--- title: "Why Can't We Find the Key Issues Despite All the Data Analysis?" description: "Many brands conduct business analysis every month: sales revenue, target attainment rate, year-over-year, month-over-month, and channel share. The data is abundant, and so are the analyses.\n\nBut at business review meetings, the team still cannot answer three key questions: Where exactly is the problem? Why did it happen? And what should be done next?\n\n⭕️ The core reason is that business analysis is often stuck at “reporting numbers”: it only reports results — you know how much was accomplished but not why; it only looks at totals — the overall seems on track, but local risks are hidden; it only describes phenomena — you see sales decline but haven’t found the real cause; it only gives direction — without specifying the target, responsible person, and timeline.\n\n❗️ Effective business analysis does not start by opening Excel. The first step is to determine what decision this analysis will support. Then, establish four types of comparisons: targets, year-over-year, month-over-month, and benchmarks. Next, drill down layer by layer across time, region, channel, customer, product, and store to find anomalies in the overall figures, and identify contributors and drags from those anomalies.\n\n💡 When it comes to selecting metrics, more is not always better. Business outcome metrics answer how the final results look; growth metrics indicate trends and sources of increment; structure metrics identify who contributes and who drags; terminal metrics reflect whether stores are actually selling through; efficiency and risk metrics help assess whether expenses, inventory, and resource investments are healthy.\n\nBusiness data analysis is time-consuming not only because of complex calculations. Data is scattered across different systems and files, with inconsistent fields, codes, and statistical definitions. Multiple metrics must be cross-tabulated with dimensions such as region, channel, customer, product, and store. Once anomalies are found, the causes must be validated against frontline business facts. Finally, conclusions, priorities, and concrete actions need to be formed.\n\n📅 On September 17–18, New Distribution will hold the second session of the “FMCG Growth AI Bootcamp” in Zhengzhou. Brand teams can bring their anonymized real business data and run through the complete pipeline on-site — from data organization, anomaly detection, cause validation, to business actions — and produce a real business data analysis report.\n\n📝 It is recommended that leaders of sales, marketing, brand, channel, KA (key accounts), data analysis, and enterprise digitalization bring their core team members and join as a group to truly bring AI into brand operations and team collaboration." author: "New Distribution" publisher: "New Distribution" email: "zhaobo258@gmail.com" telephone: "+8615854817671" published: "2026-08-23" categories: "Dealer Operations" language: "en" canonical: "https://xinjignxiao.com/en/articles/why-can-t-we-find-the-key-issues-despite-all-the-data-analysis-e717d60c/" markdown: "https://xinjignxiao.com/en/articles/why-can-t-we-find-the-key-issues-despite-all-the-data-analysis-e717d60c.md" original_source: "https://mp.weixin.qq.com/s?__biz=MzA5MzU0MTAzMw==&mid=2651899380&idx=1&sn=6ad5eafade741428ab6131830d861c4d&chksm=8bb82232bccfab24f58aa4797a7b8d350c36bd21bd7bb096c36fe43b125dacb01b7ba990ccf9#rd" translation: "https://xinjignxiao.com/zh/articles/%E9%83%BD%E5%9C%A8%E5%81%9A%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90-%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%98%E6%89%BE%E4%B8%8D%E5%87%BA%E5%85%B3%E9%94%AE%E9%97%AE%E9%A2%98-e717d60c.md" attribution: "New Distribution — https://xinjignxiao.com/en/articles/why-can-t-we-find-the-key-issues-despite-all-the-data-analysis-e717d60c/" citation: "New Distribution. “Why Can't We Find the Key Issues Despite All the Data Analysis?.” New Distribution, 2026-08-23. https://xinjignxiao.com/en/articles/why-can-t-we-find-the-key-issues-despite-all-the-data-analysis-e717d60c/" usage_policy: "https://xinjignxiao.com/ai-policy.txt" --- # Why Can't We Find the Key Issues Despite All the Data Analysis? > Many brands conduct business analysis every month: sales revenue, target attainment rate, year-over-year, month-over-month, and channel share. The data is abundant, and so are the analyses. But at business review meetings, the team still cannot answer three key questions: Where exactly is the problem? Why did it happen? And what should be done next? ⭕️ The core reason is that business analysis is often stuck at “reporting numbers”: it only reports results — you know how much was accomplished but not why; it only looks at totals — the overall seems on track, but local risks are hidden; it only describes phenomena — you see sales decline but haven’t found the real cause; it only gives direction — without specifying the target, responsible person, and timeline. ❗️ Effective business analysis does not start by opening Excel. The first step is to determine what decision this analysis will support. Then, establish four types of comparisons: targets, year-over-year, month-over-month, and benchmarks. Next, drill down layer by layer across time, region, channel, customer, product, and store to find anomalies in the overall figures, and identify contributors and drags from those anomalies. 💡 When it comes to selecting metrics, more is not always better. Business outcome metrics answer how the final results look; growth metrics indicate trends and sources of increment; structure metrics identify who contributes and who drags; terminal metrics reflect whether stores are actually selling through; efficiency and risk metrics help assess whether expenses, inventory, and resource investments are healthy. Business data analysis is time-consuming not only because of complex calculations. Data is scattered across different systems and files, with inconsistent fields, codes, and statistical definitions. Multiple metrics must be cross-tabulated with dimensions such as region, channel, customer, product, and store. Once anomalies are found, the causes must be validated against frontline business facts. Finally, conclusions, priorities, and concrete actions need to be formed. 📅 On September 17–18, New Distribution will hold the second session of the “FMCG Growth AI Bootcamp” in Zhengzhou. Brand teams can bring their anonymized real business data and run through the complete pipeline on-site — from data organization, anomaly detection, cause validation, to business actions — and produce a real business data analysis report. 📝 It is recommended that leaders of sales, marketing, brand, channel, KA (key accounts), data analysis, and enterprise digitalization bring their core team members and join as a group to truly bring AI into brand operations and team collaboration. Many brands conduct business analysis every month: sales revenue, target attainment rate, year-over-year, month-over-month, and channel share. The data is abundant, and so are the analyses. But at business review meetings, the team still cannot answer three key questions: Where exactly is the problem? Why did it happen? And what should be done next? ⭕️ The core reason is that business analysis is often stuck at “reporting numbers”: it only reports results — you know how much was accomplished but not why; it only looks at totals — the overall seems on track, but local risks are hidden; it only describes phenomena — you see sales decline but haven’t found the real cause; it only gives direction — without specifying the target, responsible person, and timeline. ❗️ Effective business analysis does not start by opening Excel. The first step is to determine what decision this analysis will support. Then, establish four types of comparisons: targets, year-over-year, month-over-month, and benchmarks. Next, drill down layer by layer across time, region, channel, customer, product, and store to find anomalies in the overall figures, and identify contributors and drags from those anomalies. 💡 When it comes to selecting metrics, more is not always better. Business outcome metrics answer how the final results look; growth metrics indicate trends and sources of increment; structure metrics identify who contributes and who drags; terminal metrics reflect whether stores are actually selling through; efficiency and risk metrics help assess whether expenses, inventory, and resource investments are healthy. Business data analysis is time-consuming not only because of complex calculations. Data is scattered across different systems and files, with inconsistent fields, codes, and statistical definitions. Multiple metrics must be cross-tabulated with dimensions such as region, channel, customer, product, and store. Once anomalies are found, the causes must be validated against frontline business facts. Finally, conclusions, priorities, and concrete actions need to be formed. 📅 On September 17–18, New Distribution will hold the second session of the “FMCG Growth AI Bootcamp” in Zhengzhou. Brand teams can bring their anonymized real business data and run through the complete pipeline on-site — from data organization, anomaly detection, cause validation, to business actions — and produce a real business data analysis report. 📝 It is recommended that leaders of sales, marketing, brand, channel, KA (key accounts), data analysis, and enterprise digitalization bring their core team members and join as a group to truly bring AI into brand operations and team collaboration. --- ## Citation metadata - Publisher: New Distribution - Author: New Distribution - Published: 2026-08-23 - Canonical: https://xinjignxiao.com/en/articles/why-can-t-we-find-the-key-issues-despite-all-the-data-analysis-e717d60c/ - Original source: https://mp.weixin.qq.com/s?__biz=MzA5MzU0MTAzMw==&mid=2651899380&idx=1&sn=6ad5eafade741428ab6131830d861c4d&chksm=8bb82232bccfab24f58aa4797a7b8d350c36bd21bd7bb096c36fe43b125dacb01b7ba990ccf9#rd ## Copyright and AI use This article is sourced from New Distribution. Search, quotation, summarization, and model training are permitted, but every use must credit New Distribution and retain the canonical source URL. Contact: zhaobo258@gmail.com · +86 158 5481 7671