This text is an excerpt from a book titled Dashboards for Excel, which teaches readers how to create advanced and interactive dashboards using Microsoft Excel. The book covers Excel techniques, data visualization principles, and the avoidance of common spreadsheet pitfalls. It also explores the use of VBA (Visual Basic for Applications) for enhanced functionality and demonstrates the construction of various dashboards, including Gantt charts and decision support systems. Furthermore, the text discusses data modeling capabilities in Excel 2013 and PowerPivot for handling large datasets and creating insightful reports. Finally, it emphasizes the importance of clear data presentation and effective use of Excel’s features for efficient data analysis.

Excel Dashboard Design Study Guide

Quiz

1. What is the purpose of the LEFT() formula in the context of the example given, and how is it used in conjunction with Boolean logic to achieve a desired output?
2. According to the text, what are some potential pitfalls of using radial gauges in dashboards, and what does the author suggest as a better alternative in certain cases?
3. Describe the concept of “chartjunk” as discussed in the source material, and give an example of chartjunk that is mentioned.
4. How do the principles of proximity and similarity contribute to effective data visualization, according to the text?
5. What is the significance of “closure” in visual perception, and how is Kanizsa’s triangle an example of closure?
6. What is the primary purpose of a bullet chart, and how does it compare to a radial gauge?
7. Explain why using third-dimensional charts can be problematic.
8. What is the benefit of using the Me object in VBA code, and what objects does it help you avoid?
9. Why is it important to test properties before setting them in VBA, and how can doing so improve the efficiency of code?
10. What is sensitivity analysis in the context of decision support systems, and what does it allow a user to investigate?

Answer Key

1. The LEFT() formula, when used with a conditional statement, dynamically returns the correct number of characters from a string, in this case either “s” or an empty string. This, combined with a Boolean expression (B1 > 1), allows the program to correctly pluralize the word “program” based on the number of programs displayed.
2. Radial gauges are often not precise in their representation of information, making it difficult to estimate values without the accompanying labels. They also contain a lot of unnecessary visual elements that do not convey information. Bullet charts are a more effective way to communicate information.
3. “Chartjunk” refers to unnecessary visual elements in a chart that don’t contribute to the data’s communication and can even hinder it. Drop shadows, gradiating light sources, and bright, unhelpful colors are listed as elements of chartjunk.
4. Proximity allows viewers to visually group data points together, creating connections. Similarity, achieved through the use of similar shapes, colors, or other attributes, helps create groupings that the viewer will interpret similarly. When used together, they can help organize large amounts of data into understandable groups.
5. Closure is the brain’s tendency to perceive incomplete figures as whole and complete. Kanizsa’s triangle demonstrates this because the triangle is not truly drawn, but the brain creates the edges to form the triangle.
6. The primary purpose of a bullet chart is to present a single quantitative measure along with complementary measures and qualitative ranges, in a simple, space efficient manner. It is preferred over radial gauges for its better linear layout.
7. Third-dimensional charts often suffer from data occlusion, which is when part of the chart obscures the view of other parts, making data difficult to interpret.
8. The Me object always refers to the container object where code resides (e.g. a sheet), eliminating the need to use ActiveSheet, ActiveCell, ActiveWorkbook and Selection objects. This simplifies code and makes it less error-prone.
9. Testing properties before setting them, for instance, by only setting a cell’s background color if it is not already yellow, avoids unnecessary volatile actions that command recalculations, resulting in more efficient code.
10. Sensitivity analysis allows users to change the weights or parameters used in a model to see how it affects the results. This helps determine how sensitive the model is to these changes.

Essay Questions

1. Discuss the trade-offs between using traditional IF statements and alternative methods, such as Boolean logic and the XOR function, in Excel formula development. Provide scenarios where each approach might be most appropriate and explain how these techniques can improve or complicate formula structure.
2. Analyze the ways in which the text suggests that we should “think outside the cell” in Excel development. Provide specific examples from the text in the context of coding and worksheet design, and discuss how these concepts lead to superior spreadsheet applications.
3. Evaluate the criteria (mutual exclusivity, common interpretation, and sufficiency) for choosing metrics on a dashboard or decision support system. Using examples, argue the importance of these criteria to the effectiveness of data communication and decision-making.
4. Compare and contrast the concepts of dashboards and decision support systems in the text, referencing the ideas of descriptive and prescriptive analytics. Provide examples of when each would be most appropriate and discuss the importance of understanding the needs of the user in both cases.
5. Explore the role of visual perception, including preattentive attributes, Gestalt principles, and color in creating effective data visualizations. Critically discuss how understanding these aspects can lead to better dashboard and data display design choices.

Glossary of Key Terms

• Boolean Logic: A system of logic based on the binary values TRUE and FALSE.
• Chartjunk: Unnecessary visual elements in a chart that do not contribute to information conveyance.
• Closure: The brain’s tendency to perceive incomplete figures as complete.
• Conditional Expression: An expression that tests a condition, returning a TRUE or FALSE value.
• Dashboard: A visual display of key metrics, providing an overview of the current state of a business or process.
• Data-Ink Density: The proportion of a graphic’s “ink” that is actually used to display data.
• Decision Support System: A system that uses data to provide prescriptive recommendations by allowing users to change input weights.
• Encapsulation: The process of bundling data and methods that operate on that data within a class or object.
• Gestalt Principles: Principles of visual perception that describe how humans group similar elements.
• Intersection Operator ( ): In Excel, this operator returns one or more cells from overlapping ranges.
• Interpolation: The process of estimating values between known data points.
• Metric: A quantifiable measure used to track and assess performance.
• Mutual Exclusivity: The principle that metrics should not overlap in what they measure.
• Preattentive Attributes: Visual properties that are processed immediately by our brain before conscious attention.
• Prescriptive Analytics: A type of analytics that not only describes the past and present, but also prescribes actions that a user can take.
• Proximity: The principle that objects that are close together are perceived as being grouped together.
• Range Operator (:): In Excel, this operator combines cells between two ranges into one contiguous range.
• Sensitivity Analysis: Investigating how changes in input weights affect outputs.
• Similarity: The principle that objects that are similar are perceived as being grouped together.
• Sufficiency: The state of displaying enough metrics for the required analysis but without clutter.
• Union Operator (,): In Excel, this operator combines multiple references into one reference.
• User-Defined Function (UDF): A custom function created by a user in programming languages.
• Visual Perception: How humans interpret and understand visual information.
• Volatile Action: An operation that forces recalculation in a spreadsheet.
• XOR: A logical operator that returns true if only one of its arguments is true.

Excel Dashboards: Design, Development, and Decision Support

Okay, here’s a detailed briefing document summarizing the key themes and ideas from the provided text, with relevant quotes included:

Briefing Document: Excel Dashboard Design and Development

Introduction:

This document summarizes the main themes and concepts from the provided excerpts, which appear to be from a book on creating effective dashboards and decision support systems in Microsoft Excel. The material covers a broad range of topics from data visualization principles to advanced formula techniques and VBA coding practices. The central idea revolves around “thinking outside the cell” to create impactful and insightful tools.

Key Themes & Concepts:

1. Data Visualization Principles:

• Critique of Common Practices: The text is highly critical of common, yet ineffective visualization methods. It particularly disparages radial gauges, often found on dashboards like the old USPTO one, stating that they “do not allow for precision in visualization” and that the “extra colors and doodads amount to extra ink that services their function little. Information visualization expert Edward Tufte calls this chartjunk.” The author stresses that these elements, while visually appealing, “do not do anything to convey information.”
• Importance of Clarity and Precision: Effective data visualization prioritizes conveying information “quickly and effectively.” The text advocates for simpler visualizations, such as bullet charts, which are described as having a “linear and no-frills design” providing a “rich display of data in a small space.” The document encourages the use of proximity, similarity, and closure to organize information visually. Proximity, is shown as a way to “visually combine and separate pieces of data into groups.”
• The Cognitive Load of Visualizations: The text stresses the limits of working memory, arguing that dashboards with “many, many metrics” can be overwhelming. If viewers need to “actively retain” information rather than understand it immediately, then the visualization is considered flawed. “If you must do this in an information visualization, then consider whether the data presented in a visual manner is as illuminating as its visualization configuration suggests.”
• Avoiding “Chartjunk”: Following the work of Edward Tufte, the document emphasizes the importance of avoiding chartjunk, referring to “extra ‘ink’ – that is, extra stuff not really required.” It suggests to ask “how well does this information communicate?”

1. Excel Development Practices:

• Thinking Beyond Basic Spreadsheets: The text emphasizes that “thinking outside the cell” is crucial, urging readers to look beyond typical spreadsheet practices. It’s described as “a personal experiment”.
• Optimization and Efficiency: The document advocates for using Excel’s built-in features and formulas to their fullest extent. It advises that we should “render Unto Excel the Things That Are Excel’s and Unto VBA the Things That Require VBA”
• VBA Best Practices: When VBA is necessary, the text advocates for good coding practices:
• Explicit Variable Declaration: The author stresses using Option Explicit and descriptive variable names to avoid errors. For example, “If you have a test variable, then (please, for the love of God) call it test; don’t just call it t.”
• Avoiding Active Objects: It advises against using ActiveSheet, ActiveCell, and Selection, and recommends the Me object for referencing the current container.
• Limiting Volatile Actions: The text stresses minimizing recalculations in Excel by using single operations instead of iterative ones, because “every time you change the value of the cell, you’re committing a volatile action.” Testing properties before setting them can also limit such actions.
• Strategic Use of Formulas: The author emphasizes the importance of mastering Excel’s formula language. “The point of this chapter is to get your mind to think differently about certain problems. IF is a common convention, but the popular choice isn’t always the best.”
• Understanding Formula Operators: The range operator (:), union operator (,), and intersection operator () are thoroughly explained with examples of how they can be used to create dynamic and flexible calculations. The text demonstrates how to use “boolean logic” and conditional statements effectively, suggesting that the IF function can often be replaced.
• Importance of “Why, How, and What”: The text stresses the need to consider the purpose of metrics in dashboards and the criteria for choosing the correct ones.
• Prioritizing Functionality Over Aesthetics: While not ignoring design, the author prioritizes effective communication and usability over visual embellishments. The text warns against “eye candy meant to draw your attention to its work” that doesn’t provide “its intended use: to communicate information quickly and effectively”.

1. Dashboard and Decision Support Systems:

• Distinction Between Dashboards and Decision Tools: The document differentiates between dashboards, which are more descriptive and meant to monitor performance, and decision support systems, which provide prescriptive and predictive insights. Decision tools “allow you to change the weights the model uses. This is called sensitivity analysis.” A dashboard may show that something needs attention while a decision support tool may suggest solutions.
• Types of Dashboards: The document identifies three types of dashboards: Strategic, Operational, and Analytical. Strategic dashboards provide a high-level overview for managers while Operational dashboards give more detail for daily monitoring. Analytical dashboards are used for more advanced data analysis.
• Metrics Selection: The text stresses the importance of selecting appropriate metrics for a dashboard or decision support system. Metrics should be “mutually exclusive,” have a “common interpretation,” and be “sufficient.” Mutual exclusivity is described as avoiding metrics that overlap with what they measure and present, “Often you’re interested only in the resulting ratio but not its components.” Sufficiency considers if enough metrics are displayed without adding redundant data.
• Interactive Elements: The text discusses creating interactive elements, such as rollovers, using a combination of VBA and formulas. It also mentions using conditional formatting and data validation to enhance the user experience.

1. Advanced Techniques and Concepts

• Power Query and Power Pivot: The document introduces Power Query for handling unclean data and Power Pivot for creating DAX formulas and analyzing large datasets. It mentions that “DISTINCTCOUNT() is one of the hundreds of formulas available in powerpivot to calculate what you want.”
• Conditional Formatting: Beyond simple formatting, the text delves into using conditional formatting for creating complex visualizations based on dynamic criteria.
• Sensitivity Analysis: The book covers sensitivity analysis to see how changing certain inputs affects results in decision support models.
• User Input and Storage: The document provides an example of creating a system to collect user input and store it in a structured database within Excel itself.
• Gantt Chart Creation: The document showcases how to create an interactive Gantt chart dashboard using formulas, conditional formatting, and VBA.

Quotes of particular importance:

• “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.”
• “The bullet graph was developed to replace the meters and gauges that are often used on dashboards. Its linear and no-frills design provides a rich display of data in a small space, which is essential on a dashboard.”
• “The result of this process is what we refer to as visual perception. Although the world outside our eyes is read in as light, how we understand that world—that is, how we perceive the world—is a product of our brain’s processes.”
• “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.”
• “What if? “Metrics, Metrics, Metrics”

Conclusion:

This book emphasizes a holistic approach to creating effective dashboards and decision support systems in Excel. It combines principles of data visualization with strong development practices, formula expertise, and VBA best practices. The key message is that developing effective dashboards is more than just knowing how to use the software: it involves clear thinking, good design principles and a user focused mindset.

Mastering Excel Dashboards: Advanced Techniques and Best Practices

FAQ: Excel Dashboards, Data Visualization, and Advanced Techniques

1. What is “thinking outside the cell” in the context of Excel development, and why is it important?

“Thinking outside the cell” goes beyond simply using Excel as a basic spreadsheet program. It involves a mindset shift that encourages creative and innovative approaches to spreadsheet development. This includes: optimizing formulas for performance and data separation, implementing effective data visualization principles, considering personal biases that can influence design, and utilizing Excel in ways that go beyond its typical applications, such as through VBA programming and interactive features. By thinking outside the cell, developers can move beyond confusion and create tools that provide rich, meaningful data for decision making. It also emphasizes the need to build with intention, rather than just throwing together numbers and visuals. This means understanding why something is being built, not just how.

2. What are the key problems with poor dashboard design, and how can they be avoided?

Poor dashboard designs often suffer from several issues, including the use of distracting visual elements (often called “chartjunk”) that don’t convey meaningful information. Radial gauges, for example, often prioritize aesthetics over clarity. Dashboards should also prioritize key metrics and avoid presenting too much information that overloads the user’s working memory. Poorly designed dashboards often lack a clear hierarchy, making it difficult to identify critical data points, and can also violate mutual exclusivity, presenting data that is redundant or confusing. To avoid these pitfalls, developers should focus on presenting information concisely, using data-ink density principles, and selecting appropriate chart types (like bullet charts over radial gauges). They should also ensure metrics are mutually exclusive, easily interpreted, and sufficient, presenting enough but not too much data. Good design considers visual perception and avoids unnecessary complexity or decoration. Finally, avoid dashboard design that locks the user into a certain way of thinking by focusing on a specific feature or chart type that makes it difficult to explore the underlying data in new ways.

3. How can Excel formulas be used more effectively, and what alternatives to nested IF statements exist?

Excel formulas can be significantly enhanced by leveraging the power of reference operators (range, union, and intersection operators) to create dynamically sized ranges, allowing for greater flexibility. Nested IF statements can become complex and difficult to understand. Instead, alternatives like Boolean logic, the CHOOSE function, and the XOR function can be used to evaluate conditions and make decisions in a more streamlined way. For example, boolean logic allows one to create a statement like LEFT(“s”, B1 > 1) to generate either “s” (if true) or nothing (if false). The CHOOSE function is effective when dealing with ordinal data, while XOR is useful when only one condition out of multiple can be true. Instead of relying on nesting, it’s best to create concise formulas using the built-in functionality of Excel in combination with logical operators.

4. What are bullet graphs and why are they recommended over radial gauges?

Bullet graphs were developed as a replacement for the often poorly implemented radial gauges. They are a more effective way to visualize a single quantitative measure against complementary measures. Bullet graphs are linear, taking up a small space, and display richer data, often including a target, past performance, and qualitative ranges. They allow for a much more efficient reading of the data than radial gauges, which can obscure information and require direct labeling in order for the user to understand them. They also avoid the problem of “chartjunk,” focusing only on relevant data.

5. What are some best practices for VBA (Visual Basic for Applications) coding within Excel?

Best practices for VBA coding in Excel emphasize: using Option Explicit to force variable declaration, using descriptive variable names for better code readability, and avoiding use of active objects like ActiveSheet and instead opting for direct referencing using objects like Me. It is crucial to test and debug carefully, understanding volatile actions and minimizing iterations for better speed. Furthermore, developers should avoid doing in VBA what can be done directly in Excel; for example, calculations are often much faster in formulas than they are in VBA. In this case, VBA should only be used for those features that are unique to it, such as the development of user-defined functions. In general, VBA should not be used to reinvent functionality that is already available in Excel.

6. What is the significance of understanding visual perception in the context of dashboard design?

Understanding visual perception is essential because it helps to determine how users will interpret data. Principles like proximity (grouping data through spacing and alignment), similarity (using visual attributes to indicate groupings), and closure (allowing the brain to perceive complete forms even when they aren’t fully drawn) all impact how information is processed. Good visualization design leverages these principles to ensure that dashboards are easily understood. This includes things like making use of white space to distinguish between different data groups, as well as making use of pre-attentive attributes like color to communicate key information. Ultimately, visual design is not a matter of making things look nice but a matter of making things understandable, taking into account how the human brain processes visual information. It also requires an understanding of the fact that human perception is highly subjective and that, therefore, some people may experience visual information differently than others.

7. How can user interaction be improved on Excel dashboards, and what are the Rollover Method and custom formatting examples?

User interaction can be improved by incorporating features like the Rollover Method, which displays additional information when the user hovers their mouse over specific elements. This method can make use of user-defined functions (UDFs), which can be activated through hyperlinks. Interactive elements such as buttons and drop-down menus can be built to allow for more dynamic filtering of information. Custom formatting allows for the presentation of data in ways that go beyond basic Excel defaults, for example, formatting cells such that numeric values are replaced with a zero-length string and custom colors are applied to cells based on a 2-color scale, and these can significantly improve visual appearance and improve usability.

8. How can Excel be used to create decision support systems, and what are key criteria for choosing effective metrics?

Excel can be used to create decision support systems that provide prescriptive recommendations based on data. This differs from dashboards which are mostly descriptive in nature. Decision support systems include features such as sensitivity analysis, which allows users to see how changing variables impacts results. Key criteria for choosing effective metrics include ensuring that they are mutually exclusive (don’t overlap in what they measure), share a common interpretation (are easily understood by users), and are sufficient (provide enough data to make informed decisions). Metrics should be selected to support a clear purpose, presenting information in a way that provides clarity and insight, rather than just filling space. They should also be chosen with the understanding that their function is to reduce uncertainty, and the way they are presented should reflect this.

Excel Dashboards: Design, Development, and Best Practices

Excel is a powerful platform for creating dashboards and decision support systems [1]. Dashboards in Excel can be informative, actionable, and interactive [2]. The book Dashboards for Excel is a guide to creating these systems [2].

Here are some key concepts about Excel dashboards from the sources:

• Purpose: Dashboards are primarily used for monitoring what’s happening in a business or organization at a given time [3]. They often contain key performance indicators and metrics [3]. Decision support systems provide increased analytical capability to the user for modeling and investigating different aspects of an organization [4].
• Types of Dashboards:
• Strategic dashboards provide high-level information to managers and decision-makers about the health of the business or organization [5].
• Operational dashboards provide insights into specific company operations, often requiring timely responses [6]. They often have drill-down capabilities [6].
• Analytical dashboards allow for comparisons of multiple factors and trends, providing the greatest amount of detail [7].
• Decision support systems go beyond monitoring and help support organizational-level decision making and may use models [8].
• Excel’s Strengths:
• Excel is a flexible and customizable tool for data presentation and visualization [9].
• It doesn’t require any special data architecture or “business intelligence” to start building dashboards [9].
• Excel allows for modifying the user experience from looking at a spreadsheet to viewing a dashboard [9].
• It is relatively inexpensive compared to full-blown data visualization packages [10].
• Excel’s Limitations:
• Excel is not a database and cannot replicate the abilities of a large database [11].
• It can’t inherently store large amounts of data effectively without modifications from the user [11].
• Excel is a tool to help make good decisions but cannot solve all problems or predict the future [12].
• Adding too many features can cause the file to become bloated [13, 14].
• Good Dashboard Design Principles:
• Good visualization practices are essential for communicating information effectively [15, 16].
• Simplified layout is important with all information presented in one view, without scrolling, and using only one tab to present information [17-19].
• Information-Transformation-Presentation (ITP) separates the back-end data from the calculations and presentation [19].
• Avoid excessive formatting and embellishment [20].
• Avoid using too many tabs [21].
• Thinking Outside the Cell:
• This involves thinking differently about Excel, going beyond its conventional use, and combining formulas and VBA to get the best results [16, 22-25]. It means understanding what is and isn’t possible in Excel, and evaluating conventional wisdom and hype [26].
• The Excel Development Trifecta:
• Good visualization practices
• Good development practices
• Critically thinking about development, or “thinking outside the cell” [15, 27].
• Common Mistakes to Avoid:Overusing pivot tables can result in volatile actions that slow down dashboards [28, 29].
• Using dials and gauges, which are not effective at conveying information [30, 31].
• Separating information across multiple tabs [21, 32].
• Adding too many instructions and documentation directly into the spreadsheet [33].
• Not presenting all important information in one view without requiring scrolling [18, 34, 35].
• Data Visualization:
• Good visualization helps explore data, communicate effectively, and foster good decisions [36].
• It builds from the science of how perception works [37].
• Preattentive attributes such as color, size, shape, and position can help highlight important information [38, 39].
• Tables are good for precise values, but charts are better for showing patterns and trends [40].
• Line and bar charts are useful for showing changes over time [40].
• Scatter charts can be used for cause-and-effect analysis, while radar charts are not recommended [24, 41].
• Bullet charts are useful for showing multiple comparative measurements [42-44].
• Interactive Elements:
• Form controls such as checkboxes, combo boxes, list boxes, option buttons, scroll bars, and sliders can be used for user interaction [45, 46].
• The “Rollover Method” is a technique that allows for creating pop-up bubbles on mouse hover, providing details on demand [47-49].
• Slicers provide interactivity and are compatible with web/tablet versions of Excel [50-52].

In conclusion, creating effective Excel dashboards requires a combination of good design practices, an understanding of data visualization principles, and the ability to think creatively about Excel’s capabilities.

Principles of Effective Data Visualization

Data visualization is an important type of communication that, when used correctly, allows for the understanding of a lot of information quickly and in a small space [1]. Good data visualization can help explore data, communicate it effectively, and foster good decisions [2]. It is a key part of dashboard and report design, but it is not required, and its use might be superficial [1].

Here are some key concepts and principles of data visualization discussed in the sources:

• Purpose:Data visualization is a type of communication that allows us to understand a lot of information in a moment and in a small space [1].
• It helps explore data, communicate it properly, and encourage well-informed decisions [2].
• It should enhance understanding, not hinder it [3].
• Principles of Good Visualization:Good visualization builds from the science of how perception works [4].
• It should take advantage of “preattentive” cognitive processes in our brain so that information is transferred seamlessly from the screen into the viewer’s mind [5].
• It is important to understand the principles of visual perception, such as similarity, proximity, closure, common grouping, and continuation [6, 7].
• Preattentive attributes of perception like color (hue and intensity), spatial attributes (position and grouping), and form attributes (length, orientation, size, curvature, shape, and width) can be used to highlight important information [8, 9].
• Data should be presented in a way that is a natural extension of the underlying thing being modeled [10].
• Visual Perception:Visual perception involves light, objects in our visual field, and us [4].
• Our minds tend to group similar elements [6].
• Proximity influences how we perceive groups, and how we scale data affects our perception of it [11, 12].
• Closure allows us to perceive shapes as complete forms when enough information is present [13].
• Common grouping refers to our ability to see connected objects as a single, uniform shape [14].
• Continuation is our ability to perceive a continuous line even when it is broken up [7].
• Good visualization takes advantage of these principles to inform instead of mislead [7].
• Data Presentation:Tables are the most basic type of data representation [15].
• Line and bar charts are useful for showing changes over time, with bar charts often being better for scanning data [16, 17].
• When there is no connection between data points, it’s best to use a column chart rather than a line chart [17].
• Scatter charts are useful for visualizing relationships between variables [18].
• Small multiples use the same chart design across different variables, allowing for multiple dimensions to be displayed without resorting to visualizing in three dimensions [19].
• Bullet charts are useful for comparing a performance measure against a target [20].
• Charts to Avoid:Pie charts are not the best way to compare proportions because it is difficult to judge precision among areas [21, 22].
• Cylinders, cones, and pyramid charts are harder to read than standard bar charts [23].
• Charts in the third dimension often suffer from data occlusion [24].
• Surface charts are not easily interpreted [25].
• Stacked columns and area charts suffer from inconsistent baselines [26].
• Radar charts offer little advantage [27].
• Data-Ink Density:
• Refers to the amount of ink used to display data.
• Good charts should maximize data-ink density, by only including necessary elements.
• Extra colors and unnecessary additions are considered “chartjunk” [28].
• The goal should be to present information clearly and simply without overwhelming the viewer with unnecessary details [29].
• Context:Data should be presented with sufficient context to tell a story, including descriptive elements (who, what, where, when) and also, when possible, why and how [30, 31].
• Metrics should be presented with context—a signal, a performance indicator, a goal, or a target [32].
• Common Pitfalls:Using visualizations that look cool but don’t work, such as those with flashy and sparkly metallic finishes [33, 34].
• Taking visualization metaphors too far [35].
• Overusing radial gauges, which communicate information poorly [28, 36].
• Presenting too many metrics, which can overwhelm the viewer’s working memory [37, 38].
• Creating charts that stress art over communication [39].
• Using a chart simply because it is available, and not because it communicates a meaning [40].

In conclusion, effective data visualization is about more than just creating pretty charts; it’s about using the principles of perception to clearly and accurately convey information and insights [2]. It is important to choose the right type of chart, avoid unnecessary embellishments, and provide sufficient context to tell a story with the data [41].

Mastering Excel Formulas

Formulas are essential for advanced Excel development because they form the infrastructure upon which much of the work is based [1]. They provide a means to manipulate elements within the spreadsheet [2]. In addition to returning results, they form the basis of interactive dashboards and decision support systems [1].

Here are some key concepts about formulas in Excel from the sources:

• Formula Components: Excel formulas are made up of four main types [3]:
• Functions: These are built-in operations like AVERAGE(), SUM(), and IF() [3].
• Constants and literals: These are values like numbers, strings, and Booleans such as 2, “Hello world”, and FALSE [3].
• References: These refer to cells or ranges of cells, such as A1 or A$1$:A$20$ [3].
• Operators: These perform operations on values, such as +, -, /, >, and : [3].
• Formula Help: Excel includes tools to help understand formulas [4]:
• F2: Pressing F2 on a cell with a formula will highlight the portions of the spreadsheet upon which the formula depends [4].
• Evaluate Formula: This feature allows you to step through an entire formula, evaluating each part [5].
• F9: This key can be used for on-demand and piecewise calculation [6].
• Operators:
• Arithmetic operators: These include +, -, *, and / for mathematical operations [7].
• Text operator: The ampersand (&) is used to concatenate strings, acting like the CONCATENATE function [7].
• Reference operators: These include [7]:
• The range operator (:) returns a contiguous range of cells, and is useful for specifying the cells to be included in a function [8]. It can also be used to create dynamic ranges by combining it with other functions such as INDEX, COUNTA, and OFFSET [8, 9].
• The union operator (, ) combines multiple ranges into one, for use in a function [10].
• The intersection operator ( ) (one space) returns cells that overlap between ranges [11].
• Conditional Expressions:
• Conditional expressions are used to test conditions using logical operators such as =, <, and > [12].
• The IF statement is a common conditional expression, but there are other ways of testing conditions.
• Boolean values (TRUE and FALSE) can also be used in conditional expressions, and can substitute for IF statements [13, 14].
• Boolean Logic:
• Boolean formulas can be used for filtering.
• The AND function tests if all supplied conditions are TRUE.
• The OR function tests if at least one supplied condition is TRUE.
• The XOR (exclusive OR) function returns TRUE if only one condition is TRUE [15].
• CHOOSE Function:
• The CHOOSE function can be used as an alternative to nested IF statements [16].
• It evaluates one condition and goes to the specified index.
• Dynamic Ranges:
• The range operator (:) can be combined with functions like INDEX, COUNTA, and OFFSET to create dynamically sized ranges [8].
• These dynamic ranges can be assigned to a named range and used in charts, dropdowns, and formulas [9].
• Array Formulas: These formulas return results across multiple cells [17, 18]. To enter an array formula, you select the cells that will contain the results, type the formula, and press Ctrl+Shift+Enter [17, 19].
• Formula-Based Sorting:
• Formulas like LARGE and SMALL can be used to create sorted lists based on criteria [20, 21].
• Lookup Formulas:
• INDEX and MATCH can be used to create formula-based sorted lists [21].
• VLOOKUP can be used to pull back data from a table.
• Aggregation Formulas:
• SUMPRODUCT can perform aggregation by using Boolean logic (where + represents OR, and * represents AND) [22, 23].
• SUMIFS and COUNTIFS can be used to test for the intersection of data but do not support OR conditions on their own [22].

In summary, Excel formulas are more than just a way to perform calculations; they are a way to build dynamic, interactive models and tools. Understanding the different formula types, operators, and functions is key to harnessing the power of Excel for dashboard development.

Excel Dashboard Best Practices

Good practices are essential for creating effective and efficient Excel dashboards and decision support systems. These practices encompass various aspects of development and design, aiming to optimize speed, memory usage, and user experience.

Here are some key good practices from the sources:

• Excel Development Trifecta:
• Good Excel development requires a combination of good visualization practices, good development practices, and the ability to critically evaluate and apply knowledge, known as “thinking outside the cell” [1, 2].
• Good Visualization Practices:
• Present information in a way that is understandable to the audience [3].
• Use preattentive cognitive processes to communicate information seamlessly [4].
• Take advantage of visual patterns to aid understanding [5].
• Choose the correct chart types for the data being presented [6-8].
• Good Development Practices:
• Use methods that use less storage memory and fewer processor resources [9].
• Employ formulas that are optimized for speed and efficiency [9].
• Be aware of volatile functions and actions, and limit their use when possible [10, 11].
• Use the INDEX function instead of VLOOKUP when appropriate, as it can be faster [12].
• When writing to a worksheet, use a single pass instead of iterating through each line in the array [13].
• Test properties before setting them to avoid unnecessary volatile actions [14].
• “Thinking Outside the Cell”:
• Critically evaluate what is possible and not possible in Excel [15].
• Evaluate the distinction between conventional wisdom and hype, implementing and disregarding each accordingly [15].
• Balance knowledge with other expertise and experience [15].
• Consider what constructions keep you locked into a certain way of thinking [16].
• Tap into your creative resources and think differently about Excel, your work, and your projects [17].
• Coding Practices:
• Make loud comments in code using bold colors to enhance readability [18, 19].
• Pick a readable font to make code easier to follow [18, 19].
• Always use Option Explicit to force the declaration of variables [20, 21].
• Use a naming convention that is descriptive and understandable [21-24].
• Use CamelCase notation instead of Hungarian notation [22].
• Avoid using underscores in variable names [23].
• Name variables according to their purpose, making them easy to understand later [23].
• Store procedures in a sheet object, not just modules, to better organize the code [25, 26].
• Use descriptive sheet tab names to make code more readable [25].
• Use similar procedure names in different sheet objects to stay organized [27].
• File and Worksheet Naming:
• Use descriptive file names that are understandable to others, not just yourself [24, 28].
• Use descriptive worksheet tab names to organize and understand your work [29, 30].
• File names should be two or three succinct words and contain few numbers [31].
• Capitalize each word as you would a document title [31].
• Abbreviate only proper nouns [31].
• Dashboard Design:
• Use a simplified layout that makes efficient use of screen space [32].
• Show relevant data together and do not separate it across multiple tabs [32].
• Employ the information-transformation-presentation (ITP) construct to organize work [33].
• Separate concerns by separating raw data, calculations, and presentation [34].
• Do not use too many worksheet tabs; all the relevant data should be on one screen, if possible [35, 36].
• Remove gridlines for a cleaner and more elegant presentation [35].
• Use a content region with a small buffer from the end of the viewing area [37].
• Avoid using needless protection on spreadsheets [38].
• Do not include stated assumptions and purpose on each sheet in the workbook [39].
• Let the data speak for itself and avoid confirmation bias [40].
• Formula Usage:
• Use formulas that are a natural fit for the problem being modeled [41].
• When possible, use Boolean formulas to test, filter, and highlight results [42].
• Data Context:
• Present data with sufficient context, including who, where, and when [43-45].
• Provide descriptive analytics to answer who, where, and when, and when possible, how and why [43, 46].
• Provide prescriptive analytics to help users determine what actions to take [46].
• Metrics:
• Choose metrics that are mutually exclusive, have a common interpretation, and provide sufficient information [47-49].
• Rollover Method:
• Use the Rollover Method to provide details on demand through interactive pop-ups [50, 51].
• Use UDFs (user-defined functions) to write to the spreadsheet, and other Excel features to go from there [52].
• Use the Selection pane to manage pop-up visibility [52, 53].
• Data Storage:
• Use a database table to store user input [54, 55].
• Use an input entry table to capture current inputs from within a wizard [54].
• Power Query:
• Use Power Query to clean and transform data by removing inconsistent spaces, periods, and typos [56-58].
• Use Power Query to consolidate data from multiple sources [59].
• Use Power Query to remove duplicate data [60].

By adhering to these good practices, you can create Excel dashboards and decision support systems that are not only visually appealing but also highly functional, efficient, and easy to maintain. These practices can help you become a better developer and transcend the unfounded reputation of Excel [61].

Mastering PowerPivot: Data Analysis in Excel

PowerPivot is a Microsoft Excel add-in that is part of the Power BI family of tools, designed to help data analysts, managers, and others answer complex questions about their data [1, 2]. It can transform Excel into a powerful business intelligence application [2].

Here are some key aspects of PowerPivot, according to the sources:

• What is PowerPivot? PowerPivot is an Excel add-in that allows users to import data from various sources, create data models, establish relationships between data, and create measures using Data Analysis Expressions (DAX) [2, 3]. It helps overcome many of the limitations of Excel and allows users to analyze data with ease [2].
• Compatibility and Availability: PowerPivot is compatible with Excel 2010 and newer for Windows [4]. It can be downloaded from the Microsoft website for Excel 2010, and can be activated in the COM Add-ins option for Excel 2013 and 2016 [4]. If PowerPivot is not available, a user may need to upgrade to the Professional Plus package or add Power BI to an Office 365 subscription [4].
• The PowerPivot Data Model:The PowerPivot data model consists of data tables, their connection settings, the relationships between the tables, and the measures and calculations built on top of the tables [5].
• When using PowerPivot, the relationship-building process occurs within PowerPivot, unlike the data model introduced in Excel 2013, where relationships are created in Excel [6, 6].

1. Steps to Use PowerPivot:Feed raw data to PowerPivot from various sources, such as text files, Excel workbooks, databases, Azure data stores, Power Query connections, or workbook data models [3].
2. Set up the data model by connecting the tables to each other [3].
3. Create measures using DAX formulas to define how calculations should be performed [3].
4. Create a regular pivot table and use the measures as value fields [3].

• DAX Formulas:DAX formulas allow for the calculation of a wide range of numbers and data summaries [7]. These formulas can perform calculations that are difficult or impossible with regular Excel pivot tables [7].
• DAX formulas are a mix of Excel formulas and pivot tables [8].
• Examples of what DAX can do include:
• Calculating unique customer counts [7].
• Determining maximum or minimum values within a data set [7].
• Comparing values from different time periods [7].
• Calculating growth rates or moving averages [8].
• Identifying the top products or stores [8].
• DISTINCTCOUNT is a DAX formula that counts how many unique values are in a table column [9].
• PowerPivot uses filter contexts to determine how a measure should be calculated for each cell in a pivot table [10, 11]. The filter context can include row labels, column labels, slicers, and report filters [11, 12].
• Measures are reusable and can be used in constructing other measures [13].
• PowerPivot and Excel Dashboards:
• PowerPivot is useful for analyzing large amounts of data in dashboards because it serves as a powerful processing engine [14].
• PowerPivot can connect datasets, which eliminates the need for long VLOOKUP or INDEX/MATCH formulas [15].
• It overcomes Excel’s processing limitations by handling large datasets of up to a few million data points [15].
• It allows users to answer complex questions with measures rather than lengthy formulas [16].
• PowerPivot data can be combined with other Excel features like conditional formatting, charts, form controls, and VBA [17].
• Limitations of Formula-Driven or VBA-Driven Dashboards:Formulas and VBA can be slow when dealing with large datasets [16].
• Formulas can be difficult to write and maintain when they are complex [18].
• They can be limited in the types of calculations they can perform [18].
• They can be cumbersome when used to connect disparate data [18].

In summary, PowerPivot enhances Excel’s capabilities by allowing users to handle large datasets, build complex data models, perform advanced calculations, and create dynamic, interactive dashboards [15-17]. The combination of data modeling, DAX formulas, and the ability to integrate with other Excel features makes PowerPivot a valuable tool for business intelligence and data analysis [17].

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

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