What Should a Data Setup Cost a Business Your Size?
Your numbers should be telling you more than they do. When you go looking for a straight answer, it is scattered across your accounting tool, your POS, a CRM, and a few spreadsheets, and none of them line up. So you go with your gut, which has carried the business this far. The information to do better is already there. It has just never been joined up, and the quote you got to fix that was built for a company ten times your size.
In short: Most SMEs do not need an enterprise data platform. They need the data they already have joined into one trusted view, the few manual jobs that eat the most hours automated, and someone to make sure it gets used. Expect that to cost a fraction of an enterprise build (USD 25,000 to 75,000 and up, often six figures all in once you add the analyst hire): a jinq audit to scope the work is from SGD 4,000, and building and running it is from SGD 7,500 a month. The full platform is the right call only at a scale most SMEs are nowhere near.
Why can't you get a straight answer from your own numbers?
Because the answer lives in pieces, in systems that piled up over the years and were never meant to talk to each other. Sales sit in the POS, money in the accounting tool, customers in a CRM or a notebook, stock in a spreadsheet. Each was added to solve one problem, by different people, at different times.
So when you ask a simple question, "which products actually make money, after discounts and returns," nobody can answer it without an afternoon of exporting and reconciling. By the time the number arrives it is stale, and you have already made the decision on instinct. This is the daily reality for most businesses your size, and it comes down to wiring: the systems were never built to share.
Why does every quote to fix it come back so big?
Because you called the names you have heard of, and the solution they sell is an enterprise data platform. The standard pitch has four parts:
- A data warehouse to centralise everything.
- BI and dashboard tooling on top.
- A data pipeline layer to move and clean the data.
- Months of consulting to build and configure it all.
That is a real product. It is also scoped for a company with dozens of analysts and millions of rows, which is why the number is what it is. A straightforward build runs around USD 25,000 to 75,000, and a complex migration from legacy systems climbs into the hundreds of thousands (ScienceSoft, data warehouse pricing, retrieved 2026-06-16). Then to run it, you are expected to hire: a single data analyst in Singapore averages around SGD 60,000 a year and SGD 90,000 or more once experienced (talent.com, retrieved 2026-06-16).
What should a data setup actually cost a business your size?
Far less, because you are solving a smaller, sharper problem. You do not have millions of rows or a team of analysts waiting for a platform. You have a handful of systems that need to share one set of numbers, and a few reports you want without the afternoon of exporting.
Here is the honest comparison.
| The enterprise quote | What a business your size needs | |
|---|---|---|
| Goal | A platform for analysts at scale | One trusted view of the data you already have |
| Build | Data warehouse + pipelines + BI suite | An integration layer connecting your current tools |
| Cost | USD 25,000 to 75,000+, often six figures all in | A fraction of that, scoped to your systems |
| To run it | Hire an analyst (SGD 60,000 to 90,000+/yr) | Existing staff, with someone embedded part-time |
| Time to value | Months of consulting first | Weeks, starting with the highest-pain report |
The enterprise tools work fine. You are being quoted for a scale you do not operate at.
Why is that platform built for a company ten times your size?
Because it has to be, for the customers it was designed for. Enterprise platforms assume large data volumes, a dedicated team to drive them, and a budget to match. Drop that into a 30-person company and two things happen. You pay for capacity you will never use, and nobody internally has the time or training to run it.
That is how the expensive part of this goes wrong. Across the market, about 67 percent of software features go unused, and enterprises wrote off more than USD 104 million on underused technology in 2024 (WalkMe, reported via StockTitan, retrieved 2026-06-16). Large implementations fare no better: roughly 70 percent of ERP projects fail to meet their goals (Concord ERP, retrieved 2026-06-16). The tool is bought, the invoice is paid, and the platform sits half-used. For an SME, that is the real risk, more than the sticker price.
What would actually help a business your size?
Three things, in order, none of which require an enterprise platform.
- Join the data you already have. An integration layer connects the tools you already pay for so they share one set of numbers, without rebuilding anything. We walked through this for retailers in One Source of Truth for Retail Data, and the same approach fits any business with scattered systems.
- Automate the few jobs eating the most hours. Not everything. The three to five manual processes that quietly cost a day a week each: the reconciliation, the report assembly, the copy-paste between systems.
- Make sure it gets used. This is the step the big quote skips. Someone embedded one to two days a week to build it, train your team, and keep it running is what turns a tool into a habit. That is the job a Fractional AI Officer does, and it is why the right-sized path gets adopted when the platform does not.
This is the same logic behind why SMEs do not need enterprise software and behind building versus buying: buy or build for the company you are, not the one the vendor is pricing for.
What does the right-sized path cost?
Start by finding out what is actually there. A jinq AI Audit is from SGD 4,000 over two weeks, remote, and comes back with a ranked list: which data to join, which jobs to automate first, and what each would cost. If you want it built and run for you, a Fractional AI Officer is from SGD 7,500 a month, one to two days a week, embedded in your business. No per-seat fees. No platform you have to grow into.
Set against a six-figure platform plus a full-time analyst hire, the maths is not close, and the version you are more likely to actually use is the cheaper one.
When is the big platform actually the right call?
To be fair, sometimes it is. The enterprise route makes sense when you genuinely have the scale: large data volumes across many systems, a dedicated data team to run the platform, and questions complex enough that a simple integration cannot answer them. If that is you, pay for the platform; you will use it.
For most businesses between 10 and 200 people, that is not yet the situation. The honest first step is to find out which case you are in before you sign anything.
What to do next
Before you take another vendor call, write down three things:
- The systems your data currently lives in, and which ones do not talk to each other.
- The one question you cannot get a straight answer to today (the one you keep deciding on gut).
- The manual job that eats the most hours in your week.
Those three answers tell you the size of your real problem. In almost every case it is smaller, and cheaper to fix, than the quote in your inbox.
Not sure it's worth it?
A jinq AI Audit (two weeks, remote, from SGD 4,000) looks at the systems your data lives in and comes back with a straight answer: what to join, what to automate first, and what it should cost a business your size. If a tool you already own can do the job, we will say so. If you want it built and run for you, a Fractional AI Officer can do that one to two days a week.