Using Public Data in Your Business
The data the state opens is free. Few businesses use it. The National Data Library will publish at least 2,000 public datasets in machine-readable format, with at least 100 available through APIs. These are not research reports. They are raw data. Used properly, they feed decisions from market analysis to pricing.
The problem is not access. The problem is knowing which data answers which question.
What Is the Problem?
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- Nobody looks
- Someone looks but nothing follows
- Decisions get made on irrelevant data
Three behaviours show up around public data.
Nobody looks
The most common one. The data exists; nobody opens it. The assumption “there won’t be anything useful for us” is accepted without ever being tested.
Someone looks but nothing follows
It gets downloaded, opened, looks complicated, gets closed. Raw data does not arrive as a finished chart. It needs work, and the first attempt is tiring.
Decisions get made on irrelevant data
The riskiest one. The dataset does not answer the question asked, but it gets used anyway. Wrong scope, old date, different definition. The decision looks solid; its foundation is not.
Why Does It Happen?
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- Data gets searched without a question
- Data literacy is thin
- Processing is assumed to be heavy
Three reasons.
Data gets searched without a question
Data browsed to “see what’s there” produces nothing. The decision question comes first. “Where should we open the new branch” is a question. “Let’s look at the data” is not.
Data literacy is thin
Definitions, scope, currency and sampling differences get overlooked. Two columns with the same name can measure different things. Seeing that difference takes training, and it is one of the topics in the national literacy programme.
Processing is assumed to be heavy
People think handling large data needs a team. For most work it does not. A spreadsheet, a few filters and one chart will do. AI tools shorten this work considerably as well.
How Is It Done?
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- Step 1: write the decision question
- Step 2: filter datasets with three tests
- Step 3: combine it with your own data
Three steps.
Step 1: write the decision question
Write the question you want answered in one sentence. Then add: which decision will this answer change? Do not go looking for data on a question that changes no decision. This is the data-side version of the decision sentence logic.
Step 2: filter datasets with three tests
Not every set will help; that is why the filter exists. Ask three questions of each. Scope: does it include my region, sector, group? Currency: when was it last updated, is there an update schedule? Definition: do the columns measure what I think they measure? If any answer is no, drop the set. The library’s quality score will make this easier — it includes machine readability, metadata completeness, currency and licence clarity.
Step 3: combine it with your own data
This is where the value appears. Public data alone stays general. It becomes specific when it crosses your own. Put your sales figures next to regional data, your customer profile next to demographic data, your supply costs next to sector data. That is where difference is born. Which means your own data has to be in order first. Orderly data is the key that makes public data usable.
How Long, Where to Start?
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- Two hours to scan, half a day to analyse
- No money cost, but a real attention cost
- First step: start from a real decision
Small arithmetic, and the first step fits this month.
Two hours to scan, half a day to analyse
Scanning the library for your sector and marking candidate sets takes two hours; repeat it annually. With a clear question, a filtered set and orderly data of your own, one analysis finishes in half a day. Compare that to buying the same work as a research report.
No money cost, but a real attention cost
The data is free. The only thing you pay is attention. A decision made on the wrong data is more expensive than a decision made without data. That is why the filtering step is never skipped. The quality score will help, but the final judgement stays with you.
First step: start from a real decision
Pick an actual decision you face this quarter; a practice question produces a practice answer. Then scan the library once and mark three to five sets. Third, put your own data beside it. Public data alone is half an answer. If your own data is scattered, start there; that is the first condition of turning data into decisions.
The Common Mistake
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- Using data as decoration
- Making current decisions on old data
- Trusting a single set
Three traps are common.
Using data as decoration
The decision is already made and data gets hunted to support it. That is not working with data; it is defending with data. One question exposes it: if this data said the opposite, would I change my decision? If the answer is no, there is no need to look; the decision is made.
Making current decisions on old data
Some public datasets update annually, some less often. In a fast-moving area, three-year-old data misleads. Always check the update schedule. The plan targets at least 80 percent of sets being updated on time; the date check still stays with you.
Trusting a single set
An important decision does not rest on one dataset. Verify with a second source where possible. Two sources pointing the same way raise confidence; conflicting sources mean you need to look deeper. And a raw number read without context misleads: high population in a district does not mean high demand. Competitor density and income levels enter the picture too.
Frequently Asked Questions
Sık Sorulan Sorular
No, because most of the preparation sits on your side. Writing the decision question, ordering your own data, building the filtering habit. None of that waits for the library. Whoever is ready when it fills uses it on day one; whoever is not says “we’ll look sometime” for another year.
The advantage is not in the data but in the combination. Everyone sees the same public data, but everyone’s own sales, customer and cost data differ. Value appears at the intersection. And one more thing: many will access the data, few will wire it to a decision.
Data in health, defence and finance will not sit in the open library. It opens through secure data spaces, only to authorised actors, under controlled conditions, with separately defined access rules. If you work in those fields, the mechanism to follow is not open data but the data-space programme.