What is data visualisation?

Data visualisation is a graphical representation of data and information to present complex datasets in a visual format that is easy to understand, interpret, and analyse. It is a powerful tool used in various fields, including business, science, research, and journalism, to communicate insights, patterns, and trends hidden within data.

The role of data visualisation in business strategy

Data visualisation plays a crucial role in business strategy by helping organisations make data-driven decisions, identify opportunities, and gain a competitive advantage.

It contributes to business strategy as it:

• allows business leaders and analysts to explore complex datasets visually, which helps in understanding the current state of the business and identifying areas for improvement,
• helps businesses identify and monitor key performance indicators (KPIs) to track progress toward business goals, identify deviations from targets, and take timely corrective actions,
• allows to monitor real-time data and make data-driven decisions on the fly,
• simplifies complex data and presents it in a visually appealing and understandable format,
• makes it easier to communicate complex data and insights to stakeholders, including executives, investors, and clients,
• aids in market analysis and competitive intelligence by visualising market trends, customer behaviours, and competitor performance,
• helps visualise predictive analytics models and forecasting results which enables businesses to anticipate future trends and plan their strategies proactively,
• assists in visualising customer data, preferences, and behaviours, allowing better understanding of customers,
• helps in monitoring and optimising operational processes,
• aids in resource allocation and risk management by presenting financial data, budgets, and risk indicators.

Shortly put, data visualisation is an integral part of the business strategy, enabling organisations to leverage data effectively, gain valuable insights, and stay agile in an increasingly data-driven business landscape.

Types of data visualisation

Data visualisation offers a wide range of techniques and visual representations to present data in meaningful and insightful ways.

Some common types of data visualisations include:

1. Bar Charts that use rectangular bars of varying lengths or heights to represent data values. They are effective for comparing discrete categories or displaying data over time.
2. Column Charts: similar to bar charts, column charts use vertical bars to represent data values. They are often used to compare categories or display data over time.
3. Line Charts which use lines to connect data points, making them ideal for showing trends and changes over time or continuous data.
4. Area Charts that are similar to line charts but fill the area beneath the line, often used to display cumulative data or compare multiple data series.
5. Pie Charts which divide a circle into segments, with each segment representing a portion of the whole. They are useful for displaying proportions or percentages.
6. Scatter Plots that use individual data points on a two-dimensional plane to visualise the relationship between two variables. They are helpful for identifying correlations or patterns in data.
7. Bubble Charts which are a variation of scatter plots with additional size or color encoding to represent a third variable. They help visualise three dimensions of data simultaneously.
8. Heatmaps which use colour variations to represent data density or distributions on a matrix, making them ideal for visualising patterns and trends in large datasets.
9. Choropleth Maps that use different shades or colours to represent data values for specific geographic regions, such as countries or states.
10. Tree Maps which use nested rectangles to represent hierarchical data structures, with each rectangle’s size proportional to the data it represents.

These are just some examples of data visualisations, and there are many more types and variations that can be used based on the specific data and insights you want to communicate. The choice of a visualisation type depends on the nature of the data, the story you want to tell, and the audience you are addressing.

Best tools for effective data visualisation

There are several excellent tools available for effective data visualisation, catering to different needs and skill levels. Here are some of the best tools widely used for creating impactful data visualisations:

1. Tableau – a popular and powerful data visualisation tool that offers a user-friendly interface, allowing users to create interactive dashboards, charts, and graphs with ease. It supports various data sources and is suitable for both beginners and advanced users.
2. Microsoft Power BI – a comprehensive business intelligence and data visualisation tool by Microsoft. It offers robust features for data exploration, interactive reports, and dashboards. Power BI integrates seamlessly with other Microsoft products and cloud services.
3. Google Data Studio – a free and cloud-based data visualisation tool by Google. It enables users to create interactive reports and dashboards using various data sources, including Google Analytics, Google Sheets, and more.
4. QlikView and Qlik Sense – data discovery and visualisation tools that empower users to explore data, uncover insights, and create visually appealing dashboards.
5. D3.js – a powerful JavaScript library for data visualisation, providing full control over the design and customisation of visualisations. It is suitable for developers who want to create highly customised and interactive visualisations.

How to choose the right data visualisation tool for your business?

The choice of a data visualisation tool depends on factors such as data complexity, desired level of interactivity, budget, and user expertise. Each of these tools brings its unique features and strengths to cater to different data visualisation needs.

When choosing the right tool remember about defining your requirements, consider user skill levels, assess data integration capabilities, think of scalability and performance, as well as of interactivity and customisation. Consider also your budget, integration with your existing systems, cybersecurity and recommendations of users.

The power of storytelling through data visualisation: examples

Data visualisation allows storytelling, which is a powerful way to communicate complex information, engage audiences, and make data-driven narratives more compelling. Here are some examples of data visualisations that effectively convey stories:

The New York Times’ “How Different Groups Spend Their Day”

This interactive data visualisation by The New York Times allows users to explore how various demographic groups in the United States spend their days. The visualisation uses stacked bar charts and line charts to show daily activity patterns, revealing interesting insights about how different groups allocate their time.

The Washington Post’s “How the Coronavirus Infected the World”

This data visualisation provides a timeline of how the COVID-19 pandemic spread across the globe. The interactive map and line charts show the progression of cases in different countries, offering a clear story of the virus’s impact on various regions.

The Guardian’s “The Wind Map”

This captivating data visualisation represents real-time wind patterns in the United States. The animated visualisation uses flowing lines to demonstrate the beauty and complexity of wind movements, turning data into a mesmerising visual story.

NASA’s Earth Observing System Data and Information System (EOSDIS) Worldview

EOSDIS Worldview is a web-based application that provides satellite imagery of Earth from various NASA missions. It allows users to create stories by selecting specific dates, regions, and events to explore environmental changes and natural disasters.

The role of data visualisation in big data and AI

Data visualisation plays a crucial role in the context of Big Data and Artificial Intelligence.

Big Data often involves massive volumes of structured and unstructured data. Data visualisation helps make sense of this complexity by presenting data in a visual format that is easier to understand, analyse, and derive insights from. It also contains hidden patterns, trends, and correlations that can be challenging to identify without the aid of visualisation.

In the realm of Big Data and AI, real-time data processing and analysis are essential. Data visualisation enables real-time monitoring of data streams and the performance of AI models, making it easier to detect anomalies and take timely actions. Those AI models, especially deep learning algorithms, can be complex and difficult to interpret.

Data visualisation can provide insights into how AI models arrive at their decisions, helping users understand the factors that influence the model’s predictions.

Another important role of data visualisation is communicating AI results to non-technical audiences, which bridges the gap between technical experts and non-technical stakeholders, allowing AI insights and results to be communicated effectively to decision-makers, business leaders, and other stakeholders.

The combination of Big Data, AI, and data visualisation creates a powerful framework for data-driven decision-making and innovation across various industries.

How to visualise data: the challenges and solutions

Visualising data effectively comes with its set of challenges, but there are solutions to address them. Here are some common challenges in data visualisation and their corresponding solutions:

Data Complexity:

The challenge here is that dealing with large and complex datasets can be overwhelming, making it difficult to identify meaningful patterns and insights. To address that issue use data aggregation, filtering, and summary techniques to simplify the data before visualisation. Employ interactive tools that allow users to explore the data dynamically, focusing on specific subsets of interest.

Choosing the Right Visualisation Type:

Selecting the most suitable visualisation type for the data and the story it needs to convey can be challenging. But you can do it right by understanding the characteristics of the data and the insights you want to communicate. Familiarise yourself with various visualisation types and their best use cases.

Experiment with different visualisations and seek feedback to determine which one effectively communicates your message.

Data Integrity and Quality:

Inaccurate or incomplete data can lead to misleading visualisations. To mitigate that, conduct data quality checks and preprocessing to ensure the accuracy and reliability of the data. Handle missing data appropriately and validate the data with domain experts when necessary.

Overloading Visuals with Information:

Including too much information in a single visualisation can overwhelm viewers and hinder understanding. To address that, practice data simplification and visual decluttering. Focus on the most critical data points and use annotations and storytelling techniques to guide the viewer through the visualisation.

Lack of Interactivity:

Static visualisations might not be sufficient for exploring complex data or answering specific questions. Utilise interactive features that allow users to interact with the data, enabling them to drill down, filter, and zoom in on specific data points of interest.

Choosing the Right Tools:

Selecting the right data visualisation tools that suit your needs and skill level can be overwhelming. To overcome that, evaluate different tools based on their features, ease of use, and compatibility with your data sources. Consider using a combination of tools if one tool alone doesn’t meet all your requirements.

By being aware of these challenges and implementing the corresponding solutions, you can create data visualisations that effectively communicate insights, engage audiences, and support data-driven decision-making.

Always remember that the goal of data visualisation is to simplify complex information and make it more accessible and understandable to your target audience.

Incorporating visual representations of data into your business strategy

Incorporating visual representations of data into your business strategy can significantly enhance decision-making, communication, and overall business performance. To do it effectively:

• determine the primary business objectives and key performance indicators (KPIs) critical to your organisation’s success,
• define data sources and metrics,
• choose visualisation types that best represent your data and facilitate understanding,
• create interactive dashboards that allow users to explore data and customise views based on their needs,
• incorporate data visualisations into reports, presentations, and business reviews to enhance the storytelling process.

Remember also about sharing data insights across departments to encourage cross-functional collaboration, monitoring and tracking performance and staying up-to-dated with new technologies.

By integrating data visualisation into your business strategy, you can unlock the value of your data, drive data-driven decision-making, and communicate insights more effectively. Visualisation enables you to turn complex data into actionable knowledge, empowering your organisation to stay competitive and responsive in a data-rich environment.

Keen to check it out within your organisation? Get in touch with our team of experienced data professionals who will be happy to take you through the process and advise on the best solutions and tools.