well, what reason was there to maintain many different spreadsheets? Why did they require someone to copy and paste the information with every update to back-end data? Macros easily streamline these rote activities.

It’s troubling then to think a large financial institutional such as JPMorgan didn’t choose to use all the capabilities available in Excel. Instead, they tended to use it in a very antiquated way. But more than that, I submit that when you don’t take advantage of the features that exist to address your needs, not only are you using Excel inefficiently, you’re using it incorrectly—and dangerously.

One of the main reasons I still see organizations operate with the mind-set that they need to maintain many different spreadsheets with manual updates is because they want a historical imprint of every piece of information for every step in the process. These files become the historical records, or in other words, the audit trail of changes. According to their internal report, JPMorgan knew that operating with several spreadsheets was foolhardy. But for many organizations—and quite possibly for JPMorgan—operating with several spreadsheets can give the appearance of having a trail of changes. JPMorgan knew they had to automate the model’s process but never did. Where was the urgency?

Are more audit controls to track every change really the solution? It would have certainly helped. But I don’t think it addresses the problem in full.

Garbage In, Gospel Out

Another major contributing factor was that nobody seemed interested in validating the numbers the model was spitting out. Even when their numbers seemed different from that of the other banks, JPMorgan failed to properly validate the model to verify its calculations were correct. Why? One reason might be because JPMorgan liked the numbers coming out of the model.

This is a common problem in my experience. We’re less likely to double-check results that seem viscerally desirable. Often these results take the form of these three cases:

• When the models provide information that appears consistent with everyone else’s numbers, we may meet the numbers with less skepticism.
• When our models provide information that is counterintuitive, we may again be less skeptical because we think we might have found a counterintuitive response.
• When our models seem to average information together, giving us a grand bargain between one or more competing ideas, we may think we have results that everyone should like.

Part of the problem is how we view models. If a lot of money and research goes into developing a model, we’re less likely to turn our backs on all of the work we’ve performed even when we suspect there are issues. We might think we can save it, if only we had more time and resources. However, some work is just not usable when we realize it’s wrong. We can try to ignore its results, but it’s hard to ignore results even when we know they’re bad. Think of it as an incorrect entry on a crossword puzzle written in ink: even when we want to ignore it, we often forget that the entry is wrong. Bad models affect our ability to judge in the same way.

When our model delivers results that are incorrect, however seemingly truthful, the model can be described as being “garbage in, garbage out.” When management believes the results no matter what, we might describe management’s behavior as “garbage in, gospel out.”

How Excel Fits In

The unfortunate reality is that Excel cannot make decisions for us. We cannot use it like a crystal ball to gaze for the truth into the future. We cannot pretend that disparate and conflicting information might form a real fluent idea as if we were reading tea leaves in high resolution. In fact, no program, no mechanism, and no promise however earnest can do this for us.

We should start by knowing what Excel can and cannot do for us. If we use Excel correctly, we can bring important decision-making insight to our management. If we try to turn it into something it’s not, we might mistakenly misunderstand or even waste its potential. At best, if we misuse our tools, we make a mistake that is well understood and correct it immediately; at worse, we follow down a path of bad results unaware that they are bad.

What Excel Is Good For

There are several good reasons why Excel might be useful for our project. Among other reasons, Excel is

• A no-nonsense commercial-off-the-shelf (COTS) solution
• Flexible and customizable for our needs
• Inexpensive-ish
• Familiar to many in the industry
• A good return on investment
• Mostly backward compatibility

A Commercial-Off-the-Shelf Solution

Commercial-off-the-shelf (COTS) software is a common term among consumers of software and information systems. In particular, the U.S. federal government holds a special designation for COTS software packages. The reason COTS solutions are preferred is their name: they’re “off the shelf.” By that, we mean that COTS products are essentially ready for us to use as if we purchased them off the shelf at a retailer. Most COTS products have some assembly required, but they represent a piece of software that doesn’t require a significant undertaking and investment into infrastructure to implement, train users, and maintain operations.

And this is where Excel really brings the advantage. There are many data visualization packages out there, and many of them require a certain amount of data architecture or “business intelligence” to implement. Excel doesn’t require any of these technologies to begin building dashboards and powerful data visualizations.

Flexible and Customizable

Even though we can characterize Excel as an off-the-shelf solution, it still brings a strong amount of flexibility and customizability. By flexible, I mean that Excel gives you many options in which you present and visualize your data and analysis. By customizable, I mean that you are able to do things to your spreadsheet to make it indiscernible from a regular piece of software. For example, you can protect certain cells from user input. You can even remove the tab banner at the top of the screen when you send out your dashboard. These things allow you to essentially modify the user experience from looking at an Excel spreadsheet to looking at a dashboard.

Familiarity and Ubiquity

Excel is ubiquitous. Currently, Microsoft Excel is the most widely used spreadsheet platform across the world. With familiarity comes less training. Because Excel is so widely used in industry, chances are your customers don’t need intense training to just pick up your work and use it right away. As well, your Excel solutions should work on any computer that also has Excel because the solutions are hardware independent (to a certain extent—there are notable limitations, and we’ll talk about how to overcome some of them later in the book). Finally, because many users are often behind—that is, they are using versions of Excel older than the most recent one available—Microsoft, as a general rule, makes worksheets fairly backward compatible. Even if you develop in a new version, chances are most if not all of the functionality will work in the most recent previous version.

Inexpensive-ish

I say “ish” because Excel isn’t incredibly cheap, especially as you move into the more expensive versions of Office, such as Professional and Professional Plus. As of this writing, the latest version of Excel, Excel 2013, was selling for $499 for the Professional Plus edition. That said, $499 is still a cheaper license than many of the full-blown data visualization packages. But chances are, you don’t have to worry about buying Excel new at your local office store. You probably purchased a computer that came with a Microsoft Office Professional license—or, your company already has some license agreement with Microsoft. Whether it came with your equipment, it was purchased on your behalf, or you had to purchase it for yourself (or for your business), the purchase represents a small fixed cost to you and essentially no additional charge to carry over to your customer.

Quick Turnaround Time

In my experience, Excel solutions just don’t take as long as other software solutions. On the front end, you can add and modify the format with ease and without code. As well, the coding language behind the scenes, Visual Basic for Applications (VBA), provides a high-level ability to modify the spreadsheet on the fly without tons of coding.

A Good Return on Investment

This should be a no-brainer at this point. When we consider all of the advantages discussed earlier, it’s not hard to see why Excel could provide a much better return on investment than a big, expensive data visualization package. But the proof is in the pudding: managers will see the good work you do and see how much they’ve paid for it.

What Excel Isn’t Good For

There are lots of things Excel cannot do (and should not be expected to do). Excel should not be used in the following ways:

• As a full-fledged database
• For enterprise-level reporting
• For replacing a full software package
• For predicting the future

In the next sections, I’ll review cases in which Excel is not the best tool for the job.

A Full-Fledged Database

Excel is not a database. It’s a flat file that can house data that has implicit relationships like a relational database. But Excel cannot replicate the abilities that a large database provides. Excel can’t learn what relational data you have. Excel can’t inherently store a large amount of data effectively without some extra modifications from you, the developer. Excel can’t inherently keep track of changes you’ve committed (you must save your work and annotate those changes—or create a macro to capture them).

For small amounts of data, Excel can provide effective storage without the bloat associated with a database. But Excel cannot replace your company’s database.

Many people will use Excel like a database and not even realize it. When they need to make a report for a given month, they’ll copy the tab from last month and then dump in the required table or make changes to their pivot table. I won’t argue that you can do this with Excel, but it’s not a good practice, and it leads to problems (see the opening to this chapter). Specifically, it requires that you disconnect the information from the copied sheet and run through a series of rote steps to update it. But to err is human, and we miss stuff. We can replace these actions with some code and formulas, and we can reduce all of those static tabs to one.

Enterprise-Level Reporting

Up until Excel 2007, you were limited to only 65,536 rows to use. But even if your data didn’t seem to use all of these rows, a large amount of data in Excel 2007 and previous seemed to cause all sorts of problems like locking up for a minute or more, spontaneously crashing your spreadsheet, and long if unpredictable save and load times. In Excel 2010, Microsoft introduced PowerPivot, which allowed for much better memory management and flexibility concerning the amount of data Excel could handle.

We’ll talk about PowerPivot in this book, specifically the last four chapters. You’ll see it can be used to create dashboards and data visualizations very quickly. However, PowerPivot is still very new, and there’s a lot to be desired from it. As of now, I do not see it as the best option for enterprise-level reporting.

A Full Software Package

In the past, I’ve made separate Excel applications that my clients really enjoyed. They wondered if I could combine them into one integrated tool. The original integrated tool was fast and lightweight. As time went on, they asked for more features, and the scope of the project became much larger. In the end, the final product had become much heavier and much more bloated. It was a lesson for me in Excel development.

Excel can do a lot, but if you keep dumping additions and capabilities into your files that were initially designed to do one thing, you should expect the file to become more bloated. When we develop Excel applications then, we should keep our work targeted and directed. Chances are, your Excel solution isn’t objected oriented to the extent that it will allow for extensible add-ons.

Predicting the Future

I know I keep repeating myself on this point, but Excel can’t solve all of your problems. Even if we make a stellar dashboard that management loves, that demonstrably saves money and time, and that provides strong insight to decision makers, we must always be concerned that what we present on our dashboard is only as strong as it is valid.

We must always be wary. A history of success isn’t proof of a valid model. We could arrive at a correct conclusion on accident and not know it until our model finally spits out something incorrect. Excel is merely a tool to help us make good decisions, but good decision making happens when we view the problem holistically and not just on the screens in front of our eyes.

Buzzword Bingo: Dashboards, Reports, Data Visualization, and Others

Today, there is a lot of interest in the field of dashboards, reports, and data visualization. These terms are often used interchangeably but can and do mean different things based on context. Dashboards and reports especially are said to mean the same thing, but it would be inaccurate to say the two are equal. Dashboards are, in fact, reports, but not all reports are dashboards. You can use the terms report and dashboard interchangeably in the proper context, but please do so care.

This book will not use the terms dashboard and report interchangeably. When I say dashboard, I mean a very specific type of report that usually does not include a lot of interactivity, and I define it in more detail in the next section. When I say report, we often mean interactive screens that allow for data lookup, interactivity, and what-if analysis among others. In this book, we’ll go one-step further and refer to these reports as decision support systems.

Data visualizations are often found in reports and decision support systems, especially on dashboard reports. But data visualization is simply a type of communication and nothing more. It’s an important type of communication because, when used correctly, it allows us to see and understand a lot information in a moment and in a small space. But dashboards and reports are not defined by their use of data visualization. Many data products use data visualization, but it’s not a requirement, and in many cases, the use might be superficial. Remember, a flashy chart, however cool looking, is wasted space if it’s unnecessary.

Let’s go over some definitions.

Dashboards

Dashboards are displays mainly used for monitoring what’s going on in a business or organization at a given time. Some dashboards provide to the user ways to drill down into the data, filter important items, or link to information outside of the view should the user need to dig deeper. They usually contain indicators (often called key performance indicators) and metrics that measure and capture different areas of an organization. Dashboards, however, are limited to only one page and only report information. They do not provide the means for further analytical investigations, such as “what-if” analysis.

The dashboard guru Stephen Few was frustrated that vendors and the business intelligence community often misused the term dashboard. Thus, he set out to create a working definition, which has now become the widely accepted definition. Few declares the following:

Decision Support Systems

Decision support systems provide increased analytical capability to the user to model and investigate different aspects of an organization or business. They often encompass areas of a business that go beyond monitoring its health. They may be able to generate different versions of each finding through analysis as well as save the steps required to make them.

I know these differences may seem confusing at first, but the applications serve different functions within an organization. Knowing the difference—and knowing when and how to use each—will allow you to provide better tools to your stakeholders. You can breathe easy because I’ll discuss in detail how both of these different applications are made. I think it’s important to understand the differences between the two, but from the standpoint of development, they’re actually made using the same items and techniques.

The Excel Development Trifecta

As the beginning of this chapter pointed out (and later chapters will attest to at length), simply knowing how to use Excel isn’t enough. You must know how to visualize data, you must understand best development practices, and you must learn to think critically about Excel and information. Therefore, three key elements go into good Excel development.

In this section I’ll go over what I like to call the Excel development trifecta (see Figure 1-1). If you follow these principles, you will be well on your way to making an awesome Excel application:

• Good visualization practices
• Good development practices
• Critically thinking about development, or “thinking outside the cell”

Good Visualization Practices

Good visualization is the cornerstone of a good Excel application product. If you are not presenting information in a way that is understandable to your audience, you communicate little, if anything. Bad visualization design will surely hinder your work. Therefore, this book will only endeavor into good visualization practices and design. We will leave what doesn’t work on the shelf—or in the bin—to where it belongs.

Many online analytical applications and Excel dashboards feature dials and gauges in a vein similar to what can be found in your car. While building these instruments in Excel may prove to be beneficial from a learning perspective, dials and gauges are notoriously bad at conveying information. That these designs have become popular on dashboards is an unfortunate turn of events and is the result of dashboard vendors whose interest in marketing the dashboard metaphor reaches further than their ability to comprehend the underlying research behind their products. Consider the following data and charts.

Not only does good visualization practice make your work effective, it places your work ahead of what’s already available. When your managers and clients see your work, they’ll instantly know what you’ve produced is better. A great feature of good visualization is that it takes advantage of “preattentive” cognitive processes in our brain. When dashboards and reports are designed to communicate, information is transferred seamlessly from the screen into the viewer’s mind. Bad visualization quickly makes itself apparent by taxing and overwhelming the visual field.

Good Development Practices

Excel is a flexible environment providing to the developer many different avenues to travel down to complete similar tasks. Yet this flexibility can also lead to traveling down dark alleys and slow lanes. Specifically, some development practices are better than others. Some formulas are better than others. This book will not prefer every method but instead prefer those that use less storage memory (random access memory), use fewer processor resources, and have proven to work faster than others. This book will always prefer best practices to any practice.

To demonstrate different possible avenues, consider the simple Excel conditional formula, the IF formula. If you recall, IF is given by the following:

IF(Condition_To_Evaluate, [Value_If_True], [Value_If_False])

The IF formula is pretty straightforward. The three parameters of the IF formula break down as follows:

• Condition_To_Evaluate: For this first parameter, you supply to Excel an expression that evaluates to either TRUE or FALSE. For example, if you wanted Excel to evaluate whether cell A1 was greater than 9, you would supply this parameter with A1 > 9 as an argument.
• Value_If_True: Whatever you supply to this parameter is what Excel will return should the condition in the former parameter result in TRUE.
• Value_If_False: Whatever you supply to this parameter is what Excel will return should the condition in the former parameter result in FALSE.

If you have a scenario with multiple conditions, that is, a scenario in which you wanted to evaluate another condition when the first evaluates to TRUE or FALSE, you could use nested IF statements. Consider the following example.

Say your organization sells widgets for $3 when a buyer purchases 100 or less, $2 when a buyer purchases more than 100 but less than or equal to 200, and $1 when a buyer purchases more than 200. You could use nested IF statements, as shown in Figure 1-4.

At first glance, nested IF statements may appear to be the best and only solution to this problem. But there is indeed another way to do this problem. You could, for example, use Boolean logic. Boolean logic employs a set of binary symbols, TRUE and FALSE, exactly like the Condition_To_Evaluate from the previous IF formula. The great thing about Excel is that TRUE and FALSE and the numbers 1 and 0 can represent both Boolean logic and numbers. From the nested IF statements shown earlier, we could convert that formula from this:

=IF(B3 > 200, B3 * 1, IF(B3 > 100, B3 * 2, B3 * 3))

to the following:

=B3 * ((B3 <= 200)  + (B3 <= 100)   + 1)

How does the new formula work? Let’s say the number 51 is in cell B3; then we have this:

=B3 * ((B3 <= 200)  + (B3 <= 100)   + 1)
=51 * ((51 <= 200)  + (51 <= 100)   + 1)
=51 * ((TRUE)  + (TRUE)   + 1)
=51 * (1 + 1 + 1)
= 51 * $3.00 = $153.00

Now just because we can convert an IF statement to a Boolean formula like we did earlier, should we? Well, that all depends. IF statements can be much slower than Booleans. They can take much longer to process, especially when they must branch to other nested IF statements. Boolean formulas, on the other hand, are quick and execute as a single equation. Not only are using Boolean formulas much faster than nested IF statements, but they also provide to us the ability to use conditional tests in many other formulas. When choosing how to construct our formulas, we should understand these trade-offs.

Consider another problem. Let’s say you have a label update on the screen displaying the sentence “Displaying x programs,” where x is the current number of programs in viewing. Most likely, you’ll want the screen to say something like “displaying 1 program” when there’s only one program and say “displaying 10 programs” when there is more than one.

To do this without an IF statement, you would write the following:

= "Displaying " & B1 & " program" & Left("s", B1 > 1)

Remember that the LEFT() formula takes in a given string and returns the amount of characters specified in the second argument. Earlier, we place a conditional statement into the number of characters field instead of a constant number. Now let’s play around with what can go into B1:

If B1 = 7 then,
= "Displaying " & B1 & " program" & Left("s", 7 > 1)
= "Displaying " & B1 & " program" & Left("s", TRUE)
= "Displaying " & B1 & " program" & Left("s", 1)
= "Displaying " & B1 & " program" & "s"
= "Displaying " & B1 & " programs"

=Displaying 7 Programs

Now consider if B1 = 1, then we have this:

= "Displaying " & B1 & " program" & Left("s", 1 > 1)
= "Displaying " & B1 & " program" & Left("s", FALSE)
= "Displaying " & B1 & " program" & Left("s", 0)
= "Displaying " & B1 & " program"
= "Displaying 1 program"

Good development practices go beyond what’s doable on the spreadsheet. Indeed, good development practices extend into our Visual Basic for Applications code. Writing a large matrix of information to a spreadsheet, for example, can be completed several ways. We could iterate through every line in the array like the code here:

For i = 1 To 10

    Sheet1.Range("A" & i).Value = vArray(i)

Next i

Or we could do it in one pass:

    Sheet1.Range("A1:A10").Value = vArray

The difference between these methods is substantive. When Excel iterates through every line of the array, it must execute each instruction inside the loop. That is, Excel must read the value from the array, print the array to the screen, and update the spreadsheet (since writing to the screen will trigger updates in certain functions known as volatile functions)—and it must do this n amount of times, which, in this example, is 100 times. So, that’s three actions per each iteration, or 3 * 100, so it’s 300 actions.

Now consider the second example. The entire process is handled in only one action. Rather than calling for Excel to update the screen 100 times, this code updates the screen only once—but it yields the same results as the previous code. For complex spreadsheets filled with many interactions, the second code snippet is clearly the better choice.

Thinking Outside the Cell

Above all, this book will encourage you to think differently, if not critically. You’ve been told before, I’m sure, to “think outside the box.” The phrase unfortunately has become a trite marketing gimmick of personal coaches and inspirational speakers. Yet, if we remove the hype surrounding its use, we find a tangible and important instruction. Specifically, we must become aware of the limitations we’ve constructed for ourselves and remove them when they hinder our work.

Were you surprised to find Boolean formulas could substitute for IF statements? Did you know that pie charts do not effectively convey information? These revelations merely scrape the surface of what is possible if we think about spreadsheet development differently, when we think outside the cell.

Because when we do, we no longer see Excel as just a boring spreadsheet, in other words, a perfunctory drawing board for the listless accountant, the engineer, or the basement dwelling nerd (well, maybe I’m still one of those at heart). Instead, we see a rich canvas upon which to paint complex pictures; a flexible landscape whose curvature can be molded into mountainous regions in which we can see everything; or simple terrains that stand in for the larger picture. We can achieve much in Excel if we take a step back from our cell walls and give our imagination some time to hold the reigns.

When we “think outside the cell,” we concentrate on what is and isn’t possible in Excel. We evaluate the distinction between conventional wisdom and hype, implementing and disregarding each accordingly. We blur the line between what we’ve been told is possible and what we’ve been told isn’t, but we also draw distinct lines between what we’ve previously thought to be hazy. We add nuance to the simple and clarity to the complex. We don’t just rely on the authority of books like this; we balance knowledge with other expertise and experience, synthesizing intuition and expert information into a holistic paradigm.

Consider what constructions keep you locked into a certain way of thinking. Why do you think the way you do? What certainties do you have about spreadsheet development, about business, about life?

More than developing quality spreadsheets, thinking outside the cell is a personal experiment. At this auspicious time, words like dashboards, reports, and visualization are at risk of becoming virtually meaningless, proffered by vendors that do not imbue these words with meanings. Already, businesses are becoming weary of those that sell these things. And yet, these words do have meaning. When we understand them and use them correctly, we can provide rich data to businesses to help them make decisions. But we only do this when we remove our work from the world of confusion in which it is born.

Available Resources

In this section, I’ll talk about what kinds of resources are available to help guide you through your journey.

Google

Google...Google...Google! Google is your best friend. If you’re ever stuck on a problem, simply ask Google the same way you might your friend. Usually, you’ll see the results of Excel forums with folks asking the very same questions.

www.google.com

Chandoo

This site, by Purna “Chandoo” Duggirala, is a phenomenal resource for every Excel developer, from novice to professional. Chandoo covers many topics including dashboards, VBA, data visualization, and formula techniques. His site is also host to a thriving online forum community. Chandoo also is a contributing author of this book (Part V).

www.chandoo.org

Clearly and Simply

Clearly And Simply is a site by Robert Mundigl. The site is mainly focused on dashboards and data visualization techniques with Excel and Tableau.

www.ClearlyAndSimply.com

Contextures

Debra Dalgleish runs the Contextures web site that focuses on Excel development and dashboards, particularly with PivotTables. Her approach to dashboards and the use of PivotTables is different from mine but well worth a read. She is also the author of these Apress books: Excel 2007 PivotTables Recipes, Beginning PivotTables in Excel 2007, and Excel Pivot Tables Recipe Book.

www.contextures.com

Excel Hero

Excel Hero was created by Daniel Ferry. While his blog is not very active anymore, you will find his older content incredibly useful. Several of his articles have served as the inspiration of the content found in these pages.

www.ExcelHero.com

Peltier Tech

Jon Peltier is a chartmaster. His web site is full of charting tutorials and examples. He provides sage wisdom on data visualization and proper data analysis. His web site covers every conceivable thing you might want to do with a chart in Excel.

www.peltiertech.com

The Last Word

The most important skill this book will require of you, however, is the desire to learn. Some of the material may appear challenging at first. You may even find yourself frustrated at times. In these moments it’s best to take a break for a moment, find your bearings, and start from the beginning of the section in which you left off. The material is complex but well within your grasp. I urge you to push through to the end of the book. The material is worth it, but more important, you’re worth it. What will you learn in this book will distinguish you.

___________________________

¹Rajalingham, Kamalasen, David Chadwick, Brian Knight, and Dilwyn Edwards. “Quality control in spreadsheets: a software engineering-based approach to spreadsheet development.” In System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference, pp. 10. IEEE, 2000.

²From Information Dashboard Design (Analytics Press, 2014) by Stephen Few.