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Good analysis follows a repeatable path. This data analysis workflow works whether you use Excel, Google Sheets or MATLAB. Need help with data analysis workflow? Message Senthil Kumar on WhatsApp: +91-9952749533 1. Define the question State what decision the analysis will support. 'Why did downtime increase last quarter?' is a better starting point than 'analyse the maintenance data'. 2. Gather and clean Collect all relevant data, merge files and fix errors, duplicates and gaps. Keep a copy of the original. 3. Explore Look at summaries, ranges, trends and distributions. Plot the data. Exploration often reveals problems and ideas you did not expect. 4. Analyse Apply suitable methods such as averages, comparisons, correlations, regression or time-series analysis. Match the method to the question and the data. 5. Visualise Choose charts that make the answer obvious. Keep them simple, labelled and consistent. 6. Report and recommend State the main find...

Good analysis follows a repeatable path. This data analysis workflow works whether you use Excel, Google Sheets or MATLAB. Need help with data analysis workflow? Message Senthil Kumar on WhatsApp: +91-9952749533 1. Define the question State what decision the analysis will support. 'Why did downtime increase last quarter?' is a better starting point than 'analyse the maintenance data'. 2. Gather and clean Collect all relevant data, merge files and fix errors, duplicates and gaps. Keep a copy of the original. 3. Explore Look at summaries, ranges, trends and distributions. Plot the data. Exploration often reveals problems and ideas you did not expect. 4. Analyse Apply suitable methods such as averages, comparisons, correlations, regression or time-series analysis. Match the method to the question and the data. 5. Visualise Choose charts that make the answer obvious. Keep them simple, labelled and consistent. 6. Report and recommend State the main find...

Strong conclusions need strong data. Careful data collection determines how reliable your analysis will be, so it deserves a plan before the first reading is taken. Need help with data collection? Message Senthil Kumar on WhatsApp: +91-9952749533 Common data collection methods Instruments, sensors and data loggers Field measurements and inspection records Online surveys and questionnaires Public databases and published reports Company records, invoices and maintenance logs Interviews and observation Plan before you collect Define the question, the variables needed, the units, the sampling interval or sample size, and who is responsible. Prepare a standard template so every entry follows the same format. Keep the data reliable Calibrate instruments and record the date. Use consistent naming and units. Record the date, time and conditions of each reading. Back up the raw files immediately. Never overwrite raw data; clean a copy instead. Respect privacy and permissions Whe...

Strong conclusions need strong data. Careful data collection determines how reliable your analysis will be, so it deserves a plan before the first reading is taken. Need help with data collection? Message Senthil Kumar on WhatsApp: +91-9952749533 Common data collection methods Instruments, sensors and data loggers Field measurements and inspection records Online surveys and questionnaires Public databases and published reports Company records, invoices and maintenance logs Interviews and observation Plan before you collect Define the question, the variables needed, the units, the sampling interval or sample size, and who is responsible. Prepare a standard template so every entry follows the same format. Keep the data reliable Calibrate instruments and record the date. Use consistent naming and units. Record the date, time and conditions of each reading. Back up the raw files immediately. Never overwrite raw data; clean a copy instead. Respect privacy and permissions Whe...