How do you solve data analysis gaps?

1 Identify the gap

The first step is to identify the gap and its root cause. What is the question or problem that you are trying to answer or solve? What data do you need to answer or solve it? What data do you have and what data do you lack? Why do you lack the data? Is it because it is not available, not accessible, not reliable, not relevant, or not compatible? By identifying the gap and its cause, you can narrow down your options and focus your efforts.

2 Explore alternatives

The next step is to explore alternatives to fill the gap. Depending on the cause and the context, you may have different options to obtain, generate, or approximate the data you need. For example, you may be able to use secondary sources, surveys, experiments, simulations, proxies, or assumptions to supplement or replace the missing data. However, each option has its own limitations, risks, and implications, so you need to evaluate them critically and compare them with your objectives, criteria, and constraints.

3 Validate the data

The third step is to validate the data you have or obtain. Validation means checking the quality, accuracy, reliability, and relevance of the data. You need to ensure that the data is consistent, complete, and correct, and that it matches your expectations, assumptions, and definitions. You also need to ensure that the data is suitable and sufficient for your purpose and scope. You can use various methods and tools to validate the data, such as cross-checking, cleaning, testing, or visualizing.

4 Analyze the data

The fourth step is to analyze the data you have or obtain. Analysis means applying techniques, methods, or models to extract insights, patterns, or trends from the data. You need to choose the appropriate analysis method or model for your question or problem, and apply it correctly and rigorously. You also need to interpret the results critically and cautiously, and consider the limitations, uncertainties, and biases that may affect them. You can use various methods and tools to analyze the data, such as statistics, algorithms, or frameworks.

5 Communicate the results

The final step is to communicate the results of your analysis. Communication means presenting and explaining your findings, conclusions, or recommendations to your audience. You need to choose the appropriate format, medium, and language for your communication, and tailor it to your audience's needs, expectations, and preferences. You also need to communicate clearly, concisely, and convincingly, and support your claims with evidence, logic, and examples. You can use various formats and media to communicate your results, such as reports, dashboards, or stories.

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?