Introduction

What is data visualization?

Data visualization is the process of translating large data sets and metrics into charts, graphs and other visuals. The resulting visual representation of data makes it easier to identify and share real-time trends, outliers, and new insights about the information represented in the data.  

A dashboard is an information visualization tool. It helps you monitor events or activities at a glance by providing insights on one or more pages or screens. Unlike an infographic, which presents a static graphical representation, a dashboard conveys real-time information by pulling complex data points directly from large data sets. An interactive dashboard makes it easy to sort, filter, or drill into different types of data as needed. Data science techniques can be used to identify what is happening, why it's happening, and what will happen next at speed.

As the amount of big data increases, more people are using data visualization tools to access insights on their computer and on mobile devices. Dashboards are used by business people, data analysts, and data scientists to make data-driven business decisions.

Why data visualization is important

See what's happening, why, and what to do next

See the big picture

Uncover insights and see patterns within complex data without relying on a data scientist.

Make better decisions

Understand your next steps and spend less time performing data analysis. Quickly act on decisions.

Present meaningful data

Share insights with others in an easy-to-understand form.

Democratize your data

Provide one source of truth for your entire organization.

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Chart types

Charts are often divided into categories based on their goals, aesthetics or visual features. Here are a few examples.

Trends

Trend charts represent data along with the time dimension. Use them mainly to track changes over periods of time of varying duration and scale. Examples include line charts, area charts, histograms and stream charts.

Comparison

Charts designed for comparison aim to visualize differences between elements. Examples include bar charts or bar graphs, bubble charts and radar charts.

Part to whole

The goal of these charts is to show the inner subdivision of a value among different categories or groups. Examples include pie charts, stacked bar charts, stacked area charts and treemaps.

Correlations

These charts are used for multidimensional data, for example, correlation between phone-call duration and customer satisfaction. Examples include scatter plots and heatmaps.

Relationships and connections

Charts included in this category represent hierarchies. They explain the role of an element within an ecosystem or to observe the inner nature of a subject in different phases and states of a process. Examples include alluvial diagrams and tree diagrams.

Maps

These charts are used for geographical data, for example, voters by county or average wage by neighborhood. Examples include choropleth maps and connecting lines.

Tips to create visualizations

Break down the complexity into steps

Define your intent with users

Identify who can benefit from data visualization. Rely on simple charts up front.

Understand and clean data

Look at your data set structure typologies. Analyze rows and columns for inconsistencies.

Model data, check for visual validity

Use basic visual models to see and understand enormous data sets. ID patterns and trends.

Experiment with structure and style

Draft different design versions. Try a variety of charts while staying within your established organizational graphic standards.

Test and iterate

Gather user impressions and opinions. Use research to influence visualization method iterations and justify changes.

Refine and implement

Look for bugs and functional errors. Check your visualizations for inconsistencies.

Data visualizations at work

Guided decision-making

“People across the organization reach out to me with problems, and I bring solutions to them through data and visualizations,” Sri Vijay, BI specialist, North York General Hospital.

Predict the trends

IBM’s COVID-19 dashboard allows any kind of user, from scientists to medical professionals, to easily use visualization techniques to see new data, helping them with strategic real-time decision-making.

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