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What do you do if your senior role in Data Visualization requires top problem solving skills?

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As you ascend to a senior role in Data Visualization, the complexity of the problems you face increases. You're expected to transform raw data into compelling visual stories that can drive decision-making. This requires not only technical expertise in tools like Tableau or Python's Matplotlib but also top-notch problem-solving skills. Whether you're dealing with data inconsistencies, integrating new data sources, or creating visuals for diverse audiences, your ability to think critically and creatively is key. So, what do you do when your role demands these high-level skills?

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Ahmed El-Shamy

?? GenAI and?? RegTech Aficionado?? ?? Quantitative Fraud & Business Intelligence Expert ?? ?? Reinventing Financial…
Jagruti Divse

Sr. Data Analyst

1 Embrace Challenges

When faced with a complex data visualization problem, your first step is to embrace the challenge. Understand that encountering obstacles is a natural part of working with data. Begin by breaking down the problem into smaller, manageable parts. This makes it easier to identify the root cause and potential solutions. Remember, each challenge is an opportunity to learn and grow in your role. By adopting a positive mindset, you can turn daunting tasks into rewarding experiences.

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Jagruti Divse

Sr. Data Analyst
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Clearly define the challenge you're trying to address through data visualization. This might involve understanding stakeholder requirements, project goals, and constraints.Communicate with stakeholders to gather requirements, expectations, and desired outcomes. Understand the context in which the visualization will be used and who the audience is. Determine what data sources are available and relevant to solving the problem. Assess the quality, completeness, and reliability of the data.Use analytical techniques like data cleaning, transformation, aggregation, and statistical analysis to derive meaningful insights.Select appropriate visualization techniques based on the characteristics of the data and the objectives of the analysis.

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Neha Yadav

Associate Manager @ Annalect | Data Analytics | Data Engineering | Python, ML & AL, Power BI, Tableau, Redshift, SQL, Alteryx
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You must have expertise on advance tool like Tableau, Power BI. Build up creative and critical thinking skills. Train yourself to analyze trends, patterns and insights from data. Make yourself comfortable with machine learning

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2 Analyze Data

Thorough data analysis is crucial. Start by ensuring the data's accuracy and completeness. Scrutinize the data sets for anomalies or patterns that could affect your visualizations. Sometimes, the issue isn't with the data itself but with how it's being interpreted or processed. By taking a step back and analyzing the data from different angles, you might discover new insights or identify errors that were previously overlooked.

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Ahmed El-Shamy

?? GenAI and?? RegTech Aficionado?? ?? Quantitative Fraud & Business Intelligence Expert ?? ?? Reinventing Financial Education ?? ?? AI & ML Specialist ?? ?????? Instructor ?? ?? Author
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???? Start with a rigorous data cleaning phase, ensuring your foundation is solid. ???? Employ statistical analysis to identify trends, outliers, and correlations. This step is critical for ensuring the data you present is reliable and tells an accurate story. ???? Visual exploratory analysis can be immensely helpful. Use basic visualization tools to see the data from different perspectives, often revealing insights you might not have noticed through traditional analysis methods alone, especially using tools like Heat Maps, etc.

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3 Leverage Expertise

Don't hesitate to leverage the expertise of others. Collaboration can provide fresh perspectives and innovative solutions to difficult problems. Reach out to colleagues who have faced similar challenges or have specialized knowledge that can aid in your problem-solving process. Additionally, staying current with industry trends and new visualization techniques can provide you with alternative methods and tools to tackle your challenges.

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Padmapiyush Pathak

Trainee at Idemia | Google Data Analytics Certified
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Teamwork makes the dream work! Don't be afraid to tap into the brainpower of your colleagues. Working together can bring new ideas and creative solutions to the table. Reach out to folks who've faced similar hurdles or have specific knowledge that can help you crack the problem. Plus, staying on top of industry trends and fresh visualization techniques equips you with even more tools and methods to conquer any challenge.

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4 Iterate Designs

Iterative design is a powerful approach in data visualization. Create initial drafts of your visualizations and seek feedback from stakeholders. This feedback loop allows you to refine your designs and ensure they effectively communicate the intended message. With each iteration, you'll likely encounter new problems to solve, which further hones your problem-solving skills.

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5 Focus on Storytelling

Effective data visualization is all about storytelling. Your visuals should convey a clear and compelling narrative. This means understanding the audience and tailoring your visualizations to their needs and expectations. If you're facing a problem, ask yourself whether the data story is coherent and engaging. Sometimes re-framing the narrative or changing the visualization technique can resolve issues and improve comprehension.

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6 Utilize Tools

Lastly, make the most of the data visualization tools at your disposal. Whether it's a sophisticated software package or a simple spreadsheet application, knowing how to leverage these tools' advanced features can help you solve complex problems more efficiently. If you encounter a hurdle, explore whether there's a feature or plugin that can help you overcome it. Often, the solution lies in understanding and utilizing your tools to their fullest potential.

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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?

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Ahmed El-Shamy

?? GenAI and?? RegTech Aficionado?? ?? Quantitative Fraud & Business Intelligence Expert ?? ?? Reinventing Financial Education ?? ?? AI & ML Specialist ?? ?????? Instructor ?? ?? Author
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The field of data visualization is ever evolving, with new challenges emerging as technology advances and datasets grow in size and complexity. Commit to lifelong learning, seeking out educational opportunities, workshops, and courses that can enhance your skills. Attend industry conferences and participate in forums where you can exchange ideas and learn from others' experiences. The journey of mastering data visualization is continuous, filled with opportunities for growth, innovation, and discovery.

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What do you do if your senior role in Data Visualization requires top problem solving skills?

1

2

3

4

5

6

7

1 Embrace Challenges

2 Analyze Data

3 Leverage Expertise

4 Iterate Designs

5 Focus on Storytelling

6 Utilize Tools

7 Here’s what else to consider

Data Visualization

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What do you do if your senior role in Data Visualization requires top problem solving skills?

1

2

3

4

5

6

7

1 Embrace Challenges

2 Analyze Data

3 Leverage Expertise

4 Iterate Designs

5 Focus on Storytelling

6 Utilize Tools

7 Here’s what else to consider

Data Visualization

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