Understanding, Communicating and Making Informed Decisions with Data Visualization

In this article, we will explore the many use cases of data visualization, including the benefits it offers to decision-makers and analysts.

Usage

Data Warehousing

Data Warehousing is a crucial process for modern businesses, enabling them to collect and store data from various sources in a centralized location. This data is then used to support business intelligence and analytics, providing valuable insights into market trends, customer behavior, and operational performance.

By utilizing Data Warehousing, companies can analyze vast amounts of data quickly and efficiently, using bar charts, pie charts, and line charts to visualize trends and patterns. This information can help businesses make informed decisions based on market trends, identify new opportunities, and manage risks.

For example, a finance company can use data warehousing to keep track of the balances and transactions as well as to generate dashboards that provide a clear overview of customer performance.

Benefits

1. Improved customer engagement.
2. Identification and management of risks.
3. Market Analysis and investment performance.
4. Data-driven decisions to drive business performance.

Data Analytics

Data Analytics is the process of collecting and analyzing large amounts of data from multiple sources to gain insights and understanding of customer behavior and preferences. One of the most critical components of data analytics is data visualization, which presents complex data in a clear, easy-to-understand format.

In the healthcare industry, data analytics is used to analyze the patient’s lab results, history, and trends in the patient’s health. The insights gained from data analytics are often presented using visual representations such as heat maps and plots, which help identify higher-risk patients and provide critical information to healthcare providers.

Benefits

1. Improved quality of care for patients.
2. Informed decisions with complex data in a clear format.
3. Analysis of public health data that can improve population health.
4. Innovation support.

Data Science

Data Science and Visualization canbe powerful tools when used together to solve multiple complex problems. The data should be collected, cleaned, and transformed to extract meaningful insights and make relevant predictions. While data science methods such as regression and machine learning can provide accurate predictions, visualizing the data can present the results in a more compelling and easy-to-understand format.

For instance, a transportation company can use data science to analyze customer data to make sales predictions while visualizing the data shows the predicted sales that can help in inventory management and strategies in marketing.

Benefits

1. Improved and informed decisions.
2. Saving time and effort.
3. Easier understanding for non-technical stakeholders
4. Reduced errors and greater accuracy.

Now focusing towards the Significance of Data Visualization.

Significance of Data Visualization

The significance of data visualization lies in its ability to communicate information and insights in a way that is easy to understand and digest. The importance of Data Visualization is rooted in terms of data analytics. Some metrics that make sense of a company’s past and future are:

• Revenue: Measuring revenue growth over time can help identify trends and forecast future revenue streams.
• Customer Retention: Analyzing customer behavior and identifying factors that impact customer retention can help improve customer loyalty and reduce churn.
• Website Traffic: Monitoring website traffic and user behavior can help identify opportunities to improve user experience and increase conversions.
• Employee Performance: Tracking key performance metrics such as sales, productivity, and customer satisfaction can help identify areas for improvement and optimize workforce management.

Data visualization aids in identifying and mitigating risks, including fraudulent activities, while also enabling informed decision-making critical for a company’s future.

Fundamentals of Data Visualization

Data visualization fundamentals include the following:

1. Data Quality: Ensuring that the data being visualized is correct and complete.
2. Respective Context (Financial, Healthcare, Transportation):  Understanding the business objectives, key metrics, and target audience.
3. Data Representation: Choosing the right charts and visuals to represent the data.
4. Interactivity: Providing interactive features that allow zooming in and hovering over the data.
5. Dashboard Design: Dashboards that are visually appealing and easier to understand.
6. Privacy and Security: Ensuring that the visualizations are secure and that they have the necessary access to view the data.
7. Data Governance: To process and manage data quality.

Now let’s head towards the types of visualizations and the different tools we can use

Types of Visualizations

Dashboards are the most effective forms of visualization, especially for tracking and presenting data from multiple sources in a clear and concise manner. The most common techniques for visualization include:

Tables

Tables are an effective means of presenting a large amount of structured information, with rows and columns enabling easy comparison of variables. However, when it comes to identifying high-level trends, users can easily become overwhelmed by the sheer volume of data presented in tables.

Pie and Stacked Bars

Pies and Stacked Bars are graphs that get divided into sections but represent the part as a whole. These are simple techniques to organize the data and compare the size of each component.

Line and Area Charts

The visuals that show change by one or more quantities in plotting a data point series over time are used frequently. Line graphs are particularly useful for showing changes, while the area charts connect more data points, stack the variables and allow for easy comparison between the variables.