By gathering data from multiple sources such as sales, orders, expenses, receipts, and deliveries, a data analysis system can identify patterns and trends, uncover insights, and inform decision-making. The more data sources that are included in the analysis, the more complete and reliable the results will be.

However, it’s also important to ensure that the data being collected is of high quality and relevance to the business objectives.

Apart from obvious quantitative data (sales revenue, expenses, etc.), we should also gather qualitative data such as new customer acquisition, sales by product category, process lead time, etc.

Qualitative data will enable us to develop key performance indicators that measure the real performance of the business and answer more complex questions.

The data collection system should be integrated into the daily operations of the business without affecting it. Most of the data comes from the daily functioning of the business, and where possible, it will be collected automatically.

In cases of qualitative data that require extra recording work, they will be made as easy as possible.

Our data will be used for meaningful statistical analysis rather than accounting purposes.

Therefore, if a metric that requires additional effort can be reliably estimated through sampling, that method will be preferred in order to better utilize the resources of the business.

The collected data will be available at any time, in its raw form, within the company.

The individuals collecting the data will be aware of the purpose for which it is intended, in order to be involved and carry out the process with greater responsibility.

To build a data analysis system, we start with the results – the information – that we want it to provide us and for what purpose we need that information.

This means identifying the key questions we want to answer, the problems we want to solve, or the opportunities we want to pursue through data analysis. Once we have a clear understanding of what we want to achieve, we can determine what data we need to collect, how to collect it, and how to analyze it to extract meaningful insights.

This process requires careful planning, attention to detail, and a focus on the business objectives to ensure that the data analysis system delivers actionable results that drive business value.

The presentation of data and primary analysis is done in real-time and is available to the business owner.

This helps facilitate minor adjustments to the model and provides meaningful information on a daily basis.

For data analysis and presentation, I use a business intelligence platform (Microsoft Power BI or Google Looker Studio) which offers extensive analysis and presentation capabilities mainly in the form of dashboards.

For more complex cases or for summary reports (e.g. monthly reports), I use the R language, which allows us to incorporate the results of the analysis into text files, which contain estimates, explanations, etc.