Public business directories are useful starting points for market research. They offer concrete examples of companies, projects, and business needs that might otherwise be difficult to find. The challenge is deciding how much those examples can actually tell us.
Bashalog’s Odoo research brings that challenge into focus. The dataset contains 903 public entries returned by Odoo’s Pakistan-filtered directory. It provides material for examining the directory’s visible industry, location, and workflow descriptions. Interpreting that material requires care: a country filter does not prove that every listed organisation is a domestic business, and a public listing does not describe every installation in a market.
Here is a practical way to approach this kind of research.
Start by defining the unit you counted
Before building a chart, write down what one row represents.
A directory entry, a legal business, an implementation project, and an active software customer are different units. If the source provides entries, describe the result as entries unless you have independently established something more specific.
That wording should stay consistent across the title, summary, charts, and downloadable data. It helps readers understand the evidence before they interpret the findings.
Treat filters as collection tools
A country-filtered directory helps define where a search begins. Its results still need inspection.
Foreign entries can appear in a geographically filtered collection. A business might operate internationally, a project description might refer to another market, or the available information might leave its location unclear.
For analysis, it is useful to separate the source’s classification from any location you independently verify. Keep an explicit unknown category rather than assigning a city because the company name or surrounding context seems plausible.
Separate observed facts from inferred fields
Public descriptions often mention activities such as accounting, inventory, sales, or manufacturing. Those mentions can help researchers understand how projects are presented.
They provide weaker evidence about the exact software modules installed, whether those modules remain in use, or the scale of the deployment.
City assignments and workflow classifications can also involve interpretation. Label inferred fields, document the evidence used, and make it possible to distinguish direct statements from analyst judgments. A reader should be able to understand how a label was assigned without having to trust an unexplained chart.
Make the denominator visible
Percentages become easier to misread when the population changes between charts.
A city breakdown based on entries with identifiable locations answers a narrower question than a breakdown covering the entire collected dataset. Show which records were included, which were excluded, and why.
The same principle applies to industry categories. Explain whether each entry belongs to one category or may appear in several. Otherwise, readers may assume that every set of percentages should add up in the same way.
Use the results to improve questions
Directory research can help identify examples worth investigating, gaps that need additional evidence, and questions for conversations with businesses.
It cannot establish total adoption, vendor market share, or the size of an addressable market on its own. A useful next step is to combine the public evidence with independently verified company information and first-hand research.
That approach also makes the work easier to update. Corrections become part of a documented research process rather than exceptions that undermine an overstated headline.
Bashalog is an Odoo implementation business and the publisher of this research. Readers can inspect the report, methodology, and downloadable data.
For anyone building a similar study, the central discipline is simple: keep each claim close to the evidence supporting it, and make the remaining uncertainty easy to see.
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