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How can you create easy-to-update data visualizations for your team?

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Data visualizations are powerful tools to communicate insights, trends, and patterns from your data. But creating and updating them can be time-consuming and tedious, especially if you have to deal with multiple sources, formats, and platforms. How can you make your data visualizations more efficient and flexible for your team? Here are some tips and techniques to help you.

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1 Choose the right tool

The first step is to choose a tool that suits your needs and preferences. There are many options available, from spreadsheet applications like Excel or Google Sheets, to specialized software like Tableau or Power BI, to programming languages like Python or R. Each tool has its own advantages and disadvantages, such as ease of use, customization, interactivity, scalability, and cost. You should consider factors such as the type and size of your data, the complexity and frequency of your analysis, the level of collaboration and sharing, and the output format and style.

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Alex Ooi

Business Analytics, Data Engineering
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Depending on the complexity of visualisation, I would prefer to "blend" my tools accordingly. I am using Microsoft Office solution but I'm sure there are other synonymous options available too. 1. Create and deploy Power BI data model to a workspace 2. Connect Excel to the Power BI data model 3. Create custom charts using data from Power BI 4. Link the charts to PowerPoint slides Now these can be easily and quickly updated whenever the Power BI datasets are refreshed.

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Lokesh Tendulkar

LinkedIn Top Data Engineering Voice | Founder and CEO at Certification Champs | Ex-Amazon | 4K+ Students on Udemy
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Tool selection for creating easily updatable data visualizations is crucial. Opt for user-friendly platforms like Tableau, Power BI, or Google Data Studio based on your team's familiarity, requirements, and ease of use. Consider factors such as intuitive interfaces, diverse visualization options, scalability with data sources, and the ability to automate data refreshes. These tools offer drag-and-drop functionalities, pre-built templates, and interactive features, enabling efficient creation of visuals without extensive technical expertise. Evaluate the tool's capabilities in handling different data types, and supporting collaborative work within your team. The chosen tool should align with your team's proficiency.

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Omkar Metri

Associate at MSG Sphere Entertainment | Seeking January Co-op & May Full-Time Opportunities | Formerly at Cisco & Lam Research
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Depending on requirements and resources, below is the concise summary of tools: 1. Using Code: - R: Statistical computing language. - Shiny: Web app framework for R. - ggplot2: R's data visualization. - matplotlib: Python's 2D plotting. - LaTeX: Typesetting for scientific docs. - MATLAB: Numerical computing language. 2. Third-Party Data-Viz Software: - Tableau: Powerful data visualization. - Salesforce: CRM platform with viz. - PowerBI: Microsoft's BI service. 3. Data Science Software: - Weka: ML algorithms for data mining. - KNIME: Open-source data analytics. 4. Online Tools: META-CHART, datavisual 5. Geospatial: PostGIS, MongoDB

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Hasan Javaid Malik

Analytics @ Jazz
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The most important & crucial step is deciding the tool to use. In my experience, there would be 100s of tools out there with rocket-science logic but the community of PowerBI & Tableau is unparalleled, so, I think if you want visualisation that’s easy to update and which can support99.9% of your KPIs and use-cases you can choose either PowerBI or Tableau, you simply can’t go wrong with any of these two. ??????

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Hasan Javaid Malik

Analytics @ Jazz
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The most important & crucial step is deciding the tool to use. In my experience, there would be 100s of tools out there with rocket-science logic but the community of PowerBI & Tableau is unparalleled, so, I think if you want visualisation that’s easy to update and which can support99.9% of your KPIs and use-cases you can choose either PowerBI or Tableau, you simply can’t go wrong with any of these two. ??????

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2 Automate the data pipeline

The second step is to automate the data pipeline that feeds your visualizations. This means creating a process that can collect, clean, transform, and load your data from various sources into a centralized location, such as a database or a cloud storage. This way, you can avoid manual errors, ensure data quality and consistency, and save time and resources. You can use tools like Airflow, Luigi, or Prefect to orchestrate your data pipeline workflows, and tools like SQL, Pandas, or Spark to manipulate your data.

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Jessica E.

Senior Data Engineer at Bradesco
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The first step is to analyze the volume, variety, and velocity of the data that will be worked on. For smaller datasets, flexible tools such as Python, Pandas, and Airflow can be used to create custom pipelines and meet diverse needs. For larger datasets, scalable technologies such as Python, Spark, and Airflow are recommended. These tools are efficient at processing large amounts of data, ensuring that pipelines run efficiently. Once the data is dimensioned, it is important to choose a storage solution. Popular solutions such as Amazon S3, Azure Data Lake Storage, and Google Cloud Storage offer reliable features for storing data.

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Omkar Metri

Associate at MSG Sphere Entertainment | Seeking January Co-op & May Full-Time Opportunities | Formerly at Cisco & Lam Research
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- Use real-time processing engine like Apache Storm/Spark to implement data processing logic (clean, aggregate or transform incoming data). - Coming to persisting the data, choose a database optimized for real-time data storage and faster access such as memcached key-value DBs (Redis, Dynamo DB, Cassandra).

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3 Use templates and standards

The third step is to use templates and standards for your visualizations. This means creating and applying a consistent set of rules and guidelines for your design, layout, color, font, legend, axis, label, and annotation. This way, you can enhance the clarity, readability, and aesthetics of your visualizations, and make them easier to update and maintain. You can use tools like Style Sheets, Themes, or Dashboards to create and apply your templates and standards.

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4 Leverage dynamic features

The fourth step is to leverage dynamic features for your visualizations. This means using functionalities that can make your visualizations more interactive, responsive, and adaptable to different scenarios and audiences. For example, you can use filters, sliders, buttons, or drop-down menus to allow users to select and explore different subsets of data. You can also use parameters, calculations, or expressions to enable users to change and compare different variables or metrics. You can also use conditional formatting, alerts, or triggers to highlight and notify important changes or anomalies in your data.

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5 Document and communicate

The fifth step is to document and communicate your visualizations. This means creating and providing clear and concise explanations and interpretations of your data, your analysis, and your findings. You should also include metadata, such as the source, date, and frequency of your data, the assumptions and limitations of your analysis, and the recommendations and actions based on your results. You should also choose the best way to share and present your visualizations, such as reports, dashboards, slides, or web pages.

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6 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?

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Amol Kokate

Programme Director @ TCS | Driving Data Innovation for Telecom Growth | INSEAD | Design Thinking | Mentor |Data & Analytics |
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I can think of below 1. Don't do extensive coding in dashboard 2. Include a separate section for Glossary or user guide 3. Incorporate last data refresh date/time 4. Delta data refresh 5. use version control tool

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Omkar Metri

Associate at MSG Sphere Entertainment | Seeking January Co-op & May Full-Time Opportunities | Formerly at Cisco & Lam Research
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Another important component of data-viz is Data Storytelling, transforming data/viz into a meaningful and relatable story. The key idea is stories are more memorable than raw data/visualisation’s, making it more likely that the audience will retain and remember important information.

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How can you create easy-to-update data visualizations for your team?

1

2

3

4

5

6

1 Choose the right tool

2 Automate the data pipeline

3 Use templates and standards

4 Leverage dynamic features

5 Document and communicate

6 Here’s what else to consider

Data Engineering

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How can you create easy-to-update data visualizations for your team?

1

2

3

4

5

6

1 Choose the right tool

2 Automate the data pipeline

3 Use templates and standards

4 Leverage dynamic features

5 Document and communicate

6 Here’s what else to consider

Data Engineering

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