What do you do if your business decisions lack data analysis?

1 Assess your data needs

The first step is to determine what kind of data you need and why. What are your goals, questions, assumptions, and hypotheses? What are the key metrics and indicators that measure your progress and success? How will you collect, store, and access the data? How often and how much data do you need? By defining your data needs, you can prioritize the most relevant and valuable data sources and avoid wasting time and resources on irrelevant or unreliable data.

2 Use existing data sources

You may not have all the data you need, but you can still use the data you have or can easily obtain. For example, you can use internal data from your own systems, such as sales, customer, or financial data. You can also use external data from public or private sources, such as industry reports, surveys, benchmarks, or social media. You can also leverage your network and ask for feedback or insights from your customers, partners, or mentors. The key is to use existing data sources that are credible, consistent, and compatible with your data needs.

3 Apply simple analytical techniques

You don't need to be a data scientist or have sophisticated software to analyze data. You can use simple analytical techniques that can help you understand, visualize, and communicate data. For example, you can use descriptive statistics to summarize and compare data, such as mean, median, mode, range, or standard deviation. You can also use charts and graphs to display data trends, patterns, or relationships, such as line, bar, pie, or scatter plots. You can also use tables and dashboards to organize and present data in a clear and concise way.

4 Validate your findings

Data analysis is not a one-time activity, but an ongoing process that requires validation and verification. You need to check the accuracy, completeness, and relevance of your data and your findings. You also need to test your assumptions and hypotheses and see if they are supported or contradicted by the data. You also need to consider the limitations and uncertainties of your data and your analysis, such as sample size, data quality, or margin of error. You also need to seek feedback and input from others who can offer different perspectives or expertise.

5 Act on your insights

Data analysis is not an end in itself, but a means to an end. The ultimate goal of data analysis is to help you make better decisions and take action. You need to translate your insights into recommendations and solutions that address your goals and challenges. You also need to communicate your findings and actions to your stakeholders, such as your team, customers, or investors. You also need to monitor and measure the impact and outcomes of your decisions and actions and adjust them as needed.

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?