Improve Your Organisations Data Literacy, Now!

An essential component of analytics that enables communication of results is data visualisation literacy. The following stages can help an organisation become more data visualization literate.

Business analysis requires organisations to swiftly make sense of vast amounts of data, but proper data visualisation literacy increases the effectiveness and speed of these data-driven decisions.

Data visualization literacy includes a range of competencies for effectively communicating one's ideas as well as knowing how to read and comprehend visuals.

Employees that are proficient in data visualisation can also learn to distinguish between stylish graphics and the data's true commercial worth.

What is Data Literacy?

A project has already told a chunk of the tale when it reaches the visualisation stage. Understanding the use case is essential to developing data visualisation literacy because the goal of data visualization is to provide quick answers to important questions.

Data literacy and data visualisation can be seen as complementary disciplines that require an understanding of where the information came from, why it is collected, and how it is used.

The accuracy of the story being communicated visually can be checked with data visualisation literacy. The two complementing abilities of data presentation and data exploration are typically referred to as data visualisation literacy.

Data presentation abilities aid in the visualisation of outcomes when KPIs or other important measures generate a summary of business data. Data exploration abilities make it easier to visually examine unexplored data in order to comprehend statistics and correlation.

Data Literacy vs. Data Visualization Literacy

Understanding the broader realm of data collecting, storage, and decision-making procedures is a requirement for data literacy. In order to create more useful charts, one must have a basic understanding of data visualisation. Understanding the benefits and drawbacks of each sort of chart, as well as how to format and decorate them are a necessary for skill.

Data visualisation literacy is also necessary for consumers to appropriately read charts and assess their reliability.

By utilising visuals to express the analysis, data visualisation capabilities make it easier to find answers to questions from a set of queries and they make it possible for users to swiftly comprehend information by allowing them to view visualisations like charts, graphs, dashboards, or animated graphics. This enables individuals and teams to create a coherent narrative across various but connected facts in order to accelerate the speed at which superior data-driven decisions are made.

This includes knowledge of various data sources, how data pipelines are made, various analytical methods, and methods for data transformation. For an organisation to make successful and efficient data-driven decisions that advance the company's goals, it must be competent in these areas.

Here are a set of actions businesses can take to encourage data visualization literacy within the company.

Use a Consistent Visual Language

Enterprises should adopt a consistent use of visual language, f or instance, a certain shape can always convey a particular idea. As a result, messages can be more clearly understood more quickly.

Additionally, linking the visualisation to the data directly can aid in grouping the information into easily recognisable and understandable categories. It is simpler to evaluate complicated data connections through streamlined graphics when there is a suitable visual language with direct linkages to the data.

Understand Your User Base

Success depends on knowing the users' identities, whether they would utilise the same data, and how they will interact with it.

Here are some recommended practises for matching consumers' needs with data visualization techniques:

• Learn more about the users and how they plan to use the data. Recognize the needs of the target market and the business.
• Multiple pages of data should be used. The best methods for data visualization are keeping the page brief. Having a lot of information in one area won't assist you keep your attention on what you need to know.
• Discover the relationships between various sorts of data. Filters can influence how different charts are related to one another. Whether and how to group the data can be decided by the needs of the users.
• Apply accessible font and colour conventions. Make sure that the data is simple for persons with disabilities to read.
• Start with a mobile-first strategy if the audience is primarily using mobile devices, and then expand it to desktop.

Identify the Business Narrative

How data is graphically presented and how the story is communicated need to be very carefully considered. Think about how data is visualised and the types of charts that are used.

Data visualisation literacy requires you to have a basic awareness of the business context and your target audience.

For example, someone might show information when only two data points are tracked over time completely differently than they would depict a broad population and sectors within that population. A straightforward line chart works well when there are just two data points.

A combination of line charts, bubble charts, and bar charts may be used to tie demographics including age, race, occupation, and income to a business-relevant statistic when visualizing a general population.

The ideal way to relate the data presentation to a potential commercial component would depend on one's knowledge of data visualization, such as finding distinct population segments that spend more on various product lines or categories.

Establish a Feedback Loop

Similar to how authors of books modify their language for the intended audience, data visualisation creators bear the bulk of the responsibility for adapting their visualization. Consumers must be able to understand the data presentation.

Efficient data analysis and visualization feedback loops can increase everyone's abilities to produce and consume visualisations.

Analysts must comprehend what has to be done and offer a preliminary data analysis design, including the visualization during the requirements collection phase. The stakeholder can then gain knowledge of the techniques, offer suggestions, and understand how to evaluate the data. Based on the response, the analyst can then modify the amount of complexity.

Teams should also identify any individual or organisational data visualisation literacy gaps. By learning more about data analysis and visualisation methods and tools or by creating impactful data visualizations for sharing insights from various types of data, people can then take action to close any skill or knowledge gaps.

Discover the most appropriate chart types to use and when to use them, and discover the distinction between a bar chart and a scatter plot.

Get Creative and Have Fun

Data visualization is both an art and a science. To understand how to use a data visualisation tool, we advise locating datasets that are personally relevant as data that is interesting to you will encourage you to follow explore the data and look for trends or correlation.

Taking into account how charts are presented in the media is also beneficial. Are they being truthful or deceiving? How differently could the facts be displayed? Also, when charts are used in presentations at work, consider if they are useful or distracting as well as the reasons behind each.

Be Considerate Towards Culture, Procedure, and Technology

Technology, procedure, and cultural knowledge are necessary for data visualisation literacy. Growing and democratising data-driven insights depends on getting the proper individuals access to analytical technology. Repeatable procedures provide uniformity, standardisation, and governance, giving teams a curated path to becoming more perceptive when using the visualizations and insights from analytic platforms.

The right structure for education, cooperation, skill development, and funding is created in part by culture.

Data analytics value is delivered by combining data literacy and data visualisation literacy.

All of the data in existence can be used to create stunning visualizations. However, the value is quickly gone if the user isn't data literate and lacks the ability to effectively interpret and express these insights to drive action.