As point of interest (POI) data grows in importance for businesses, both for outward-facing applications and inward-facing dashboards, organizations face a fundamental question: should they build a POI database internally or buy one from a dedicated provider.
POI data supports a wide range of use cases, including consumer-facing map applications, trade area analysis, market forecasting, site selection, and investment research. Because the quality of these outputs depends directly on the quality of the underlying data, the build vs. buy decision is not a short-term technical choice. It is a long-term strategic investment.
At first glance, building a POI database may seem attractive due to the promise of customization and control. Buying POI data, on the other hand, may appear faster but potentially limiting. The decision involves deeper trade-offs related to cost, data quality, maintenance, and objectivity. Understanding these factors clearly helps organizations choose an approach that aligns with their business goals and technical capacity.
Whether your organization uses POI data to power a map, analyse foot traffic, or support internal analytics, one principle remains constant: the accuracy of your insights is only as good as the accuracy of your data.
POIs open, close, move, and change attributes more frequently than many teams expect. Maintaining a large-scale, highly accurate POI database therefore requires continuous effort. Most organizations evaluating POI data strategies tend to fall into one of three approaches:
Each option comes with its own costs and trade-offs.
Building a POI database internally can make sense for organizations whose core business depends heavily on geospatial data. The primary benefits include customization, ownership, and full control over how the data is structured and used.
However, these benefits come with significant responsibilities.
Building a POI database requires substantial upfront investment in infrastructure, tooling, and talent. Organizations must design and deploy data pipelines for sourcing, cleaning, merging, and storing POI data. This typically involves hiring experienced software engineers and data scientists, often costing well over $150,000 per role annually, in addition to cloud computing and storage expenses that can quickly reach six figures.
These costs do not end after launch. As coverage expands or new attributes are added, infrastructure must scale accordingly.
Once infrastructure is in place, organizations must source POI data. Open datasets such as OpenStreetMap may seem appealing due to their low cost, but they often suffer from incomplete coverage, inconsistent updates, limited documentation, and licensing restrictions that may prohibit commercial use.
Web scraping publicly available POI information is another option, but it introduces legal considerations, additional engineering complexity, and costs that can rival licensed datasets.
Raw POI data is rarely usable without extensive processing. Entries must be deduplicated, verified, geocoded, classified, and enriched with attributes. Variations in naming, addresses, and coordinates across datasets make matching records that refer to the same place particularly difficult. Advanced machine learning techniques are often required to perform this work at scale.
Poorly cleaned data can lead to flawed internal analysis and unreliable consumer-facing products, eroding trust among stakeholders and customers.
Beyond direct expenses, building a POI database diverts engineering and data science resources away from an organization’s core products. For companies where geospatial data is not the primary value proposition, this opportunity cost can slow innovation and growth elsewhere in the business.
A middle-ground approach is to buy or license a partially complete POI dataset and then clean, enrich, and maintain it internally. This can reduce some upfront data collection costs while still allowing customization.
However, this option still requires significant internal effort. Organizations must carefully evaluate data quality, including geographic coverage, update frequency, attribute completeness, and documentation. Older or infrequently updated datasets are more likely to contain errors, omissions, and outdated information.
Even after licensing data, teams must invest in infrastructure and skilled personnel to correct inaccuracies, resolve duplicates, and maintain freshness over time. As scale increases, these ongoing costs can approach those of building a database from scratch.
The third option is to buy or license a complete POI database from a provider that handles data collection, cleaning, verification, and ongoing maintenance.
This approach significantly reduces time-to-value. Because the data is delivered in a ready-to-use format, organizations can focus their internal resources on analysis, modelling, and application development rather than data engineering.
A fully managed database also reduces long-term maintenance risk. Dedicated providers invest continuously in improving accuracy, coverage, and freshness, spreading these costs across many customers rather than placing the burden on a single organization.
In practice, organizations that switch from fragmented or poorly maintained POI datasets often see dramatic reductions in the time spent cleaning data, allowing teams to deliver insights faster and act on opportunities sooner.
Regardless of which option an organization is considering, several core factors should guide the decision.
Cost includes more than licensing fees or salaries. It also encompasses infrastructure, cloud resources, tooling, and the long-term expense of keeping POI data accurate and current.
High-quality POI data requires continuous validation and enrichment. Dedicated providers build specialized teams and processes to maintain this quality at scale, which can be difficult to replicate internally.
POI data is not static. Without continuous updates, even a well-built database quickly becomes outdated. Maintenance capacity is often the deciding factor between success and failure.
POI data that is created and consumed internally may introduce unintentional bias. Using third-party data provides a level of separation that helps maintain objectivity across industries and use cases.
The decision to build or buy a POI database is ultimately a strategic one. While building offers control and customization, it demands sustained investment in infrastructure, talent, and ongoing maintenance. Partial solutions reduce some barriers but still require significant internal effort.
For many organizations, buying a fully managed POI database offers the most reliable balance of cost, quality, scalability, and objectivity. By offloading the complexities of data engineering and upkeep, teams can focus on turning accurate, up-to-date POI data into actionable insights that drive better decisions.
Buying POI data typically involves licensing a dataset from a third-party provider that collects, cleans, and maintains the data.
Yes, particularly for organizations whose core business depends heavily on custom geospatial data pipelines and who have the resources to maintain them long term.
POI data changes continuously, so frequent updates are essential to maintain accuracy.
Open-source data may be incomplete, outdated, inconsistently maintained, or restricted by licensing terms.
Poor-quality POI data can lead to incorrect models, flawed insights, and loss of trust.
No, but it significantly reduces the engineering and maintenance burden compared to building or partially managing a dataset.
Key criteria include coverage, update frequency, attribute completeness, documentation, and long-term maintenance commitment.
