Business activity analysis isn't something you jump into blindly. There's groundwork involved—understanding your data landscape, knowing what questions to ask, and honestly assessing where your organization stands today.
We've worked with over 130 companies across South Korea since 2022. The ones who get value fastest? They show up prepared. Not perfect, just ready to engage with the process.
This page walks through what that preparation looks like in practice.
Different starting points require different approaches. Here's how various organizational states map to realistic outcomes and timelines.
| Current State | Data Availability | Team Readiness | Typical Timeline | Initial Focus Area |
|---|---|---|---|---|
| Early Stage Startup | Limited historical data, mostly operational metrics | 1-2 people handling everything | 3-4 months to establish baseline | Cash flow patterns, expense categorization |
| Growing Business | 12+ months of transaction records, some reporting | Finance person or small team in place | 6-8 weeks to operational insights | Revenue trends, cost structure optimization |
| Established Company | Multiple years, various systems, some integration | Finance department with defined processes | 4-6 weeks to advanced analysis | Profitability drivers, strategic scenario modeling |
| Enterprise Division | Comprehensive but siloed across departments | Multiple stakeholders, complex approvals | 8-12 weeks including alignment phase | Cross-functional efficiency, resource allocation |
You'll spend a week or two just figuring out what financial data exists, where it lives, and who can access it. This sounds basic but it trips up about half the companies we talk to. Spreadsheets on someone's laptop don't count as a data system.
What do you actually want to know about your business? "Everything" isn't an answer. Real questions look like: "Which product lines generated positive cash flow last quarter?" or "What's our customer acquisition cost trend over 18 months?" Specificity matters here.
Get everyone who'll use the analysis in a room. Finance, operations, maybe sales leadership. They need to agree on definitions—what counts as revenue recognition, how you categorize expenses, when a customer is considered "active." These conversations take time but prevent months of confusion later.
Your data has errors. Everyone's does. Duplicate entries, miscategorized transactions, formatting inconsistencies. Budget time for someone on your team to work through the obvious problems before formal analysis starts. A Gyeonggi manufacturing client spent three weeks just reconciling vendor names across systems.
How will insights actually change decisions? If analysis shows your Seoul location is underperforming, who has authority to act on that? What's the approval process? Understanding this upfront means results don't sit in a report gathering dust.
I've been doing this work since 2018, mostly with mid-size companies in the Seoul metropolitan area. The mistakes people make before starting are predictable—and avoidable if you know what to watch for.
First mistake: assuming your accounting system is analysis-ready. It's not. Accounting systems are built for compliance and reporting, not for answering strategic questions. You'll need to transform that data, and transformation requires understanding both the source system and what you're trying to learn.
A logistics company came to us in early 2024 with three years of "clean" financial data. Took us two days to find that their revenue recognition timing had changed twice during that period without documentation. The data was accurate for tax purposes but told a misleading story about growth patterns.
Second issue: not involving operations people early enough. Finance teams know the numbers, but operations teams know what actually happened in the business. When you see an expense spike in Q3 2024, operations can tell you that's when you moved warehouses. That context changes how you interpret everything.
Third problem: unrealistic scope. You can't analyze every aspect of your business simultaneously. Pick two or three priority areas and go deep. A Dongan-gu retail client wanted to understand profitability by product, by location, by customer segment, and by sales channel—all in the first month. We talked them down to product profitability first, then expanded from there.
And honestly? Sometimes the preparation phase reveals you're not ready yet. Maybe your data is too fragmented, or you don't have buy-in from key stakeholders. That's okay. Better to know that now than six weeks into a project that goes nowhere.
These are tools and frameworks we share with clients during the preparation phase. Nothing revolutionary—just practical starting points that work.
A structured checklist for inventorying your financial data sources. Covers transaction records, reports, manual spreadsheets, and third-party integrations. Takes about 3 hours to complete thoroughly.
How to translate business concerns into analytical questions. Includes examples from real projects and a worksheet for prioritizing what matters most to your specific situation.
Identify who needs to be involved, what they care about, and how they'll use analysis results. Helps prevent misalignment and ensures insights lead to actual decisions.
Self-assessment to determine if you're ready to start or need more preparation. Covers data quality, team capacity, timeline expectations, and organizational support.
