BeforeJapanese businesses using Power BI or Looker Studio are operating dashboards with a January calendar year default, meaning year-to-date and year-on-year comparisons are misaligned with the April-to-March Japanese fiscal year. A Japanese business reviewing performance in February is in month eleven of the fiscal year, but a calendar-year dashboard shows it as month two. All trend analysis requires manual recalculation to align with the fiscal year, and budget versus actuals comparisons are impossible to read directly from the dashboard without adjustment. This is not a minor formatting issue — it makes every period comparison in the dashboard unreliable for a Japanese management audience.
AfterMonthly 取締役会 report compilation is replaced by an automated dashboard export. The two-to-three day manual compilation process is replaced by a scheduled Power BI export that arrives in the right format, with correct Japanese labels and number notation, on the scheduled report date.
BeforeJapanese dashboards displaying metric labels and category names in English or romanised Japanese are unsuitable for presentation to senior Japanese management. The expectation for 取締役会 reporting and formal management dashboards is Japanese-language labels, Japanese date formatting (using either the 令和 era system or standard Japanese date format with 年月日), and Japanese large number formatting using 万 (10,000) and 億 (100,000,000) notation rather than Western comma-separated thousands. A dashboard displaying ¥125,000,000 instead of 1億2500万円 is formatted incorrectly for a Japanese board audience, regardless of the accuracy of the underlying data. Configuring Japanese number formatting in Power BI requires custom DAX measures and format strings that are not available in the default localisation settings.
AfterJapanese fiscal year comparisons are accurate in every chart and table without manual recalculation. YTD, prior year, and budget versus actuals figures align to the April-March fiscal year across the entire dashboard; management stops correcting calendar-year figures before presenting to the board.
BeforeJapanese mid-size businesses with core operational data in kintone are unable to connect kintone directly to Power BI or Tableau using a native certified connector. kintone's API provides access to app data via REST API calls, but building a reliable, scheduled connection to Power BI requires a custom API connector built in Power Query M code or a middleware integration layer. Without this connector, data is extracted manually from kintone each week — downloaded as CSV, pasted into a staging spreadsheet, and then refreshed into the dashboard. This manual extraction step introduces errors, creates a reporting lag, and means the dashboard is only as current as the last manual export, typically days or weeks old at the time of any management review.
Afterkintone data is live in the dashboard without manual CSV exports. The weekly data extraction task is replaced by a scheduled API refresh; operations data is current to the last refresh cycle rather than days old at the time of any management review.
BeforeJapanese e-commerce sellers managing three separate platforms — Rakuten Ichiba, Amazon Japan, and Yahoo Shopping — are operating without a unified view of channel performance. Each platform provides its own seller analytics dashboard, but none of them shows how the three channels compare against each other, what the blended GMV and net margin look like after platform fees, or which products are performing across all three channels simultaneously. Sellers are manually downloading reports from each platform and combining them in Excel to produce a monthly channel comparison — a process that takes several hours per month and produces a static snapshot rather than a live view. Without a unified dashboard, decisions about platform fee negotiation, product listing prioritisation, and promotional budget allocation are made without access to cross-channel data.
AfterCross-channel e-commerce performance is visible in a single dashboard for the first time. Rakuten, Amazon Japan, and Yahoo Shopping GMV, fee comparison, and product-level performance are combined in one view; channel allocation and promotional investment decisions are based on blended margin data rather than platform-level gross revenue.