Why Data Visualization is Important in Descriptive Analytics?

Data visualization is presenting data visually or graphically to uncover patterns, trends, and correlations that would not be found in raw data. Trends and patterns in data cannot be recognized and go undiscovered if not presented visually. Data visualization tools are more popular and easier to use and understand than statistical software.

Data visualization is a key component of business intelligence (BI). Most BI application software places a high value on data visualization and offers robust data visualization features. Data visualization is an essential tool for data scientists and analysts. It enables them to gain insights into data trends, behaviors, and patterns. However, visualizing data has practical use. Individuals and entrepreneurs commonly use it in decision-making by analyzing and visualizing results.

Individuals and entrepreneurs frequently use data visualization (DV) to sway public opinion and send targeted messages. Data scientists and analysts rely on DV to gather insights into purchasing trends, shared behaviors, and consumer wish lists, it is also useful in the real world.

Why is Data Visualization Important?

Data visualization is important because it converts complex data sets into visually appealing representations that are easier to comprehend, interpret, and analyze. Data visualization creates graphic representations to present data. The presentations are mostly descriptive, focussing on 'raw' data and short summaries. They can display transformed data, often based on complex transformations. The primary purpose is to visualize data and statistics to evaluate results and get insight.

Key Reasons Why Data Visualization is Important

Here are several key reasons why data visualization is important −

Identifying Trends and Patterns − Data visualization enables individuals to easily identify trends, anomalies, and patterns in the data. For example, a line graph can depict trends over time, but a scatter plot can show correlations between variables.
Improves visual presentation of Data attractively − Data visualization using graphs, charts, and maps makes complex patterns, relationships, and trends easier to understand. Humans assimilate visual information more quickly than bare numbers or text.
Assist in Decision-Making − By making data more accessible and intelligible, decision-makers may swiftly get insights and make informed decisions. Visual data makes it easier to compare various options and results.
Improves Data Storytelling − Data visualization can create a fascinating tale by bringing data to life. With the right visuals, you can lead the audience through a narrative, resulting in increased engagement and retention of information.
Simplifies Large and Complex Data − Complex and large datasets can be overwhelming in their raw form; however, visual tools such as heat maps or pie charts can simplify this data and make it easy to understand.
Data-Driven Culture − In organizations, good data visualization technique supports a data-driven culture by analyzing data to produce graphical results and using data insights in strategic decisions.
To find Errors and Outliers − Visualisations aid in detecting data flaws, inconsistencies, and anomalies in raw datasets, leading to improved data accuracy and quality.
Interactive Exploration − Many modern data visualization tools have interactive aspects that allow users to explore data from various perspectives. This dynamic interaction allows for more in-depth data exploration.
Engaging and Persuasive − Well-designed visuals can increase the engagement and persuasiveness of presentations, influencing opinions and driving action based on data insights.

Data visualization is a useful tool for simplifying complex information, improving communication, and facilitating more accurate and timely decision-making.

How does Data Visualization Work?

Data visualization works by transforming raw data into visual formats that allow for easier analysis, interpretation, and communication, making it a vital tool in data-driven decision-making.

Steps of Data Visualization Process

Data visualization works by transforming raw data into visual representations like charts, graphs, maps, and other graphical formats, making it easier for people to understand and analyze information. The data visualization process involves the following steps −

1. Choosing the Right Visualization Technique

Visual representations of data may vary as it depends on the type of data. For example, bar charts, pie charts, and treemaps are used for Categorical Data. Line graphs, scatter plots, and histograms are used for Numerical Data. Maps or heat maps are used for Geographical Data and Tree diagrams and dendrograms are used for Hierarchical Data.

2. Mapping Data to Visual Elements

This entails mapping data values to visual elements including position, size, color, and shape. For example: Representing multiple dimensions, such as time on the X-axis and amount on the Y-axis. Used to distinguish between groups or indicate the size of a value. Different shapes could represent various data points or categories. Numerical data is scaled to fit within the visualization (for example, normalizing numbers to a range of 0-100%).

3. Data Visualization Tools

Data visualization tools are essential for descriptive analytics because they convert raw data into graphical representations that are easier to understand and analyse to gain data insights. These technologies help the process of finding trends, patterns, and anomalies in data easier for decision-makers. There are various tools available to create visualizations, such as Excel or Google Sheets, Tableau, Power BI, D3.js, Plotly, Matplotlib, and Customization. Some of the key data visualisations charts and graphs used in descriptive analytics are as follows −

Bar charts and column charts − Used to compare categories.
Line charts − Effective for showing trends over time.
Pie charts − Show proportions of a whole.
Histograms − Display frequency distributions.
Heatmaps − Represent data density or concentration.
Scatter plots − Show relationships between two variables.
Dashboards − Combine multiple visualizations on a single screen for a holistic view of key metrics.

4. Analysis and Interpretation

In descriptive analytics, analysis and interpretation entail summarizing and explaining historical data to provide insights into what has occurred during a specified time period. Descriptive analytics aims to understand historical performance, find patterns, and summarize data using some set of suitable methodologies. Overall, it includes potting Trends and Patterns, Comparisons, and Data Insights. Interpreting data with reference to business or operational context. For example, why did sales increase in a given month? Could it be related to a marketing campaign or a seasonal trend? Using data interpretation, a user can derive relevant insights from the analysis, such as key performance indicators (KPIs), performance patterns, or areas for concern.

It provides the findings in a concise way, allowing stakeholders to make informed decisions. This could involve creating reports or dashboards that highlight the most important insights.

5. Communication

Data visualizations are used to share findings with others, whether through reports, dashboards, or presentations. A well-designed visualization facilitates the rapid communication of critical insights. Data visualization is frequently an essential component of data storytelling, in which data is utilized to construct a narrative that leads to informed decisions or strategic actions. Communication in descriptive analytics entails successfully presenting and explaining the findings from data analysis. The goal is to present the findings in a way that is clear, actionable, and relevant to the audience's needs, allowing for informed decision-making. The process consists of numerous crucial components, each of which is critical for converting data insights into intelligible information.

Data Visualization in Descriptive Analytics