When working with a dataset, it can reveal many stories. I make sure I focus on what matters, the problem I am solving. With an outcome-based mindset, I start to make sense of the data.

New Concepts

I have developed a solid foundation of business acumen, having worked with a broad range of stakeholders and problems. When approaching an unfamiliar problem, I use this foundation to quickly understand new concepts and adopt industry specific knowledge. This allows me to reduce time to value on new skill sets, meet stakeholder expectations, and ensure my analysis is relevant.

I collaborate with stakeholders and colleagues early in the process to draw out further valuable viewpoints and knowledge I may not already have.

Analyse

With a firm understanding of the outcome and business context, I begin to uncover what the data is telling me. This includes creating calculations (aggregating the data), discovering relationships, and identifying trends. With this knowledge, I can then confidently communicate both the data and findings to my stakeholders.

Problem Solving

Every problem is different and may require a technical implementation I am unfamiliar with. This is when I use my well developed problem solving skills and start asking relevant questions to identify potential solutions.

One of the great aspects of data is the amazing community of fellow enthusiasts. They have a passion for learning and sharing knowledge. Having access to this public ecosystem of people and resources to help with problem solving is exciting and helps strengthen what I can do with data.

The culmination of the analysis process, and the step that's the most rewarding, being able to communicate what the data means.

Visualisation

I use visual representations of data to distill complex information into something that is easy to understand. While there are endless ways of displaying data, the fundamentals stay the same. I strive to display data in a compelling and simple way. This means:

• choosing the right visual

• reducing clutter (keep focus on the data)

• grouping visuals (highlight relationships)

• orientating elements (create a logical easy to read flow)

See my portfolio on the Home page for a sample of how I use these principles to communicate the meaning and context of data.

Choosing a Visual

With an array of options, which visual do I use to tell the right story? It starts with the type of data I am presenting (quantity, changes over time, categorical comparisons, relationships etc.) Then, while considering the problem I am solving and understanding what my stakeholders want to know, I choose the appropriate visual.

For more on how I select visuals, this guide prepared by numerro gives a great summary.

Feedback

Developing visualisations is an iterative process where I not only continue to ask myself questions, I collaborate with colleagues and stakeholders to seek their feedback. This helps me focus on my audience and incorporate new ideas I may not have considered.

Dynamic Content

I believe people should have access to the right information and business intelligence tools. This enables them to continuously drive their understanding of data and become proactive in making better business decisions. Therefore, I develop tools that embed dynamic elements, allowing people to derive specific insights tailored to their needs.

However, this can introduce an element of risk that the data may be misinterpreted. To mitigate this, I include the below features to ensure users have a better understanding of the story.

Information Guide and Glossary

An Information Guide provides key notes about data context and guidance on how to read the visualisations, ensuring the best use of the tool and information.

The Glossary provides definitions for terms used in the tool. This helps users understand terms they are unfamiliar with, including how they are used in context of the analysis.

Titles and Visual Annotations

I provide clear and concise titles and annotations in context of a visual, this helps reinforce what the visual is communicating.

Tooltips

Where appropriate, I provide further related information using tooltips. This allows me to maintain a balance between summarising the meaning of the data and incorporating further detailed information.

See my portfolio on the Home page for a sample of how I use the above features to communicate the meaning and context of data.

Meaningful data in context can spark change and help people meet their goals. Presenting the meaning of data and ensuring the audience understands the context, can help ensure the best decisions are made.

Presentation

I generally have two approaches when presenting, Familiarisation and Analysis Summary.

Familiarisation

This is where I walk my stakeholders through the tool and its story. This allows me to communicate context, purpose and use. This helps my stakeholders become proactive in their data driven decision making.

Analysis Summary

While visual contextual information and guides help communicate the analysis, it may be appropriate that I present my analysis and findings. My stakeholders may not be as familiar with the data and may require a summary of the key insights.

The information presented will include:

• what data is available (set expectations on what questions can be answered)

• the problem we are trying to solve (context)

• the result of the analysis (insights using visualisations)

• supporting details (the data that supports the insight)

• how the insight solves the problem (why it's important)

If you would like to discuss job opportunities and how I can help you to deliver, I can be contacted through LinkedIn.

For a summary of the value and contributions I have made throughout my career, my resume is available on request.