As Big Data takes precedence, accurate data analysis becomes decisive for businesses. Choosing the right visualization technique is crucial for intelligible data, while poorly chosen methods can hinder its usability and potential.
What is good data visualization?
Data visualization involves representing data through graphical forms such as graphs and charts, utilizing various methods and tools for optimal illustration. The primary objective is to convert data sources into visually accessible information, making it easy for viewers to comprehend the complete insights.
While familiar with conventional visualization formats like line charts and pie charts, the growing accumulation of substantial data within organizations, encompassing customer and company activity, necessitates advanced tools. These tools play a crucial role in enhancing understanding of both company operations and customer behavior, especially when traditional approaches prove ineffective in handling massive datasets
Either you choose conventional formats or advanced tools, please keep in mind the 4 fundamental characteristics for a good data visualization, as follows:
- Accurate: The visualization should accurately represent the data and its trends. If viewers have doubts about the accuracy of data, it will not be used to make decisions. In the long term, erroneous visualized data can make people more cautious about your other data sets.
- Transparent: Your visualization should be easy to understand. Various elements can harm data clarity: inappropriate fonts/size, over-decorative colors, miss-descriptive title, and messy layout.
- Empowering: The reader should know what action to take after viewing your visualization. Does your visualized data get used regularly and does it help people make decisions? These qualities are best for evaluating a data visualization’s usefulness.
- Succinct: Your message shouldn’t take long to perceive. One of the primary advantages of a data visualization technique is that it displays various data representations at the same time. Asking viewers to scroll to see different data visualizations can limit viewers from reaching major conclusions due to people’s limited working memory.
Benefits of good data visualization
By taking advantage of data visualization, organizations stand to reap many transformation stages.
Users can also bring up more advanced visualizations with the current and more evolved data visualization capabilities. For example, converting some random data into a bubble graph or a heat map is a simple way to visualize it. It is needless to say data visualization methods can materially increase the speed of decision-making processes.
Simple to grasp and unlock key values
Their most practical side is that they allow us to absorb large amounts of information in the blink of an eye. It is partly because humans can process visual images 60,000 times faster than text. As a result, viewing a graph, chart, or other visual representation of data is more comfortable for the brain to process than comprehending text.
Data visualization techniques ensure that key values can be extracted from large amounts of data which can be overwhelming and challenging to process. Data visualization aids this by making the data’s key values clear and easily visible. Eventually, relevant information would be highlighted, and the noise in the data would be removed. This makes it simple to comprehend and interpret.
Determine Patterns
Exploring data patterns allows users to focus on specific areas that potentially help accelerate their organization.
Data visualization enables us to identify emerging trends and previously hidden patterns to respond quickly based on what we see. These other emergent properties can lead to valuable insights and correlated parameters that would not have been discovered otherwise. Exploring these patterns allows users to focus on certain areas of data that require attention to identify the importance of these areas in moving their organization ahead.
Friendly-viewer Data
Unlike traditional data visualization methods (such as spreadsheets and PowerPoint), the user can delve deeper and transform data into various visualizations. These are not only easier to use, but also allow users to quickly manipulate data into desired configurations.
Whether for employees or managers, a visual representation of data in reporting and presentations is always effective. Not every detail should be shown or listed, and not every listener has the time or knowledge to comprehend them. They frequently notice only the most significant and vital issues.
17 Data visualization techniques (Examples included)
The data visualization technique you use will depend on the data you’re working with, the audience, and your data types. Here are some data visualization techniques to be aware of:
1. Pie Chart
A Pie Chart is a type of graph that shows data in the form of a circular diagram. Each pie slice is proportional to that category’s size in the overall group. The entire “pie” represents one hundred percent of the whole, whereas the pie “slices” represents portions of the whole. They do not show changes over time.
What is the intended use?
Pie charts aid in comprehending the parts-to-whole relationship.
We use pie charts to represent data as a fraction of the whole visually. It is used to compare data and determine why one is smaller/greater than the other. A pie chart is preferable when dealing with a small number of buckets and discrete data sets. They can also be used in the following ways:
- To compare areas of growth such as profit and loss in business.
- To show the time allotted to each section, percentages of students’ grades in school
- To represent the marketing and sales data for the comparison of brands.
This graph is often best used as a basic two-dimensional. Using a three-dimensional pie chart often adds confusion and is not recommended.
When to use it?
Assume we want to concentrate on how much revenue a specific product or set of products contributes to the overall revenue. Given our natural construct of how we read a clock, it’s fairly obvious that Product A accounts for approximately a quarter of total revenue and that Products A, B, and C combined account for roughly two-thirds of total revenue. A pie chart could effectively communicate these general part-to-whole relationships. The following two pie charts illustrate the second use case. I can easily draw one broad conclusion from each of these: one slice of the pie is relatively larger or smaller than the others.
Practical examples
Let’s look at a pie chart of a company that wants to determine the proportion of employees in each job category.
Pie charts typically include the following components:
- All observations are represented by a circle (“pie”).
- For each category, draw a circle segment (“pie slice”).
- Slices can optionally have labels indicating their relative (percentage) or absolute size (count or summary statistic).
Based on the pie chart representation, we can interpret that Manufacturing is the company’s largest category, followed by R&D. Janitorial is the smallest group.
2. Bar Chart
A bar chart is a graph made up of rectangular bars. It has rectangular bars or columns, referred to as bins, representing the total number of observations in the data for that category. Vertical columns, horizontal bars, comparative bars, or stacked bars can display bar charts. The bars can contain multiple types of information.
The vertical axis of the bar graph refers to the y-axis, while the horizontal one refers to the x-axis. Typically, the chart compares different categories. Although bar graphs can be plotted vertically (bars standing up) or horizontally (bars lying flat from left to right), vertical bar graphs are the most common. The length of the bars/columns determines the value described on the y-axis when interpreting a bar graph.
What is the intended use?
A bar graph’s purpose is to quickly convey relational information by displaying the quantity for a specific category. Bar graphs are used to compare two data groups or track changes over time. They work best when significant changes occur.
When to use it?
Assume you just conducted a poll of your friends to determine which types of movies they preferred. Then to present it visually, we have the bar chart. It’s a great way to show relative sizes: we can see at a glance which types of movies are most popular and which are least popular.
Not just that, we can use bar graphs to show the relative sizes of many things, such as the type of car people drive, the number of customers a shop has on various days, and so on.
Practical example
A grocery retail business surveyed the customer’s attention to specific fruits. They presented the results in a table and bar chart. As you can see in the chart, they intentionally used the color that corresponds to the name of the fruit so that we can absorb the information better. Blueberries have reached the highest point, which means most people desire fruit to buy them.
3. Histogram
A histogram is a graphical representation of data points organized into ranges that the user specifies. The histogram, which resembles a bar graph in appearance, condenses a data series into an easily interpreted visual by grouping many data points into logical ranges or bins. For each column, the vertical y-axis represents the number count or percentage of occurrences in the data.
Columns can be used to display data distribution patterns.
What is the intended use?
Histograms are frequently used in statistics to show how many particular types of variations occur within a given range. They can be used to compare the distribution of numerical data in different interval ranges and assist audience to quickly and easily understanding essential patterns associated with a large amount of data.
When to use it?
Under certain conditions, the histogram graph is used. They are as follows:
- The information should be numerical.
- When you need to examine the shape of the data distribution
- Or you want to assess the changes in a process over different periods
- Or you might tackle the situation when dealing with two or more processes, it helps determine variations in the output.
- Or you need to determine whether a given process meets the needs of the customer.
Practical example
The following table gives the lifetime of 400 neon lamps and the histogram solution for the table of data below. You will see how the lamps’ life expectancy lasts and their corresponding unit.
| Lifetime (in hours) | Number of lamps |
|---|---|
| 300 – 400 | 14 |
| 400 – 500 | 56 |
| 500 – 600 | 60 |
| 600 – 700 | 86 |
| 700 – 800 | 74 |
| 800 – 900 | 62 |
| 900 – 1000 | 48 |
4. Gantt Chart
A Gantt chart is a popular and useful way of displaying activities (tasks or events) against time. A list of activities appears on the left side of the chart, and a time scale appears along the top. Each activity is represented by a bar, with the position and length of the bar reflecting the activity’s start, duration, and end date.
What is the intended use?
A gantt chart shows you what needs to be done (the activities) in a project and its deadline. allows you to see:
- What exactly are the various activities?
- When each activity starts and finishes
- The duration of each activity
- Where and how much do activities overlap?
- The project’s start and finish dates
When to use it?
Best for project managers when they want to create a project schedule by breaking projects down into manageable chunks of work and assigning start and end dates. It’s also useful for identifying key project milestones and task reporting.
Practical example
This Gantt chart example clearly shows:
- The project schedule’s start date
- What are the project tasks?
- Which team member is responsible for each task?
- When activities begin and end
- Each activity’s completion rate
- How tasks cluster, overlap, and connect to one another
- Finish-to-start, start-to-start, finish-to-finish, and start-to-finish dependencies are examples of task dependencies.
- Scheduled milestones and project phases
- The project’s critical path
- The project’s completion date
5. Heat Map
It is a method of graphically representing numerical data that uses colors to indicate the value of each data point. The warm-to-cool color scheme is the most commonly used in heatmap visualization, with warm colors representing high-value data points and cool colors representing low-value data points.
What is the intended use?
Heatmaps assist you in uncovering hidden behavioral insights that allow you to:
- Determine the best and most popular elements/areas and the average and least popular elements/sections.
- Find solutions by highlighting specific data points in the generated heatmap.
- Determine the ideal page length.
- Discover segment-specific behavioral patterns, identify user experience gaps, and much more.
When to use it?
The warehouse environment is one example. Businesses can use heat maps to see how many employees are clustered in a specific area or the area that slows down fulfillment.
- Heat maps may be used when ride-hailing companies or logistics companies want to see where vehicle requests are coming from to dispatch drivers and reduce wait times.
- An IT department could use heat maps to determine which servers have the most incoming and outgoing connections. These servers may consume the most resources or be the most vulnerable to external attackers.
Practical example
A stock index heatmap is used for investors in stock markets so you see current market trends at a glance. It utilizes a cold-to-hot color scheme to distinguish between bullish and bearish stock options. The former is highlighted in green, while the latter is highlighted in red. As a result, investors can easily make decisions to sell, buy or hold stocks.
6. Box and Whisker Plot
A box and whisker plot is a graphical method of displaying variation in a data set. Box and whisker plots are efficient and easy to read because they can summarize data from multiple sources and show the results in a single graph.
What is the intended use?
Box and whisker plots allow for comparing data from different categories for more accessible, more effective decision-making.
When to use it?
Use box and whisker plots when you have multiple data sets from different sources that are related in some way. Here are some examples:
- Test results differ between schools or classrooms.
- Before and after data from a process change
- Camshaft lobes and other similar features on a single part
- Data from identical machines producing the same products
Practical example
Assume you wanted to compare the performance of three lathes in charge of rough turning a motor shaft. The design tolerance is 18.85 +/- 0.1 mm. The figure shows a box and whisker plot of diameter measurements from a sample of shafts taken from each roughing lathe.
- Lathe 1 appears to be producing good parts and is within tolerance.
- Lathe 2 appears to have too much variation and produces shafts smaller than the minimum diameter.
- Lathe 3 has less variation than Lathe 2; however, it is centered on the lower side of the specification and produces shafts that are below specification.
7. Waterfall Chart
This informative chart effectively illustrates data changes over a defined period. By representing both positive and negative values, it provides a clear visualization of the cumulative impact. The fluctuations in increments and decrements create a dynamic display, showing how the initial value is affected, making it easy to grasp and analyze
What is the intended use?
It is frequently used in financial or sales departments to depict changes in revenue or profit. For instance, your monthly recurring revenue (MRR), new revenue, upsell, lost revenue, and current revenue.
When to use it?
To help you understand, let’s look at a simple example. An inventory audit of men’s t-shirts in a retail outlet is the most basic example. It would help to determine how many salable T-shirts you have on hand to begin next month. Typically, some units will be available to start the month. Some units will be damaged while the t-shirt is on display and various people try it on. Some of these damaged units could be refurbished and added to the stock, bringing us to the total number of salable units.
As a result, in this waterfall chart (also known as the bridge chart), the initial value of “Units in stock” goes through a series of ups and downs, one up and one down, to arrive at the final value of Salable Units. This is also known as Waterfall Reporting.
8. Area Chart
What is an Area Chart?
The area chart is similar to the line chart. Both charts depict a time-series relationship, demonstrate continuity across a dataset, and are useful for identifying trends rather than individual values. However, the area below the line should be filled with a specific color or texture. Area graphs are created by first plotting data points on a Cartesian coordinate grid, then connecting the points with a line, and finally filling in the space beneath the completed line.
What is the intended use?
Area Graphs, akin to Line Graphs, illustrate the progression of quantitative values over a defined timeframe. They are often employed to highlight trends rather than convey precise values. There are two main types: Grouped and Stacked Area Graphs. Grouped Area Graphs share a common zero axis, whereas Stacked Area Graphs have each data series beginning from the endpoint left by the preceding series
When to use it?
Choose area charts solely when depicting the evolution of values across time. If your aim is to illustrate variations among different categories, opt for (stacked) bar charts, column charts, or split bars instead.
9. Scatter Plot
What is a Scatter Plot?
A scatter plot (also known as a scatter chart or scatter graph) uses dots to represent the values of two numerical variables. Each dot on the horizontal and vertical axes represents a value for a single data point. Scatter plots are used to investigate the relationships between variables.
What is the intended use?
Scatter plots are commonly used to identify correlational relationships. In these cases, we want to know a good prediction for the vertical value if we were given a specific horizontal value. The variable on the horizontal axis is frequently referred to as the independent variable, while the variable on the vertical axis is referred to as the dependent variable. Variable relationships can be described in various ways: positive or negative, strong or weak, linear or nonlinear.
When to use it?
When searching for correlation in a large data set, a scatter plot is what you need. The data sets must be in pairs, with dependent and independent variables. When the data is plotted, the results show that the correlation is positive, negative (varying degrees), or nonexistent. Including a trend line will help demonstrate the correlation and how statistically significant it is.
Practical example
The scatter plot shown above depicts the diameters and heights of a sample of fictional trees. Each dot represents a single tree; the horizontal position of each point indicates the diameter (in centimeters) of that tree, and the vertical position suggests the tree’s height (in meters). The plot shows a generally strong positive correlation between a tree’s diameter and height. We can also see an outlier point, a tree with a much larger diameter than the others. This tree appears relatively short for its perimeter, which may merit further investigation.
10. Pictogram Chart
What is a Pictogram Chart?
Pictogram Chart visualization is widely used and popular. It displays the data using small icons, with each icon representing a different category. The data becomes easier to understand thanks to these icons. We arrange these icons in a grid or a linear fashion.
What is the intended use?
Because Pictograms use icons and pictorial representations of data, it is much easier to explain the data even when there are language or cultural barriers. These are strongly advised when visualizing data in reports, infographics, or presentations. It enhances the data by making it more memorable and interactive.
When to use it?
A pictograph employs visual symbols to represent statistical information, but it can be challenging to accurately interpret data using this method. Therefore, caution is advised when using pictographs to prevent unintentional or intentional misrepresentation of data.
11. Timeline
What is a Timeline?
A timeline chart is a graphical representation of a sequence of events, which can be made into a chart or a graph. Timeline charts can be made for anything that happened over time. As a business owner, you may have a timeline chart of new product development or a sales campaign.
What is the intended use?
A timeline chart aids in the conceptualization of a process or sequence of events. It can help you understand the complexities of a project or why something appears to take so long to complete.
When to use it?
The timeline chart you create must correspond to the information it covers and the purpose for which it was created. Here are some common applications for a simple timeline chart:
- Aids in the management and completion of complex tasks on time.
- Allows you to visualize a process or events that occurred over time.
- Assists in making decisions
- Simpler to allocate resources at the appropriate time.
12. Highlight Table
What is a Highlight Table?
Highlight tables, as the name implies, are text tables that have been colored to show high and low values. Highlight tables add interest to text tables while maintaining their form. They encode measure value ranges with the color attribute, from lowest to highest. These tables can show either continuous or diverging palettes of colors. They can also use a stepped color palette. A diverging color palette employs a variety of colors to emphasize the crossing of a meaningful threshold.
What is the intended use?
Colored highlight tables are used to compare categorical data in ranking to help users read them more intuitively and effectively.
When to use it?
Assume you have a financial spreadsheet that keeps track of your revenue, loss, profit, net income, gross income, costs, and sales. You can use different colors on the measured value for each labeled data point. Use color to highlight tables to indicate salient values on a text table. Viewers can quickly distinguish the higher measures from the lower ones within seconds of looking at the view.
13. Bullet Graph
What is a Bullet Graph?
Bullet Graphs are typically used to display performance data functions similarly to Bar Charts but are accompanied by additional visual elements to provide more context. Initially, Bullet Graphs were created as an alternative to dashboard gauges and meters, which displayed insufficient information, were less space-efficient, and were cluttered.
The main data value is encoded by the Feature Measure, which is the length of the main bar in the center of the chart. The Comparative Measure is the line marker that runs perpendicular to the orientation of the graph and is used as a target marker to compare against the Feature Measure value. So, once the main bar has passed the position of Comparative Measure, you’ve achieved your goal.
What is the intended use?
A bullet graph will help you display your goal, the current data set, and previous data sets all in one visualization if you have a target goal you need to meet at regular intervals. These bullet graphs can be displayed alongside multiple data sets to overview how one system works comprehensively. The bullet graph is ineffective for analyzing change-over time, part-to-whole ratios, flow, or distribution. They are best suited for comparisons and can be used to show more than just progress toward a goal.
When to use it?
A bullet graph is useful when you want to compare the performance of a primary measure to one or more other measures. Combining several bullet graphs into a dashboard provides a quick overview of multiple data sets. While bullet graphs are more efficient at encoding data than gauges, they are not as widely used or understood as other charts. Before using them, make sure your audience is trained to know what they’re seeing.
Practical example
This bullet graph compares the sales of various coffee products to the budgeted goal allotted for each product. Take note of the bars that represent the product’s goals.
This bullet diagram:
- Each product’s target is represented by a vertical bar.
- Colors (yellow and green) are used to distinguish which product met its sales targets.
- The goal bar is highlighted in gray around the product bar.
- Begin with a zero value.
14. Choropleth Map
What is a Choropleth Map?
A choropleth map (also known as a color theme) is a thematic map that colors or shades administrative areas based on the range in which the aggregated statistic of interest falls. Unlike a heat map, a choropleth map applies colors to defined geographic regions such as states, counties, postal codes, or other features.
What is the intended use?
Choropleth maps are simple to make and easy for map readers to understand. It excels at displaying the overall geographic pattern of variables. Because each color or shade has a value or range of values, the map reader can easily determine the values shown on the map. It is also helpful in comparing different choropleth maps to see how the spatial distribution of variables changes.
When to use it?
When we have data associated with enumeration units and want to show overall geographic patterns and make it relatively easy for our map readers to extract specific data rates from the map, we use classed choropleth maps.
15. Word Cloud
What is a Word Cloud?
Word clouds (text clouds or tag clouds) operate straightforwardly: the more a specific word appears in a source of textual data (such as a speech, blog post, or database), the larger and bolder it appears in the word cloud.
A word cloud is a collection, or cluster, of words depicted in various sizes. The larger and bolder the word appears, the more frequently it is mentioned within a given text and the more important it is.
What is the intended use?
These are excellent data visualization methods for extracting essential parts of textual data, from blog posts to databases. They can also assist business users in comparing and contrasting two different pieces of text to identify wording similarities.
When to use it?
Many studies your organization conducts will include at least one open-ended question that prompts respondents to provide a textual response.
For example, you could inquire about what current customers like and dislike about your new product line. You could also ask them for suggestions on how your company could improve. They may also be able to elaborate on any issues that bother them
That’s when you get your text-based insights into measurable analytics that boost your business operations.
16. Network Diagram
What is a Network Diagram?
A network diagram is a graphical representation of a computer or telecommunications network. It depicts the network’s components and how they interact, such as routers, devices, hubs, and firewalls.
What is the intended use?
Network diagrams can depict how network components interact. Thus, it can be used for a variety of purposes, including:
- Creating a home or professional network structure
- Coordination of existing network updates
- Troubleshooting and reporting network issues
- Documentation for external communication, onboarding.
- Keeping track of components
- Sending relevant information to a vendor for an RFP (request for proposal) while keeping confidential information private
- Selling a network proposal to potential investors
- Proposing high-level changes to the syslog infrastructure
When to use it?
Network diagrams can be used in two ways: according to the type of network they represent and network topology, or the arrangement of components.
Practical example
This network diagram shows a local area network (LAN). A network diagram may contain a lot of detail or just provide a broad overview, depending on its scope and purpose. A LAN diagram, for example, might show the IP addresses of individual computers, whereas a MAN (metropolitan area network) diagram might represent buildings or areas with a single node.
A network diagram can be physical or logical in character.
17. Correlation Matrices
What is Correlation Matrices?
A correlation matrix is a table that displays the coefficients of correlation between variables. The matrix illustrates the relationship between all possible pairs of values in a table. The correlation coefficient is contained in each cell of a table.
What is the intended use?
The correlation matrix is frequently used alongside other types of statistical analysis. For example, it could be useful in analyzing multiple linear regression models . A correlation matrix can be used to summarize large datasets as an input to a more advanced analysis, or as a diagnostic for advanced analyses.
When to use it?
A correlation matrix is typically “square,” with the same variables displayed in the rows and columns. This demonstrates correlations between the stated importance of various things to people. The main diagonal is a line of 1.00 running from top left to bottom right, indicating that each variable is always perfectly correlated with itself. This matrix is symmetrical, with the correlations shown above the main diagonal mirroring those shown below the main diagonal.
How to choose the right data visualization techniques for your business
There is a plethora of charts available. When presenting data, selecting the right visualization is critical. It is not as simple as it sounds because a false representation can lead to the audience receiving the wrong message or making a bad decision, or whatever you had in mind when creating the chart may not be conveyed to the audience. The foundations for selecting the suitable visualization charts for your project, strategy, or business goals will be formed by asking the right questions. The fundamental categories that distinguish these questions are as follows:
- Data Comparison
- Relationship
- Distribution
- Composition
FAQ
Let’s walk through some questions to ask when creating impactful data visualization.
At its core, online data visualization is about taking raw data and turning it into actionable insight by telling a story with it. Data-driven storytelling is powerful because it contextualizes statistics and metrics through a narrative that everyone inside and outside of the organization can understand.
Another important aspect of selecting the correct data visualization types is clearly understanding who you want to tell your story to – or, in other words, asking yourself, “Who is my audience?”
You may be aiming your data visualization efforts at a specific team within your organization or attempting to communicate a set of trends or predictive insights to a group of corporate investors. Take the time to research your audience, and you’ll be able to make a more informed decision about which data visualization chart types will make the most tangible connection with the people to whom you’ll be presenting your findings.
Every data visualization project or initiative is unique, so different data visualization chart types will suit other goals, aims, or topics.
After gaining a better understanding of your audience and the type of story you want to tell, you should decide if you wish to communicate a specific trend related to a particular data set over a predetermined period. What will be the most effective?
– Line diagrams
– Column diagrams
– Diagrams of areas
If your primary goal is to demonstrate the composition of your data, that is, to illustrate how individual segments of data make up the whole of something – selecting the suitable types of data visualizations is critical to preventing your message from becoming lost or diluted. The most effective visualizations in these situations are:
– Pie graphs
– Waterfall diagrams
– Stacked graphs
– Charts based on maps (if your information is geographical)
While most data visualizations allow you to compare two or more trends or data sets, there are some graphs or charts that will enhance the power of your message. If your primary goal is to demonstrate a direct comparison of two or more sets of data, the best option is:
– Diagrams with bubbles
– Spider graphs
– Graphs with bars
– Visualizations with columns
– Scatter graphs
Data visualization is based on making a picture out of your data rather than just leaving it static in a spreadsheet or table. Technically, any method counts, but as discussed here, some charts are far superior at telling a specific story.
You’ll be able to select a graph or chart that will provide you with an instant overview of figures or comparative trends over a specific period if you understand whether the data, you’re looking to extract value from is time-based or time-sensitive. In these cases, the following are incredibly effective due to their logical, data-centric designs, functionality, and features:
– Bar graphs
– Dynamic line charts
It’s critical to consider how you want to present your key performance indicators, as this will determine the success of your analytical activities and how clear your visualizations or data-driven stories are to your audience. Consider what information you want to gain from specific KPIs within your campaigns and how they will resonate with those with whom you’ll be sharing the information – if necessary, experiment with different formats until you find the graphs or charts perfectly fit your goals.
Conclusion
The application of data visualization techniques is crucial across various professions and in large-scale data projects. Leaders, in their pursuit of gaining insights from vast data sets for decision-making, find visualization methods to be a natural fit.
In essence, this practice aids businesses in understanding the factors influencing customer behavior, pinpointing areas for improvement or increased focus, enhancing data memorability for stakeholders, determining optimal product placement timing and locations, and predicting sales volumes.
While the benefits of data visualization techniques are plentiful, it is important to choose wisely to fully harness their potential when presenting to an audience. If you’re uncertain about the best option for your data visualization needs, consider seeking assistance from Synodus. We leverage cutting-edge technologies to help clients formulate innovative strategies and achieve impactful outcomes.
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