How do you manage data overload in your analysis?

Working with data can sometimes lead to information overload, especially when dealing with large datasets or complex analyses. In such situations, it's essential to go back to basics and revisit your research question. Take a step back and assess if your initial question is still manageable within the scope of your research timeline. Refining your research question to something more focused and achievable will help you regain clarity and avoid getting overwhelmed. By narrowing down your research question, you can prioritize specific variables, streamline your data collection and analysis processes, and ensure that your findings align with your overall goals and objectives.

Firstly decide the main goal and objectives of your research analysis. This will help you focus on collecting and analyzing only the data that is relevant to your objectives, reducing the risk of overload.

To handle data overload in analysis, employ systematic strategies. Define clear objectives and scope. Employ data preprocessing to clean and filter irrelevant data. Leverage sampling techniques for representative subsets. Use dimensionality reduction methods like PCA. Utilize automated tools for repetitive tasks. Focus on key metrics aligned with goals. Employ visualization for insights. Regularly refine your approach based on analysis outcomes, prioritizing quality over quantity.

Having a clear and specific research question is very important as it will allow you to formulate the problem better. A general problem formulation can be of form, predict y given set of variables x1,x2,x3,x4. Eg:- Predict the income of a person given his location, gender, nationality, and education. 1. Allow you to filter the sources you should consider for the data 2. Allow you to filter specific variables within those sources 3. Make sure that you are not taking any biasing data (Eg:- You should not use tax data to predict income)

Unanalyzed data is worthless! Knowing the end users of the data is very important. Critical to provide them with the information they require in the format that they prefer. Also, be careful not to bring conclusions to the table that are untrue or not supported by the data. Accurate data is invaluable! Organize information so it can be easily retrieved by all parties making decisions.

An often untapped data source that can contain many hidden insights is qualitative data. Most organizations have a considerable?amount of data in text format,?including reports, stories, interviews or focus groups, that go unanalyzed. By developing?thematic categories of interest and tagging these data sources (i.e., thematic qualitative coding), and?aggregating trends, new insights can arise-- leading to more data-driven actions.

The best tool to use for organizing data is a spreadsheet (You can also use pandas if you can code). Also, you should organize the data in terms of the variables you want to predict/model (Which are the output) and the variables you are using as input.

With data, we can bring light through a topic or create big lies. Keep it clear, easy to access, and include all the assumptions and sources to support them. Remember your initial goal and present the relevant numbers that support it, but remember that it should be possible to trace back and arrive at the same conclusions.

Filtering and cleaning your data will likely be 80% of the overall work effort for any data analysis. Any statistician, data scientist, machine learning engineer, or analyst will tell you this.

Filtering and standardizing your data is vital to make sure there are no outliers, and everything is in the same format. A few examples are: 1. Convert the age of everyone to numbers 2. Make sure the incomes are in same currency etc.

Visualizing your data will save you from a lot of analytical traps. Data sets can have the same values (mean, median, SD, range, etc.) but have completely different underlying distributions. You won't see that story unless you plot the data. Refer to Anscombe's quartet and the Datasaurus Dozen for an example.

A few of the methods to visualize your data depending on it's complexity are: 1. Graphs - You can use simple spreadsheet graphs 2. A random forest visualization 3. PCA.

I have only used SPSS, which I have found useful. But this point ?? is so important. You need to be aware of whether your data is suitable for the statistical test. For example, is your data the right type of data (categorical a, scale etc) and have you met the assumptions of the test? As SPSS will run the test regardless of whether the assumptions have been met or not. That is your job as the researcher to ensure it is suitable to use. If you don't check this then the results you obtain may not be reliable or even correct.

I would highly recommend using scikit learn for this. In fact you can use random forest or SVD visualization and they will give you really good idea of what variables are most important.

SAS should also probably be included in the list of software data analysis tools. Julia and Stata probably earn an honorable mention too.

At this point, having an external view from someone who is knowledgeable and will not be afraid to criticize the work is essential. Don't be cold towards the person even if their feedback is negative, just thank them for the input and proceed to evaluate if it makes sense. It is better to get feedback at this stage, even if you are tired, than later on getting negative feedback from peer reviewers or your client.

Focus on having a solid hypothesis aligned with quality versus quantity. Too much information is an excellent problem, but low-quality data from questionable sources or incomplete data sets is a headache.

Tell stories to share your data findings. Stories make data more engaging and memorable. Start by introducing the problem you solved, then describe your data and how you analyzed it. Finally, share your findings and conclusions. Here is an example: Problem: A company wants to increase sales. Story: The company collected data on customer demographics, purchase history, and product preferences. They found that customers are more likely to buy products that are recommended to them and are on sale. So, they implemented a recommendation system and offered more sales. Stories help you communicate the most important insights from your data in a clear and concise way.