BI · Business Intelligence
Business Intelligence
What is BI?
- Visualizing data as dashboards and reports
- Supports data-driven decision-making
- Treating the dashboard as the end product
The first thing BI changes is the meeting. The report that someone spent all night compiling in Excel every Monday morning disappears, and attendees walk into the room having already looked at the same screen. The argument shifts from 'is the number right?' to 'why did this happen?' That is the change BI actually delivers. The three days once spent building the report go back into interpreting the data and deciding what to do, and the habit of numbers being quietly polished as they move up the reporting line fades along with it.
BI usually falls into three types: standardized reports that run in the same format every week, self-service analysis where practitioners change filters and ask their own questions, and embedded BI that places metrics inside your own service or a customer portal. BI covers only 'what happened and where'; collecting and cleaning the data upstream is the job of the data warehouse, and prediction and recommendation downstream belong to analytical models. Blur the two and you get the misplaced complaint, 'We installed BI, so why can't it predict anything?' What decides whether adoption succeeds is not screen design but whether metric definitions are managed in one place. If there is no one-line agreement on whether 'revenue' includes VAT or excludes cancellations and returns, then no matter how polished the dashboard is, the meeting goes right back to verifying numbers.
The most common misconception in the field is that buying a tool gives you BI. It is not unusual for a company to buy 50 licenses and have only five or six weekly active users. A dashboard crammed with 40 to 50 metrics leaves people unsure where to look, so in the end no one opens it. And when each department builds its own similar screens, the same item shows different numbers, and trust in the data itself collapses first. Before adding more screens, attach a rule: 'when this number crosses its baseline, who does what.' A dashboard nobody watches is inventory that only costs money to maintain.
Jaryong KooMarketing PhD and authority in AI, data analytics, and personal branding
John KimAuthor of B2B Marketing Design and CEO of Nanum ConsultingThe term was used in a 1958 paper by IBM's Hans Peter Luhn, and since the 1990s it has become the standard for corporate analytics alongside data warehouses and visualization tools.
Consider a hypothetical case. Company A, a Korean household goods manufacturer (620 employees, KRW 180 billion (≈ US$134,670,100) in annual revenue), only saw the results of its 350 dealerships nationwide 8 days after each month-end close. While the sales division stitched together 47 Excel files by hand, inventory decisions were already a beat late, and second-quarter returns rose 21% from the previous quarter. Management said, 'Let’s buy a BI tool first,' but the sales planning team raised a more basic problem.
- When Deputy Manager Kim of the sales planning team went through 47 files, she found that 'revenue' was defined differently by each team: three teams included VAT and two deducted returns. So the first two months went not into screens but into aligning 12 metric definitions.
- While the IT team was loading ERP, logistics, and CRM data into the warehouse in weekly batches, the head of sales put his foot down: 'I will only look at three numbers.' That cut the initial dashboard metrics from 38 to 9.
- The weekly Monday 9 a.m. sales meeting now runs off a single dashboard screen. When the Central Region branch head asked, 'Why is inventory turnover in our region only 1.8?', the team drilled down by item on the spot and had the answer in 5 minutes: three specific SKUs were the cause.
- In June, when the five dealers with the highest return rates were highlighted in red, the merchandiser in charge visited them in person and fixed their display and ordering standards. In the meeting, someone said, 'With the numbers in plain sight, there are no excuses.'
- Nine months later, the time to see results had moved up from 8 days after month-end close to 9 a.m. the next day, the return rate had fallen from 4.7% to 2.9%, and the monthly report that had tied up three people took just 4 hours a week.