In different business processes like product development, supply chain, operations, and finance – numbers and data drive critical decisions. Yet, standalone figures can sometimes be hard to decipher. This is where the power of visual aids like charts comes into play. With our Ultimate Charts (Part 5) spreadsheet template, we transform complex data into easily understandable visual stories. This template offers ten dynamic and customizable charts, tailored to distinct analytical tasks. They are grouped into four main categories: Progress, Timeline, Distribution, and Statistical charts. Each one has a specific role, be it in tracking trends, outliers, or analyzing complex data distributions.
In this article, you'll learn:
And before we dive into our template, which you can download by the way – please remember that anything in blue is data that you can edit and replace with your own datasets. Text in black represents formulas that should not be altered.
The "Actual vs. Target" chart is favored by businesses that need comparative insights between real-time achievements and predetermined goals. For example, it could track two crucial metrics: 'budget' and 'forecast'. In retail sectors, for instance, the chart measures actual sales against projected figures across various branches or regions. In manufacturing, it's often used to compare actual production outputs against forecasted quantities.
Blue bars represent the actual sales figures, while the yellow and blue lines capture budgeted and forecasted values, respectively. Businesses can promptly discern which regions or departments outperform or lag behind their targets, facilitating more informed decisions. This chart integrates into diverse business scenarios, from monitoring employee performance in HR to tracking customer satisfaction in the service industry.
The Gauge chart is a visual analytics tool designed to offer an immediate snapshot of performance against set benchmarks. With its speedometer-like appearance, this chart gives businesses an intuitive performance overview. It's particularly suited for corporate metrics representing a single value or a percentage within a defined range, such as quarterly sales achievements. It excels in situations with a clear target, effectively conveying how close a value is to a benchmark.
Sales teams, for instance, frequently utilize it to showcase monthly revenue against set targets. A pointer towards the green indicates that the team is either on track or exceeding expectations, while a nudge towards the red suggests there's room to improve. Similarly, in customer support, the Gauge chart can depict satisfaction ratings. After a round of customer feedback, a green-leaning chart is an encouraging sign of positive customer experience.
In our implementation of the Gauge chart, you can define categories and view the gauge for each one. The "Sum of Value" display is used to compare a category's performance relative to others, while the "Goal" is used to compare performance against a specific target. In our template, you can also give a custom name to each gauge section and the percentage of the gauge said section should include.
The "Trend chart" visualizes long-term patterns and fluctuations in your data. It's usually employed in sectors that need consistent monitoring of time-based metrics. Whether you want to monitor sales, customer feedback, or inventory levels, some insights can only be offered by observing how these numbers change over time. For instance, in retail, it can show sales trajectories over quarterly periods, pinpointing spikes or declines. Similarly, in operations, it can map out inventory levels over time, helping managers anticipate restocking needs or identify surpluses. In essence, wherever there's a need to comprehend how a metric evolves over time, the Trend Chart provides a straightforward visualization using a continuous line.
By tracing the line, one can spot seasons of high performance, recognize potential bottlenecks, and strategize accordingly. Meanwhile, the dotted line offers a glimpse into potential future trends based on the present data. Our template includes a date filter, for which you can view the trend line within a subset of your data. That way, you can see the trend line for a 30-day, 60-day, or 90-day window, or any custom timeframe you define.
The 'Variance chart' is used to discern short-term shifts in metrics, emphasizing month-to-month changes. Instead of painting a broader, long-term picture like the Trend Chart, the Variance chart zeroes in on monthly fluctuations, presenting the exact percentage differences between consecutive months.
This dual view holds an understanding of data nuances that could be lost in more straightforward visualizations. The Variance chart shows the percentage changes in sales for that month compared to its predecessor.
By identifying such short-term fluctuations, businesses can evaluate the immediate impact of their decisions. Each monthly bar portrays the figures, while the percentage markers show the month-over-month growth or decline.
The 'Control chart' is fundamental for monitoring the stability of a process. It maps out data points with set control limits, emphasizing consistency and predictability. By flagging anomalies that stray beyond these limits, the chart aids in ensuring that processes remain consistent, enabling organizations to manage and improve quality proactively.
It's particularly instrumental for industries where even minor deviations can have significant consequences and aim for operational excellence. In essence, it serves as an early warning system where organizations can detect and address process deviations before they escalate into more substantial issues or defects.
The central yellow line represents the average of the data points, serving as a benchmark for understanding general performance. The green and red lines, on the other hand, represent the Upper and Lower control limits. Any data point straying beyond them suggests unusual variation that may need further investigation. Our template includes a date filter to select the period you want to analyze. Additionally, all control limits are automatically calculated and updated based on the range entered.
The 'Waterfall Chart' focuses on the accumulative effect of sequential data points, detailing how an initial value is affected by subsequent positive or negative changes. This chart is essential for businesses that need a concise representation of events over a timeline, emphasizing incremental shifts toward an outcome.
Given a starting amount, each bar represents an incremental change—green for growth and red for reductions. To the right of the chart one can see the final value, the accumulation of all changes. For instance, in retail inventory management, the Waterfall Chart visualizes the sequential impact of product inflows and outflows. Starting with a month's initial count, the chart displays additions from supplier deliveries as green bars and deductions from sales or damages as red bars. By month-end, the chart offers a clear account of inventory changes, aiding precise replenishment decisions.
The "Histogram" is used to understand the distribution of variables by dividing the data into buckets, or "bins", and then plotting the number of occurrences in each bin. For example, manufacturers might use histograms to understand the lifespan of batteries, determining the most frequent duration before they need replacement.
Similarly, e-commerce businesses can analyze the distribution of customer purchase amounts, helping to define pricing strategies or unique offer thresholds. In essence, a histogram is the distribution curve of an occurrence. In our template, it's possible to use a filter to categorize the histogram view further. You can analyze different characteristics with a single click, or change the bucket size you prefer – that way you can see your distribution in more detail.
The "Calendar chart" provides a concise visual representation of task distribution across a month. By selecting a specific month, it's possible to instantly view the distribution of completed versus pending tasks. This chart highlights productivity patterns – green markings signify days in which tasks were completed. On the other hand, red markers are days when set targets weren't achieved, representing potential challenges or areas that may require intervention. Gray dots represent days on which no tasks were listed.
For example, let's say you are the operations manager of a factory and you need to ensure that a machine is cleaned each day. In the data section you can list out the days the machine needs to be cleaned, and your team can mark whether or not they cleaned the machine. By looking at the "Calendar chart" in a single glance, as a manager, you'll be able to tell which days the machine was not cleaned – as opposed to trying to analyze your data row by row.
The 'Scatter chart' helps to illustrate how one variable might affect another. A company might want to figure out if there's a link between product quality and sales. Or perhaps be curious about the link between the time dedicated to a task and its eventual outcome. This chart maps out each data point, making patterns, clusters, or even outliers immediately evident. To decipher patterns, look for trends in how the dots are grouped:
However, if dots appear dispersed without a clear pattern, it signifies no strong association between the two variables. Also, if two variables are correlated, it is hard to identify what causes that correlation. If you're unfamiliar with this chart please search online "correlation vs. causation".
The 'Box and Whiskers chart', commonly called a 'Box plot', is a graphical representation that provides a snapshot of a dataset's distribution. It's handy when you want to spot outliers, determine data symmetry, or get a sense of data spread. This template enables you to examine values within an entire dataset or to isolate specific variables to observe their unique behavior.
Here's how to read each box: the centerpiece of the chart is a rectangle, often called the 'box', which contains the middle 50% of the dataset's values. In other words, half of all the data points lie within this box. Inside it, there's a line that divides the box into two. This line represents the median and marks the exact middle of all data points. So, when you look at this line, you see the value where half of the data points are above it and the others are below. The top and bottom edges of the box are called 'hinges'. The top hinge represents the 75th percentile (Q3), and the bottom hinge represents the 25th percentile (Q1). Together, they frame the range of this central half of the data.
The span between these hinges (Q3 - Q1) is known as the Interquartile range, which, as explained, represents the range of the central 50% of the data. There are two lines extending out from the box, the 'whiskers'. These whiskers stretch to the smallest and largest data values within a calculated range. Any data point beyond these whiskers is typically considered as outlier, meaning they are unusual. If you're familiar with a histogram, imagine if each box with its whiskers represents a single distribution. Each bar is a histogram with only four buckets representing 25%, 50%, 75%, and 100%. Our box chart template also has a filter to segment data by any category in the dataset.
We hope you've enjoyed our Ultimate Charts (Part 5) template, and that these charts can ultimately help you save hours of work. If you work in the industry and noticed we missed some essential charts, please let us know so we can add it to our next version of this template.
