How do you use data visualization to communicate your maintenance findings and recommendations?

2 How to choose the right data visualization tool for maintenance engineering

When selecting a data visualization tool for maintenance engineering, there are a few factors to consider, such as the type and format of your data sources, the purpose and audience of your data visualization, the level of interactivity and customization you need, the budget and time constraints you have, and the compatibility and integration with other software and platforms. It is also important to evaluate the tool's usability, functionality, performance, and support. Excel, Power BI, Tableau, and Qlik are some of the most popular data visualization tools available in the market. You can compare different options by reading online reviews, ratings, watching demos, and trying out trials to find the best fit for your needs.

3 How to prepare your data for visualization

Before you start creating your data visualization, you need to prepare your data for analysis and presentation. This involves cleaning, organizing, and transforming your data to make it suitable for visualization. To do this, you should check for data quality issues such as missing values, duplicates, errors, and outliers. Additionally, you should standardize and normalize your data to ensure consistency and accuracy. Classifying and categorizing your data can help you group similar items and identify patterns. Furthermore, aggregating and summarizing your data can reduce complexity and highlight key metrics. Joining and merging your data from different sources can create a unified view. You can use various tools and techniques to prepare your data such as formulas, functions, filters, pivot tables, and queries. It is important to document your data preparation process as well as keep track of any changes you make.

4 How to select the best data visualization type for maintenance engineering

When selecting the best data visualization type for maintenance engineering, you should consider the message you want to convey, the level of detail and complexity, the main variables and dimensions, how to compare and contrast your data, and how to engage and interact with your audience. Additionally, it's important to follow some general best practices for choosing data visualization types. These include using simple and familiar types that match your audience's expectations, appropriate scales, axes, labels, and legends to make it easy to read and interpret, consistent colors, fonts, and styles to make it appealing and professional, minimal clutter and noise to keep it clear and focused, and multiple types to show different aspects of your data. Examples of data visualization types are bar charts, pie charts, line charts, scatter plots, histograms, heat maps, and dashboards.

5 How to evaluate and improve your data visualization

Once you have created your data visualization, you need to evaluate and improve it to make sure it meets your objectives and standards. You can use various methods and criteria to evaluate and improve your data visualization, such as testing it with your target audience and collecting their feedback, reviewing it with peers and experts, comparing it to similar or competing ones, and checking its accuracy, validity, reliability, and consistency. After refining your data visualization based on the results of the evaluation and improvement process, you should also monitor and update it regularly to keep it relevant and current.

6 How to present and share your data visualization

The final step of using data visualization to communicate your maintenance findings and recommendations is to present and share it with your intended audience. You can use various formats and channels, such as reports, presentations, websites, blogs, social media, and emails. Additionally, you should tailor your data visualization to suit the needs and preferences of your audience. A clear title and description should introduce your data visualization while a logical structure should organize it. Furthermore, stories and narratives can connect your data visualization with your audience's emotions and values. Lastly, questions and prompts can engage your audience and invite their participation.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?