Why is colour important in data visualisation?

Colour is important in data visualisation because it can help to convey information and enhance the overall impact and understanding of the data being presented.

It is important to use colour effectively in data visualisation to ensure that it supports, rather than detracts from, the overall message of the data. When using colour, it is important to choose colours that are easily distinguishable and have appropriate contrasts, and to use colour consistently throughout the visualisation to maintain clarity and consistency.

How to choose best colours for data visualisations

Whichever colour scheme you select, ensure that it’s consistent throughout. This will support clarity and make it easier for your viewer to understand the data. In terms of how to choose the best colours, here are a few approaches to consider.

• Analyse collected data insights

By analysing the data insights, you will be able to identify patterns and relationships between the variables included. You can then highlight certain patterns or emphasise the relationships using specific colours, perhaps choosing colours that best represent the patterns.

• Evaluate other visualisations

Consider what you think works, or doesn’t, in other visualisations you have seen when it comes to the colour selection. Finding examples that reflect your data could also be helpful in assisting you with your colour choices.

•  Highlight important data points

If you want to draw attention to particular sections of data or specific points, consider using a colour that is in high contrast to the rest of your selection so it will stand out. 

Types of colour palette

The following palette types are worth considering when selecting colours for your data.

• Qualitative palette

This palette is specifically for categorical data where the emphasis is on the distinction between categories rather than on quantitative differences. Qualitative palettes use a limited number of colours with high saturation and good contrast. This ensures each category is easily distinguishable from the next. 

• Sequential palette

Sequential palette’s use a range of colours that change smoothly from one colour to another to represent a progression of values. This palette is often used to show a continuous variable that has a clear ordering, such as temperature, rainfall, or population density. The colour palette is chosen such that each colour represents a different range of values, with the first colour representing the lowest values and the last colour representing the highest values. For example, with temperature, blue to represent cooler temperatures and red the warmer.

• Diverging colour palette

A typical diverging colour palette has a neutral colour in the middle, such as white or grey, that represents the baseline or reference value. The colours on either side of the neutral colour represent values that deviate from the reference value, with one colour representing values above the reference and the other colour representing values below the reference. The change from one colour to the next is usually gradual, allowing the viewer to easily see the progression of values represented by the data.

Diverging colour palettes are useful for showing the spread of data around a central value and for highlighting the difference between two opposite values. They are often used in data visualisations such as heat maps and bar charts.

Tools for using colours

It’s well worth checking out these tools for supporting your colour selection process.

• ColorBrewer

Providing pre-designed colour palettes for a wide range of data visualisation types including sequential and diverging palettes. Designed to be colour-blind friendly, ColorBrewer is widely used by data scientists, cartographers and designers – a popular resource for creating effective and accessible data visualisations. 

• Color Thief

A small library for grabbing the dominant colour or a representative colour palette from an existing image is the central focus of Color Thief. It was created for web developers who need to extract colours from images to use in their designs. The tool analyses the pixels of an image and finds the most frequently occuring colours, then returns the dominant colour palette of the image. 

• Viz Palette

Viz Palette helps to create colour palettes based on colour theory, or provides predefined colour palettes for use in visualisations. This tool can ensure colour consistency across different visualisations and presentations, making it easier to compare and interpret data. 

What is data governance?

Data Governance is the set of processes, policies, standards, and roles responsible for managing data throughout its lifecycle in an organisation. It ensures that data is managed and used in an appropriate, consistent, and efficient manner, which is critical for making informed business decisions.

In recent years due to the growing volume of data generated by organisations (for mind-blowing reference, predictions estimate the world will generate 181 zettabytes of data by 2025!) data governance has become increasingly important. This data can be used to make informed decisions, but only if it is of high quality and managed appropriately. Data Governance helps to ensure that data is consistent, reliable, and secure, which is essential for making effective decisions.

What are the benefits of data governance?

• Improved quality of data

By establishing a set of policies, procedures and standards for managing data, accuracy, consistency and reliability of data can be improved. 

• A common understanding of data

Data governance can help ensure that everyone within an organisation is using the same definitions and standards for data. This can reduce the risk of confusion, errors and misinterpretation.

• Increased trust in data

Ensuring that data is properly managed and protected and used in an ethical and compliant manner will increase user trust in it.

• Improved data management

Organisations are able to better manage their data, reduce duplication and generally make it more efficient. 

• Consistent compliance

Data governance can assist organisations comply with regulations, regulators and any clients that require proper management of sensitive information. 

Data governance in the cloud

As with any data in an organisation, processes and practices for data management regardless of its location are required if you want to embrace quality data governance. Benefits of properly implementing data governance in a cloud computing environment include:

• easy scalability for data storage requirements
• improved security and privacy
• cost savings
• increased collaboration and accessibility  

It’s important to note that Data Governance in the cloud is not a one-time process, but rather a continuous effort to maintain and improve the management of data. Organisations must regularly review and update their policies and procedures to ensure they align with changes in technology, regulations, and business needs.

Who’s responsible for data governance?

It’s vital that everyone in an organisation has a clear understanding of what data governance is and their role within it. The following key people are generally responsible for data governance:

• Chief data officer (CDO)

A CDO is responsible for overseeing the data governance strategy for the organisation and ensuring that company data is managed, protected and used in compliance with legal and ethical standards.

• Data owners

Individuals or departments within a company that have control over specific types of data and are responsible for defining and implementing policies for its use are data owners. 

• Data stewards

Individuals responsible for maintaining policies established by data owners are sometimes referred to as data stewards. Their role is to ensure the data is properly classified, stored and maintained and that data access and usage is consistent with company policy.

Types of data governance tools

The following tools will assist in creating sound structures for your company data:

• Data cataloguing

A central repository of information about the organisation’s data assets is known as the data catalogue. It includes a running record of the data, its definition, structure and usage.

• Visualisation

This tool helps organisations better understand and manage their data assets by presenting data in a graphic format. Data becomes more user-friendly in this format and can enable opportunities to be identified for improvement in data quality, security and compliance. 

• Data lineage

Data lineage provides a complete and accurate record of the history and movement of data from its origin to its current state. This tool helps organisations to understand how data is transformed, moved, and used over time and to track the relationships between different data assets. 

• Threat detection

Organisations can identify potential security threats to their data assets and prevent unauthorised access or misuse of sensitive data. Automated tools and processes that monitor and  analyse data access patterns and trigger alerts in the event of any suspect activity.