Dashboard
Dashboard
What is a dashboard?
- See key metrics on one screen
- Status at a glance
- Output of BI
What a dashboard actually changes is not the volume of data but the nature of the meeting. In organizations without one, the people responsible spend all their time producing numbers, and the meeting ends up checking whether those numbers are right. Once a single screen fixes the current picture, that verification step disappears, and the time left goes to 'why did this happen and what will we do about it.' If a report is a document that explains last month, a dashboard is a screen that helps you decide what to fix this week.
Dashboards split three ways depending on who is using them. Executive dashboards show only about 10 metrics at a monthly or quarterly level. Analytical ones need a drill-down structure so you can dig in by product and channel. Operational ones depend on minute-level refresh and threshold alerts. Realistically, a single screen without scrolling holds 7–9 metrics; add more and people end up looking at none of them. Before fixating on 'real-time,' ask whether you can act the same day when that metric changes. Refreshing a metric every 5 minutes when a daily refresh would do just raises system costs.
Dashboards most often break down on metric definitions. If Sales books revenue when a deal is closed-won and Finance books it when the tax invoice is issued, a dashboard just starts arguments between departments faster. Dashboards that get built and then ignored are common, too. Check the access log three months after launch and you can count the weekly users on one hand, because nobody has decided what to do after looking at the screen. A good-looking screen is not the same as a useful dashboard. If you haven’t set who does what when a metric turns red, the dashboard ends up as wall decoration.
Jaryong KooMarketing PhD and authority in AI, data analytics, and personal branding
John KimAuthor of B2B Marketing Design and CEO of Nanum ConsultingThe name comes from the dashboard of a car, and the tool became a standard part of management as BI and data analysis tools matured.
Let’s look at a hypothetical case: Company A, a mid-sized food manufacturer with 320 employees and KRW 78 billion (≈ US$58,357,000) in annual revenue. Every Monday, three people on the corporate planning team spent two full days preparing the executive meeting deck. Because the numbers that sales, production, and logistics each submitted in Excel did not match, half of the meeting time went to 'which number is right?' At the CEO’s direction, the company launched a four-month project to build an operations dashboard.
- Kim, the corporate planning manager, re-read six months of meeting minutes and found that the items executives actually asked about came to just nine: revenue, inventory turnover days, shipping delays, claim rate, and so on.
- When the complaint that 'revenue differs by department' kept coming up, the head of finance and the head of sales sat down together, standardized revenue on a tax-invoice issuance basis, and settled it by signing a single metric definition sheet.
- The first screen carries only 9 executive metrics. When the team needs to dig into root causes, one click drills down to screens by product line and by customer. It’s a two-level structure.
- The head of the logistics center asked to be alerted the same day if shipping delays exceed 20 a day, so we connected an alert to the logistics team’s chat channel that fires when the threshold is crossed.
- Three months after launch, the access log showed 3 metrics that had never been opened once. We removed them and put in their place the per-line utilization rate that the floor had asked for.
- Meeting prep dropped from two days to half a day, and the 16 hours a week spent compiling data fell to 2. Average response time for shipping delays fell from 3 days to 1.