Author: augmented_admin

Can data visualization services transform your business? Or is it just another tool that promises more than it delivers?

Can charts and graphs really help double your traffic, capture the right audience and increase your ROI? 

If these questions are keeping you awake, you are in the right place. Every tool promises different results, but the risk of disappointment is real. 

Rest assured, we’ll help you make the right call. Dig in to see if business data visualization is really worth the investment or if there is something better left on the shelf. 

What are Data Visualization Services?

Data visualization or data visualization services refers to converting and presenting your raw data into clear and actionable insights with easy-to-understand visuals. These visuals are presented using different visualizations, including graphs, charts, bars, and more.

Think of it as converting raw, complex data and numbers into meaningful information that your team and stockholders can easily and instantly grasp and make sense of.

Imagine presenting a quarterly sales report to a stakeholder. Instead of presenting a long list of numbers or a spreadsheet with messed-up rows, you use a graph to highlight the KPIs (Key performance indicators).

The result?

• The stakeholders are no longer lost in scrambled numbers. 
• The visualizations are quick and instant. 
• They can interpret what’s working and what needs improvement. 
• They can make better decisions and identify future trends. 

Business data visualization is a powerful tool to make your numbers more digestible. It helps you get your point across quickly and efficiently. Whether it’s your team, stakeholders, or your clients, data visualization helps them understand the story behind your numbers. 

However, the big question still remains: Will Data visualization really help make smarter business decisions and create more effective strategies?

Keep reading to see how data visualization for decision-making can change the way your business interprets and leverages its data. 

Why Should You Consider Business Data Visualization?

Do you know that images predominate the text? According to the International Forum of Visual Practitioners, the human brain loves pictures as it is naturally wired to absorb, process and communicate information through visuals.

In fact, 90% of the information transmitted to our brain is visual. Images are also processed 60,000 times faster than text and quicker than you can blink. For the same reasons, you should pay attention to the benefits of data visualization.

Think about it! When you look at a pie chart, doesn’t it feel like you are grasping the data way better than a mountain of numbers on a spreadsheet?

Whether you are tracking customer behavior, analyzing your sales report or predicting market trends, you are swimming in numbers. All of this data needs a translator to uncover the “Why” and “How” behind those numbers.

To know what exactly data visualization for decision-making brings to your table, let’s dig into its benefits.  

Top 5 Benefits of Data Visualization

Data without visualization is like trying to solve a jigsaw puzzle without the picture in its box. You have the pieces (data), but it’s hard to see what they create or how they fit together.

Data visualization gives you a big-picture view. You connect the dots, see patterns and understand the story of your data. What are the other benefits of data visualization for decision-making? Let’s have a look.

1. Simplified Data Implementation

Data visualization makes your data easier to understand. It simplifies your messy and complicated data into patterns, trends and anomalies.

Data that would take hours to decipher with a spreadsheet or a table can be processed and understood faster with a well-designed graph or chart.

How can it benefit you?

• Integrate all your data from different sources into a single cohesive visual. 
• Get real-time updates and reduce the need for manual data handling. 
• Reduces the risk of misinterpretation or human error. 
• Easy to navigate and faster to understand for new members. 

From easily tracking growth to identifying the drops, data visualization helps you lead with clarity and offers efficient decision-making power.

2. Better Communications

You don’t want your end users, such as your team, clients, and investors, to be bombarded with dry numbers, right?

Data visualization is a great tool for communications. It acts like a universal language and makes it easier to share information with your end users regardless of their level of expertise or familiarity with the data.

The visuals add a cherry on top and ensure that even complex information is accessible and understandable for all.

How can it benefit you?

• Presents data in consistent visuals, ensuring everyone is on the same page.
• Share live interactive visuals with teams working remotely or in different time zones.
• You can improve your stakeholder engagement by presenting data-driven insights.
• Showcase a clear, engaging and easy-to-understand story.
• Foster collaborative discussions using interactive dashboards.
• Data becomes accessible to everyone regardless of their technical skills.

With data visualization, you can make your team or a boardroom full of executives grasp the key takeaways and collaborate with a well-designed visualization. Furthermore, you can make real-time updates with cloud-based tools, like Google Data Studio or Power BI. 

3. Increased Customer Engagement

Wouldn’t it be easy to understand and respond to each customer personally? Of course, who wouldn’t want that? However, with so much data spread across different touchpoints, it’s hard to identify what they prefer or need. 

Data visualization drills down into your data to explore customer trends and predict their future preferences. Interactive dashboards allow customers to filter based on different parameters and offer a personalized experience. 

How can it benefit you?

• Use customized and personalized visual dashboards to show real insights. 
• Offer clear reporting by breaking down numbers into graphs and maps. 
• Make them feel more controlled by giving them direct access to their business performance. 
• Strengthen their trust by helping them track and measure their progress.

Using interactive visualizations, you can understand your users’ needs and preferences and make adjustments in real time. When your users can engage with the data, they feel more prioritized, which, in turn, increases your customer engagement. 

4. Enhanced Data Analytics

Imagine reading a massive book in one sitting. Now imagine reading it by breaking it into small chapters or sections. What’s more manageable, and what’s more overwhelming? The latter is manageable and enhances your power to understand.

The same goes for data. When complex data is divided into smaller chunks, it is easier to analyze. Data visualization does the same with graphs and charts. You can thus focus on one piece of data, see trends and make decisions without getting lost.

How can it benefit you? 

• Identify trends, patterns and shifts over time quickly
• Break down large data sets into smaller segments for better and more targeted analyses
• Spot outliners or anomalies that you might have missed in your raw data
• Improve accuracy for better insights
• Simplify complex and multi-dimensional data
• Predict and forecast the future outcomes

With data visualizations, you can make faster decisions, make better strategic plans and have a deeper understanding of your customers. 

5. Improve Decision-Making

Scrolling through pages of survey responses or acting on an issue after it gets bigger is a no-no for any business. Time is money. Good and faster decision-making is also important to grow in the ever-growing business industry.

Data visualization allows you to make faster and better decisions by detecting trends and spotting problems.

How can it benefit you?

• With up-to-date visual data, make real-time decisions. 
• Have clearer business insights
• Access business performance and make adjustments as needed with KPIs.
• Make data-driven decisions and not decisions based on your gut.
• Take immediate action on key findings.

Data visualizations help detect declines in customer satisfaction, identify sales opportunities, and understand shifts in market demand before competitors do.

In a race to make better, faster decisions, a picture is indeed worth a thousand spreadsheets. 

If you really want better outcomes, improved business productivity, enhanced customer experience or identified growth opportunities, start leveraging the benefits of data visualization today. 

The Challenges of Data Visualization

We all know what goes into building a strong, well-designed house (the foundation, costs, workforce, intricate details, interiors, and so much more).

Data visualization for decision-making is like building a house. The goal is clear: a reliable and designed visualization that points towards the right decision. However, the path to get there isn’t always simple. One wrong decision can lead to confusion instead of clarity.

In data visualization, the challenges lie in the following:

• Selecting the right tool
• Choosing the appropriate visualization method
• High initial costs
• Poor designs 
• Low data quality and inaccurate data 
• Over-reliance on the visualizations
  

Ways to Overcome these Challenges:

• Select the right tool that suits your business needs.
• Make sure that the visualization type you choose is easy to interpret
• Be careful with the visuals. Do not overwhelm or mislead your audience.
• Be prepared for the financial commitment that comes with high-quality customized visualizations.
• Do not make poor decisions that can lead to confusion. Make clear and accurate designs.
• Verify your data beforehand and ensure that there is no missing or misleading data. 

Overcoming these challenges is not a hard nut to crack. Data visualization for decision-making just needs the right structure, tools, techniques and approach.

Furthermore, the data visualization future trends, including AI (artificial intelligence), AR (Augmented reality), and Virtual Reality are even more fascinating. 

Are Data Visualization Services the Right Choice?

Yes, data visualization is absolutely the right choice. Data visualization helps you spend time making better business decisions and taking your business to new heights.

But do you know what the real question is: Can you afford not to invest in it? With the ever-growing data, staying ahead means making quick business decisions confidently. Data visualization helps you do that and lets you stay ahead in the game.

However, for smaller businesses with limited budgets, it could be difficult to justify unless you can demonstrate a data-driven need. You can ask yourself questions such as:

• Do you have large and complex data that needs interpretation?
• Are there non-technical stakeholders for whom you need data-driven insights for better communications?
• Do you have the budget to invest in the tools and expertise?
• Do you want to improve the accuracy of your reports?
• What is it that you want from your visualizations?

If you answered yes to all the above questions, why wait? Start visualizing your data. You can start by talking to a data visualization consultant at Augmented Systems. 

How Augmented Can Help?

Our data visualization consultancy team and developers help you craft impactful stories that turn your data into narratives for strategic growth and innovation.

We won’t give you cookie-cutter solutions. Instead, we will understand your needs, collaborate with you and personalize the solutions for your unique needs.

If you are ready to change your business for the better, contact our data visualization experts today.

FAQs:

 

Q1. What is bad or misleading data visualization?

Bad or misleading data visualization distorts or confuses the viewer, often by using manipulated scales, improper chart types, or incomplete data. 

 

It can exaggerate or obscure trends, misrepresent proportions, or lack clarity, leading to inaccurate conclusions. 

 

Effective data visualization should be clear, accurate, and contextually appropriate.

 

Q2. What is the future of data visualization?

The future of data visualization is in Virtual Reality (VR) and Augmented Reality (AR). These immersive technologies can transform how we experience information.

 

Imagine using a VR headset to explore your data or overlay insights onto your surroundings with AR, making the experience more interactive and intuitive.

 

Q3. How is data visualization used in businesses?

Data visualization in business is used to simplify complex data, identify trends, and make informed decisions. It helps businesses analyze performance, track KPIs, improve communication, and uncover insights that drive strategy and growth.

 

Q4. Why do modern businesses need data visualization?

Data visualization empowers business users to extract insights from vast datasets, identifying patterns and anomalies. Charts, graphs, and dashboards allow users to grasp insights and trends quickly, enabling better decision-making and data literacy.

 

Q5. What is a data visualization consultant?

A data visualization consultant is a specialist who assists businesses in creating clear visual representations of data. They develop charts, dashboards, and interactive graphics to enable organizations to analyze, interpret, and communicate data insights more effectively.

 

Ever seen a sky full of stars? Isn’t it beautiful, awe-fully inspiring, but impossible to make sense of?

Do you feel the same when looking at your big data? It’s insightful and important but complex and confusing.

If you are nodding along, then it does.

Big data can easily become a cluster of constellations with endless data points and zero clarity. But what if you can actually understand and make use of your data sets? 

Get ready to do just that as we bring you the top 10 big data analysis techniques in this blog. 

Why Do You Need Data Visualization Analysis for Big Data?

Do you know the amount of data created, consumed and generated daily? We are breaking down the numbers for you, so take a back seat because they will blow up your mind. 

With data creation predicted to reach 147 zettabytes by the end of 2024 and 180 zettabytes by 2025, daily data created is breaking down its records, surpassing human imagination. 

Data is generated endlessly every single day, and its sources are diverse, including:

• Images 
• Videos
• Social Media
• Google Searches 
• The Internet
• YouTube
• File Sharing 
• E-mails 
• Maps 
• Forecasts 
• Stocks (and many more)

Every move or action you make today is equal to data creation. When analyzing this vast data, many people do not see the forest for the trees. Why? The truth is there is too much raw information, and focusing on the details in between this vastness is very difficult.

This is when data visualization specialists use big data analysis methods, trends, techniques and tools to give meaning to your data.

Big data’s sheer volume, variety and velocity require data visualization best practices to understand and make sense of this ever-multiplying data. Ready to make your data understandable and acted upon? Let’s see how these big data analysis methods can help.

Top 3 Visual Data Analysis Techniques in Big Data

When choosing a data analysis or big data visualization tool, it is important to understand that just picking any chart or graph won’t work. All big data visualization tools are different, so before you start making a decision, ask yourself a few simple questions, such as:

• What relationship between your data sets are you trying to explore? 
• Do you want to analyze a single value over time or multiple values? 
• What are you looking for? Outliners or analysis trends?

Similar questions and the key components of data visualization will guide you in finding the right big data visualization tools. To help you in your decision making, here are some of the most common big data analysis techniques.

1. Regression Analysis

Regression analysis is a statistical method for finding a relationship between a dependent variable and one or more independent variables.

Using this big data analysis method, you can determine how changes in one variable are related to changes in other variables. It’s like trying to figure out how one thing affects another.

For example,

How are marketing and sales influencing your business revenue? Or what’s the relationship between customer acquisition and marketing budget?

Regression analysis lets you see patterns, trends and hidden connections between different data points. You can predict outcomes and estimate what the upcoming results might be. 

How and Why to Use Regression Analysis?

With mountains of data, it can sometimes be confusing to distinguish what matters most from noise. Regression analysis helps you find the important parts of your data by making it easier to understand. Your decisions with regressing analysis will be based on evidence, not guesses.

• You can start by deciding what you wish to analyze
• Ask yourself questions
• Gather and clean your data
• Choose the type of relationship you want to showcase
• Use the right tool to perform your regression
• Analyze the output and
• Find your answers

Benefits:

• Simplifies complex data 
• Helps make predictions
• Provides quantitative backing 
• It’s flexible across industries

Limitations:

• Relies on assumptions
• Overfitting for complex models 
• Sensitive to outliners (It can distort your results)
• Multicollinearity can skew the analysis

Big Data Visualization Tools You Can Use:

• Heatmaps 
• Scatter Plots 
• Regression Line Plots
• Line Charts 
• Interactive dashboards

You can use Power BI, Excel, Tableau, and D3.js for regression analysis of your data. From analyzing business performance to predicting customer behavior, regression analysis helps you bring clarity to your data. It helps you see what’s happening and where to go next. 

2. Time Series Analysis

Another statistical big data analysis technique is time series analysis. This technique analyzes data points collected or recorded over a period of time, allowing you to understand how things change over time.

You can spot patterns, identify trends, seasonality and even forecast future outcomes, helping you track changes and predict the future.

For example,

You can track your business sales over regular intervals, identify the pattern and see if they grow every year or month.

It can also help you check if your website’s traffic has a peak on weekends or predict your next month’s traffic. 

How and Why to Use Time Series Analysis?

Time series analysis is one of the best visualization tools for preparing for what might happen next. It allows you to assess seasonal patterns, quarterly patterns and other cycles over a set period of time. 

To use a time series analysis method start with gathering your data. 

• Collect the data that changes over time
• Visualize it on a timeline
• Identify the trend, repeating patterns and the noise 
• Choose a model to make predictions
• Analyze and forecast predictions 

Benefits:

• Identify long-term trends 
• Better decision-making 
• Highlights lows and highs 
• Easy to understand time-based data

Limitations: 

• Results can be misleading with inaccurate or missing data
• Cannot handle sudden changes 
• Random fluctuations do not provide meaningful insights 
• Difficult to process highly complex data

Big Data Visualization Tools You Can Use:

• Line Plot
• Histogram
• Seasonal Decomposition Plot
• Area Chart
• Time Series Plot 
• Rolling Average Plot 

QlikView, Google Data Studio, MATLAB, Tableau, and Python are some of the tools commonly used for time series analysis. 

Time series analysis is a great tool for time-based forecasting, including weather forecasting and preparing for season demands. For best results, you only have to provide clean and consistent data. 

3. Cluster Analysis

Another visual data analysis technique in big data is cluster analysis. It is a method of organizing your data into groups with similarities. Using these clusters, you can identify similar data points in one cluster.

Thus, instead of making rough guesses on how to organize your data, cluster analysis brings out patterns naturally so you don’t have to (particularly with predefined categories). It’s similar to organizing your closet by color or type of cloth.

For example,

You can cluster data points together for customers who buy product A and a different cluster of customers who buy product B. Each cluster or bubble represents a data point. The size and color of the bubble can represent different attributes of that particular cluster. 

How and Why to Use Cluster Analysis?

The most common types of clutter analysis are dendrograms (tree-like diagrams showing hierarchical data), bubble graphs and multi-dimensional scaling plots. 

You can use these techniques when you want to group, classify or work on customer segmentation, market analysis or high-dimensional data. 

To use a cluster analysis method, start by asking yourself what outcome you want. Do you want to identify patterns in your data or segment different types of customers? 

• Start with a goal 
• Clean your data 
• Standardize it based on variables or scale 
• Choose a cluttering method
• Use tools to run the analysis 
• Plot the clutter to visualize it

Benefits: 

• Helps find hidden patterns
• Helps target strategies better
• Simplifies complexity 
• Versatile for all industries

Limitation:

• Sensitive to the quality of your data
• Different algorithms produce different clusters for the same data 
• Understanding and labeling the clusters can sometimes be complex 
• Sometimes difficult to find out how many clusters you need

Big Data Visualization Tools You Can Use:

• Bubble Chart
• Dendrograms
• 3D Scatter Plots
• Elbow Curve
• Spider Plot 
• Heatmaps
• Box Plot

Cluster analysis will help you organize your chaotic data and make intuitive decisions. You can use tools like Tableau, Power BI, QlikView and Google Data Studio.

See Data Like Never Before 

The ocean of data available is not stopping any time soon. Does that mean you cannot grasp the individual drops from it? No. Visual data analysis techniques in big data empower you to comprehend the finer details of your data within the bigger picture.

It may not be as simple as dressing up your graph to make it look better, but it is a powerful tool for big data.

Don’t want to let your data go to waste? Consult our team of data visualization specialists. We will transform your data the same way we’ve transformed countless others.

FAQs:

 

1. What is visualization data analysis?

 

Data visualization is a technique for converting raw information or data into a visual format, such as a map or graph. It is done to make data easier for us to understand, interpret and make data-driven decisions. 

 

2. What are the types of big data visualization categories?

 

Various forms of data visualization exist, including but not limited to bar charts, line charts, scatter plots, pie charts, and heat maps. These represent commonly used methods for presenting and interpreting data.

 

3. What are the benefits of analyzing big data?

 

The benefits of analyzing bid data are many, including:

• Improved decision-making
• Increased agility and innovation
• Better customer experiences
• Continuous intelligence
• More efficient operations
• Improved risk management

 

4. What is big data analysis?

Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from varied, high-volume, high-velocity data sets. These data sets may come from various sources, such as web, mobile, email, social media, and networked smart devices.

 

5. Why is big data used?

 

Big data allows you to collect and process real-time data points and analyze them to adapt quickly and gain a competitive advantage. These insights can guide and accelerate the planning, production, and launch of new products, features, and updates.

Does your spreadsheet look like the Great Wall of China—filled with numbers, endless rows of revenue figures, profit margins and stock fluctuations? 

If you’re reading this right now, it probably does.

Shifting through rows and columns of data could leave you clueless with a room full of errors. 

But what if there was a way to give meaning to your data – More clarity, better understanding and actional insights?

This is where a data visualization consultant steps in. With their expertise, you can transform complex financial data into clear and compelling visuals that drive informed decisions.

Data visualization in finance can turn the tables and do just that. How?

Let’s break it down together in this blog. 

What is Data Visualization in Finance?

Data visualization in finance is the process of visualizing massive sets of unprocessed data into graphical representations. Visually appealing and easy-to-understand visualizations help you make better financial decisions.

These visualizations represent financial data, reports and statements to provide insights into different financial metrics. These metrics include:

• Sales figures
• Profit and loss
• Equity
• Assets and liabilities
• Income and expenses and other financial data.
  

But you must be wondering, isn’t this what spreadsheets and other financial tools already do for you? Well, not everyone on your team has the data science skills to interpret all of the ever-growing data or the time to spend hours understanding it. Isn’t it?

That’s when the role of data visualization enters into the picture.

What is the Importance of Data Visualization in Finance?

In traditional methods, humans crunch numbers, and decision-making is based on assumptions drawn from calculating risks and trends. However, with global data expected to skyrocket to 180 zettabytes by 2025, these methods are becoming absolute.

Finance teams are under pressure, and the sheer volume of data highlights the necessity for advanced tools and strategies.

Here’s when data visualization bridges the gap in transforming, processing and analyzing Big Data. Moreover, businesses that prioritize visual analytics are 28% more likely to find timely insights. 

 What more can you do by visualizing financial data? You can: 

• Take advantage of trends
• Improve performance
• Enhance service offerings
• Set realistic goals
• Make more informed decisions
• Improve communications with stakeholders
• Tell more compelling and clear stories 
• Forecast revenue (Apart from many more things)
  

You would be wrong to think data visualization is all about collecting data, processing it and presenting it. It isn’t all that valuable until your stakeholders or end-users can draw meaningful conclusions.

Therefore, different charts and graphs are used to make the visualizations simple and easy to understand. 

What are the Common Types of Financial Data Visualizations?

An often-used statement on the internet states, “People process visuals 60,000 times faster than words.” Therefore, charts, graphs, pictures, diagrams and plots are used to understand the data relevantly.

There are several types of data visualizations, and we would like to re-familiarize you with a few common ones.

• Bar Charts 

In the finance sector, you can use a bar chart to compare data across different categories. You can also visually present prices over a given period of time. The vertical axis of the bars represents the highest and lowest data range for any specific asset.

Complex and big financial data is easy to interpret for visualizing trends, comparisons, and distributions. Using these charts, you can track expenses, perform a revenue analysis, visualize returns for assets and do a risk analysis.

• Line Graphs 

A line graph, a line plot or a line chart is another visualization graph that uses lines to connect individual data points. You can represent a specific value at a particular time interval at each point of the line graph. It also visualizes trends and changes over time.

The graph depicts an asset or security’s historical price action. It is also ideal for tracking continuous data, identifying peaks or dips, and observing patterns in financial metrics.

By monitoring stock price fluctuations and revenue growth over time, analyzing trends and understanding liquidity patterns, you can forecast your business’s future performance.  

• Scatter Plots 

Commonly used in statistical analysis to visualize numerical relationships, scatter plots, scatter charts or XY graphs, represent numerical data points. The data points have one variable on each axis with data plotted as points on the graph.

A regression line runs through the dots that represent the relationship between the variables. Widely used in finance, these graphs allow you to identify correlations, trends and outlines to make data-driven decisions.

Moreover, you can also use scatter plots to analyze risk vs. return, perform loan analysis or analyze changes in expenses impacting your revenue. 

• Heat Maps

A heatmap is a two-dimensional data visualization graph that uses colors to represent data and values. The map is typically a grid of colors, with each cell colored differently based on the values.

They are often used in financial analysis to represent data such as stock prices, trading volumes and market trends.

From risk management to revenue performance, heatmaps can help you identify areas of concern, opportunities and patterns that may not be obvious, enabling you with faster insights.

Each visualization fulfills a specific purpose but shares a common goal of extracting meaningful information from your scattered data and numbers.

The key factors of data visualization are helping the end user understand their data, uncover trends and tell a data-driven story. Visualization will help you analyze your data perfectly and help avoid confusion.

Leveraging correct financial data visualization tools, you can easily turn your messy, raw data into user-friendly and easy-to-read visualizations.

The next question is how to correctly and perfectly visualize your data. To help overcome this challenge, we have a few tips. So keep reading. 

6 Best Practices for Financial Data Visualization

Visualizing financial data is similar to navigating through a new city without a map. You might reach your destination, but there are so many changes. You can either get lost a few times or take a much longer time to reach it.

Therefore, even though your charts and graphs look pretty, they won’t be of any use if you don’t follow the best practices for visualizing them. We have a few tips for you to help you give your visualizations an edge. These include: 

1. Keep it Simple and Clear

The key to an impactful data visualization in finance is to keep it simple and clear. Your user should be able to easily understand and interpret the data presented.

Lack of clarity and simplicity may lead to confusion and misunderstandings. A clear, concise and straightforward data visualization is intuitive and effective.

Tips for Simple and Clear Visualization

• Don’t overload the data, focus on only the essential data
• Avoid using distracting elements
• Use a logical and consistent color scheme
• Add clear labels, such as axes and titles
• Ask for advice to ensure clarity and relevance
  

Simple data visualization will enable your end-user to grasp the information quickly, leading to effective decision-making and a better understanding of your financial data. 

2. Make it Interactive

Interactive features engage your audience more with your data and offer better insights. Your users will rapidly explore the information, recognize patterns and evaluate the data better.

Tips to Make Your Visualization Interactive

• Use features such as tooltips, filters and zoom functionalities
• Add design elements to your graphs and charts 
• Create interactive dashboards
• Integrate your visualizations for real-time tracking 
• Provide side-by-side comparison visualizations
  

Interactive data visualization in finance will help your audience to:

• Analyze the data 
• Recognize trends
• Explore and compare scenarios
• Manipulating the data for a better understanding
• Engage with the visuals
  

Interactive visualizations are crucial, especially for real-time financial data visualizations. 

3. Understand Your Audience

Before you finalize the type of visualizations for your project, ensure that you first understand who your audience is. Are they your employees, executives, stakeholders, or managers?

Do they need a clear trend line, high-level insights or bar charts breaking the monthly expenses?

It is crucial to understand your audience and tailor your visualizations to their knowledge level and needs.

Tips to Understand Your Audience

• Find out who your audience is. Are they financial experts or stakeholders?
• Understand their goals and what they desire from the visualization
• Consider their time 
• Ask for feedback from your colleagues 
• Know your audience’s pain point
• Use the language they prefer
  

Understanding your audience will ensure clarity, prevent confusion and help them make better decisions.

4. Tell a Story with Your Data

It is important to weave a compelling story around your financial data. Visualizations are not about pretty pictures. Each visualization should guide your end-user through key insights.

Every chart or graph you create should have a clear purpose. You can start by asking yourself, “What is the key takeaway of your project?”

Tips for a Compelling Story

• Create a narration, and give a start, middle and end to your data story
• Show how the data evolves and why it matters.
• Make the complex data, digestible by breaking them down
• Offer clear recommendations 
• Bring context into your data 
• Explain why the data matters in the real world

Moreover, to make the story compelling, you can use titles, headings, and captions and guide your audience using a story for your data. For example, you can highlight the monthly or quarterly profits and growth trends and pinpoint the spikes and lows. 

5. Ensure Accuracy and Consistency

Accuracy and consistency mean validating your financial data and visualizations. This does not mean that you show or present all your financial details, but the visuals should not mislead the audience.

Tips to Bring Accuracy and Consistency

• Double-check the numbers
• Do not compromise on details and data sources
• Keep your data updated 
• Be consistent with fonts, colors, scale and labels 
• Cross-check for errors 
• Clearly define assumptions and limitations
  

Accuracy is crucial to ensure credibility and trustworthiness, as small errors can lead to big disasters and discrepancies.

Additionally, consistent use of colors, fonts, tone and other visualization elements makes your visualizations less distracting and ensures credibility. 

6. Use the Right Visualization Tools

Selecting the right charts and graphs matters, but what is equally important is choosing the right financial data visualization tools.

There are several tools available in the market for financial data visualization. Tools such as:

• Tableau 
• Power BI 
• Infographics and more
  

Also, make sure that you choose the right visualization for the right project. Use bar charts for comparisons, line graphs to show trends over time, pie charts for illustrating proportions and heat maps to reveal trends.

Tips to Choose the Right Visualization Tools

• Understand and assess the complexity of your data 
• Research for a tool that is compatible with your existing system
• Look for a tool that is user-friendly and customizable
• The tool should be flexible and scalable 
• Consider the cost of the tool and its value for money 
• Try different tools before you finalize one
  

The right visualization tool will help you avoid clutter, and each element or visual will help you serve a specific purpose for your visualizations.

The importance of data visualization cannot be overstated in the field of finance. However, it needs to be well presented. When done poorly, the visualizations will only confuse your audience, resulting in misinterpreting the data and making them feel more frustrated.

To avoid poor visualizations, you can also consider hiring data visualization consultants to help you transform your complex financial data. 

Tips to Avoid Pitfalls in Financial Data Visualization

Ever seen a confusing graph overloaded with numbers? Were you able to interpret anything out of it? Maybe a little. 

Data visualization is a great way to transform your raw data into actionable insights. However, small missteps and poor decisions can lead to potential pitfalls, hindering your progress. Here are a few tips to help you avoid these pitfalls when visualizing financial data.

1. Do not over-simplify or overdo your visualizations.
2. The user should have proper knowledge of the tools and their elements. 
3. Avoid using missing or misleading information.
4. Remember to follow regulatory guidelines. 
5. Check with the management for confidential data. 
6. Integrity and quality of data are paramount. 
7. Check for human errors before presenting the visualizations. 
8. Do not overlook important information due to over-reliance on the visualizations.
9. Poor storytelling might create complete chaos and confusion. 
10. Give proper context to your numbers. 
11. Clearly label everything. 
12. Use proper colors. Use them wisely. 
13. Ensure your axes remain consistent. Watch out for scales.
    

By following best practices and keeping these tips in mind, you can ensure to implement a successful visualization for your financial data. Avoiding these pitfalls will make your visualizations insightful. 

Conclusion

Collecting data is easy. Businesses collect all sorts of data every minute. What matters is reading, analyzing and presenting the data. 

Right findings will only take your business to new heights, so utilize data visualization to present data findings in a way that makes sense to everybody and helps your business succeed. 

If you think your financial data feels overwhelming or lacks direction. Contact our data visualization consultants with specialized skills to help you craft visuals just right for your audience.

FAQs

 

1. What is the role of data visualization in finance?

Data visualization simplifies the task of recognizing patterns and identifying potential issues in your organization’s finances. It enables you to pinpoint which processes or products are successful and profitable, as well as those operating at a loss or requiring optimization.

 

2. How does data analytics help in risk management?

By leveraging data analysis, you can uncover hidden patterns often missed by traditional risk assessments. This approach allows you to identify risks and proactively mitigate them, ensuring better outcomes and greater stability.

 

3. What is a data visualization consultant?

Data Visualization Consulting is a specialized field that uses visual representations to convert complex data into intuitive, actionable insights. As businesses gather extensive data, it becomes crucial to interpret and utilize this information effectively.

 

4. How is financial data analyzed?

One of the most common ways to analyze financial data is to calculate ratios from the data in the financial statements to compare against those of other companies or against the company’s own historical performance.

 

Financial analysis can be conducted in both corporate finance and investment finance settings. 

 

5. How to visualize financial statements?

Use a line chart, column chart, or area chart to show how your data changes over time. These charts highlight trends, making acceleration, deceleration, and volatility more visible.

In the information age, attention spans are decreasing, whereas growth is more important than ever. 

In the era of thrive or die, how can you reach more people and inform them of your products and services?

If you’re still with us, that means this question is of vital importance to you. 

And the answer to your question lies in visualization. 

We believe that visualization holds the power through which you can gain and retain customers even in the age of ever-reducing attention spans.

However, data visualization isn’t without its challenges. Just as vivid visualization vamps viewers, poor picturization promotes peril. 

So, why are some data visuals more attractive than others? What sets a good visualization apart from a poor one? 

Well, visualization is like any other concept—there are ideal ways to use it and mistakes that can blur its message. 

But don’t worry; we won’t let your visuals get lost in the void of visualization errors!

In this blog, we’ll tell you how you could be committing data visualization heresy. We’ll also baptize you with data visualization’s best practices to help you engage with your target audience!

Pitfalls of Poor Visualization – Is Bad Visualization Really That Bad?

The role of data visualization has been to transform the way we tell stories or convey information. Gone are the days when people clung to every word they read while exploring ideas. Scanning is the new reading.

So, before we understand what data visualization mistakes you can make, let’s see why you should avoid them in the first place:

• “Failed to Deliver Message” 

What does the data visualization process entail? To create an image that says a thousand words without using any. So, it’s not that bad visualization doesn’t paint a picture. Rather, the visual invokes words that aren’t the ones you want it to. 

So, the first peril of bad visualization is its failure to deliver the intended message.

• Inaccurate Visualization is Broken Communication

It’s not that viewers will just ignore bad visualization. After all, how would they even know that the visualization contains a mistake without at least glancing at it? So, unless you’re using really repulsive colors (which, as we will see, you should avoid at all costs), people will at least give your visual a chance. 

And that’s exactly where the problem lies. If the viewer trusts your visual or is enticed by it, they’ll expect it to convey something useful. 

If it doesn’t, they might not appreciate the message. Worse, they’ll probably not even give your next visual a fair chance. 

To them, you’ll be the boy that cried wolf (or “look here”). Thus, poorly visualized data can be worse than no visualization at all!

• Visible Waste of Resources

Data visualization consultants say that every instance of visualization has an opportunity cost. The radar chart you drew up to demonstrate your product’s strength to potential customers could instead have been a Venn diagram that did both – conveyed the strengths and convinced them to purchase it. 

Remember, a billboard, the first slide of your presentation, or the lead image of an Instagram story can only accommodate one visual. Thus, every suboptimal visual is an opportunity lost. And we’re not even accounting for the time and the resources wasted in making the visuals themselves.

Now that you see why you should take a closer look at the visuals you create and use, let’s consider what mistakes could make them less effective.

Common Data Visualization Mistakes (And How To Avoid Them)

Not all of us are data visualization specialists. So, data visualization mistakes can be hard to see. After all, you just converted complex calculations into accessible images! What could’ve gone wrong?

Well, let’s look at a list of common errors to watch out for when before and during the process of data visualization:

1. Warning: Information Overload Detected!

Why do we visualize? To convey a message without asking the viewers to perform mental gymnastics. Well, bad visualization does both: it puts an image into the viewers’ minds and asks them to interpret its meaning. 

Thus, bad visualization takes the scenic route to failure – the list that should’ve been an image becomes an image that might as well be a list! How should you avoid this?

• Identify Purpose

Before visualizing the data, visualize the purpose of visualization. Why do you want to show your viewers this data? Do you want to inform, invite or incite? 

Once you know why you’re visualizing, you’ll better understand how you should do it.

• Use Interaction

Sometimes, backing a visual with a lot of data is unavoidable. For instance, say you’re running a fantasy football and betting app. Your visualization must be supported by detailed analytics so that participants can understand trends and odds. 

How should you avoid overloading information in the visual? 

By allowing users to select what data they want to visualize! They can select the month, the player, the game or the team whose data they want to analyze visually. Interaction allows viewers to be in control, which retains their attention and even increases traffic.

2. Use the Wrong Charts, Get the Wrong Results (Alt: Wrong Charts = Wrong Results)

Another common data visualization mistake is to use the wrong medium. 

For example, let’s say you want to show your team quarterly sales trajectories of the three products you launched last year. Should you choose three different bar graphs or one neat column chart? 

You could go with the first one – but everyone will hate you after sitting through the sales meeting. 

And that’s certainly not the goal of your visualization!

So, you must avoid data visualization’s problem-solution mismatch. Every type of data will have an ideal visual representation. 

But how will you know which visual fits what data? Consider two suggestions:

• Study Available Variants

After you have identified why you want to visualize your data, explore different media of visualization.

• If you’re going to demonstrate the user intent of a single product, use a pie chart. 
• Go for a Venn diagram to identify common user intents of different segments. 
• If you want to understand the buying journey, visualize it through a Gantt chart.

 

• Try Different Charts

Sometimes, instincts can fail you: a chart you through was optimal for a dataset looks skewed now that you’re staring at it. 

In such cases, data visualization consultants suggest you try visualizing the same data through different charts until you spot the ideal visual. 

You don’t get a second chance at first glance, so iterate till you’re satisfied you’ve found the best version!

Criticizing your creations is hard. After all, you understand the work and the story behind your data, so why wouldn’t others? 

Precisely because they’re not you! 

We use visuals because we understand the data and want the viewers to grasp what we see. But still, we’re the ones making the visuals. How should we account for this gap?

Source

 

• Visualize for Viewers

First, ask yourself: For whom are you visualizing the data? Fellow employees? Potential customers? Executives? Partners? 

Curate the visualization based on their experience, expertise and enthusiasm. Tailor it to the level of their investment in seeing the visual. Match the intent behind the visualization with the viewer who needs to be convinced.

• Visualize Where?

Second, consider where people will encounter your data visualization. Is it for a captive audience during a product launch? For a billboard that people could easily miss? Or will people come across it on their Instagram feeds?

• A Venn diagram could look great on a billboard. 
• But people could definitely look at a radar chart on their Instagram. 
• On the other hand, even a captive audience would appreciate a simple pie chart showing them how much discount they’re about to get.

So, visualize how and where people will encounter your visualization before rendering it.

4. Error: Narrative Not Found

Good visualization tells a story. For instance, a Venn diagram can tell viewers why they’re at the center of a product’s appeal. A heat map can advise them to drop a player from their fantasy football team. A density map could convince them to come and live in your new rental unit. 

Similarly, a bad visualization doesn’t tell a story: it merely displays data. 

Poor pictures may have a purpose, but they don’t have the right one. A good visualization always tells a story; if it doesn’t, the users will make one up on their own or forget the visual. Either way, your resources get wasted. To avoid this: 

Source

• Tell a Story

Before visualizing, narrativize. Ask yourself: what does the data tell you? What would convince the viewers? 

Bridge the gap between the two questions to develop a story that fits the data and will appeal to the viewers. Even if the viewers forget the visual, they’ll remember the story. After all, isn’t that the goal of a visualization?

• Seek Feedback

Even the best data visualization consultants seek feedback on the visuals they render. We recommend the same. Feedback helps break through creator bias and look at the visual through a different lens. 

At the same time, it allows you to understand the narrative that your visual constructs in the viewers’ minds. If it’s not what you intended, refine, reframe, and rework the visual.

5. Dirty Designing

Data visualization is as much about visualization as it is about data. Without bright colors that bite or a consistent scheme that soothes, your visualization is incomplete! 

And an incomplete visualization will convey an incomplete message! So, colors are critical for clear delivery. Here’s how to avoid displeasing design, the deity of data visualization:

Source

• Choose Consistent Colors

Sticking with the theme of your visual is as vital as choosing one. This is easier said than done. Answer three questions before picking the colors for your visualizations:

I. What colors soothe and suit the message?

II. Which colors can distract the viewers from the story?

III. Where should color contrast be avoided to minimize noise?

To pick consistent and comforting shades for your visuals, respect the holy trinity of coloring visualizations.

• Let the Colors Talk 

Sometimes, colors offer more than contrast. They represent more than different variables. They can also show sensitivity. 

For instance, if you’re a health insurance agent, a visual representing the cost of medical care in red and the savings through insurance in green can symbolize blood and money without you even mentioning the words. 

So, don’t just pick contrasting colors; tell a color story.

• All About the Data

Sometimes, your visuals are doomed before you even render them. Why? Because they’re based on shaky data. After all, how can your visual convince the viewer if its message itself is incorrect? 

For instance, an outrageous visualization that contrasts Republican and Democrat voters can garner a million views.

However, if you fail to contextualize the data with population density, the comments on the visual will drive you to infamy. Thus, clean data is a prerequisite for clear visuals.

Here’s how you should avoid visualization errors due to convoluted data:

• Clean the Data for Consistency

Sometimes, your sales figures tell a brilliant visual story. But, the scale on which they are compared is inconsistent. While Product P is scaled per thousand units sold, Product Q is measured for every ten thousand units. 

Remember, if your visual narrative is outrageous but based on an error, even one closer look will unravel it. 

Ironically, the visualization that is supposed to aid your storytelling could be the end of your story. So, the moral is to clean the data as much as possible before visualizing it.

• Digest the Data Before Designing the Visual

“If you torture the data long enough, it will confess to anything,” goes the adage. Remember, confessions under torture are almost always inaccurate: they’re volunteered out of insight but spat up for survival. 

The best visuals aren’t aimed at survival; their goal is to convince. And in the long run, it is better to convince people of genuine insights rather than lead them to quick conclusions. 

To this end, don’t design a visual and then hunt for data that fits. Trust the process: follow the data and show the viewers its genuine conclusion.

Data Visualization Consultants Say…

Visuals used to cost a dime a dozen. With the advent of AI, they cost even less. Or do they? 

As we have seen, bad visualizations cost more than money. Defective data, dirty design, no narrative, biased basis, clunky charts, and invasive information can blur even the most vivid visuals. They can drive users away, fail to deliver the message and waste your resources. 

So, what should you do to ensure you follow data visualization’s best practices? 

We can help! At Augmented Systems, we specialize in helping you eliminate data visualization mistakes by providing you with visualization experts tailored to your company’s needs! 

Look no further for expert visualization assistance: contact us today, and together, let’s create promising visualizations for your audiences!