How do you organize, visualize, and analyze data for problem-solving?

I became a Certified Quality Technician thru ASQ so my job was to mainly perform different studies throughout our plant. One was Process Capability on different Machines. 100 consecutive pieces without any adjustments from the operators. Measure the key characteristic and account for its variation. All of our parts are round. Now because of so many types of data terms used could be normal and fine with other products. But when my boss asked me to throw out a reading because it was a “flyer”. A random fluke. Throwing the study off. I was new but I refused. I said you want it out you rekey it 99 pieces instead of 100. You can’t make a solid decision if you don’t prepare for those unexpected things that could become regular things.

Every time I tackled a problem with a dataset, a significant portion of my time was devoted to cleaning and making sense of the data. While it may have taken up most of the allocated time, ensuring accurate and meaningful data was essential to lay the right foundation for my analysis. Data cleaning is a critical step that sets the stage for impactful insights.

To analyze and visualize data properly, first organize it in a structured format like spreadsheets or databases. Choose file types based on usage - spreadsheets for frequent manipulation, databases for large complex data. Clean the data by fixing errors, removing duplicates, standardizing values. Add metadata like column headers to document the data. Establish naming conventions and folder structures to easily locate files later. Store data securely in a central location like a network drive or cloud storage. Use data management systems like SQL for very large datasets. Record processing steps to enable reproducibility. Well-organized, documented and structured data from the start makes downstream analysis and visualization much simpler.

Cleaning and validating data is a very important step when organising data. This ensures accuracy and completeness of your data. Once someone identifies errors in your data, they may tend to doubt the whole report.

There are great free resources of data. One of them is Kaggle. It's an open source data that you can find different type of data. Not only that, Kaggle has a community of data analysts who are giving reports and analysis on different subjects.

I agree. However, some visualizations can become complicated. I feel those are for the Analysts and Engineers working together to figure out which would be the best ones to use for meetings. A collaboration between all 4 groups. Analyst and Engineers present to Management and Customers. Conclusion key characteristics, processes, cost, …Then there is the Quality Control Department. They deal with operators and the products. If charting is implemented and we had different departments. That meant different charts. For operators that same quote comes back. Keep it simple. But always remind them. Control your Variation and you can improve you process.

In my experience before visualization you need to analyze your audience, understand who they are to make appropriate and usable visualizations

As an analyst, selecting the right visualization to communicate insights often depends on the stakeholders' preferences. To effectively convey our findings through visualizations, it's crucial to empathize with the stakeholders and understand their perspective. By putting ourselves in their shoes, we can craft visualizations that resonate with them and deliver a compelling 3rd perspective of our analysis.

You might need to put yourself in place of your audience to know how would they understand your chart and visualization of your data. A beneficial tip is that you can use the feedback of one of your colleagues to help you in making your presentation.

Here are some additional tips for visualizing data for problem-solving: Keep it simple. The goal of visualization is to communicate your findings in a way that is easy to understand. Avoid using too many colors, fonts, or patterns. Use labels. Make sure your visualizations are labeled clearly so that your audience can understand what they are looking at. Use context. Don't just show your data in isolation. Put it in context by showing it alongside other data or by explaining what it means. Test your visualizations. Show your visualizations to a few people before you share them with a wider audience. Get their feedback and make changes as needed.

Make sure you understand what the end result should be. There are a ton of people who just gather and analyze as much data as possible, but that might not be the most efficient use of your time. Instead it is important to only analyze the important parts of the data for your business case.

It is always important through out the analysis journey to relate your analysis to your target or main subject. So just keep that in mind to be more focused and effective.

Keep your data limited and pertinent to your subject matter. Too many people go off on a tangent without any reason to go there. As an electrical and electronic tech for over 40 years, it didn't take long to recognize what is important and what is not to get you to the facts and solve the problem. Whether you work on paper or with your hands and knowledge as I do the basics are always the same.

Descriptive analysis: This type of analysis is used to summarize data and describe its characteristics. It can be used to answer questions like What happened? or What is the distribution of this data? Explanatory analysis: This type of analysis is used to understand why something happened. It can be used to answer questions like Why did this happen? or What factors influenced this outcome? Predictive analysis: This type of analysis is used to forecast future events. It can be used to answer questions like What will happen? or What is the probability of this event happening? Prescriptive analysis: It's used to recommend actions. It can be used to answer questions like What should we do? or What is the best course of action?

In my experience it’s very important for analysts to ask right questions, ask as many as needed for understanding needs, problems and solution. In my own it is one of most important part.

I believe every effective problem-solving process starts with asking the right questions. The quality of our questions determine the quality of our solutions. When it comes to resolving organisational problems, the easiest way to frame the initial understanding is in my opinion - the gap analysis. 3 simple questions: 1. What are your business goals? 2. What are your top challenges? 3. How are your challenges preventing you from reaching your goals? Just by understanding the big picture, we can already narrow down on the specific problems to tackle!

It's important to keep you and your stakeholders (your boss for example) on the same level of understanding. This is to avoid re-doing the job due to miss alignment.

This part could be part of some of the analysis. Especially if the data is reproducible. So just keep that in mind if you're going to build a model predicting a results.

Relevance is key. It may be a really good report but if the results and recommendations are not solving a problem for the stakeholders, it may have been a waste of time.

Organizing, visualizing, and analyzing data for problem-solving is pivotal in any decision making activity. Start by categorizing data into meaningful segments. Use visualization tools like Gantt charts, flowcharts, or mind maps for clarity. Data analytics aids in identifying trends and anomalies. Emphasize collaboration; diverse perspectives enrich understanding. Periodically reassess data organization to ensure alignment with evolving project goals. Effective data management is key to informed decision-making.

Organizing, visualizing, and analyzing data is crucial for effective problem solving. Begin by structuring data using spreadsheets or databases, ensuring consistent formats. Create visualizations like charts or graphs to identify patterns, trends, and outliers. Tools like Excel, Tableau, or Python libraries (matplotlib, seaborn) can help. For analysis, employ descriptive stats, regression, or clustering to derive insights. Break down complex problems into smaller parts, focusing on relevant data. Iterate between visualization and analysis to refine your understanding. Communication of findings through clear visuals and concise explanations is vital for impactful problem solving.