More and more companies are using third-party data to make better decisions, for example POI data that describe physical stores or other interesting places. Investing in spatial data science is something that 68 percent of all organizations plan to do in the next two years. When it comes to leveraging POI data, we’ve talked to many companies that have considered building their own database rather than buying it from a data vendor like SafeGraph. At first, this seems to make sense: why go with an outside firm if you can potentially build your own dataset? But before you take this on, there are four important factors to consider:
1. Cost
2. Quality
3. Maintenance
4. Objectivity
We’ll discuss each in detail below.
Building a POI database takes time and money to design, build, and deploy. While having your own database might look like an investment that pays for itself over time, there is an enormous upfront fixed cost involved with setting up the initial foundation of partnerships, services, and infrastructure for collecting, cleaning, organizing, and merging data. While some organizations may have the skillset to do this, many do not. Ask yourself if it’s worth the trouble, time, and money to do everything yourself with the risk of failure, or instead rely on another party that has the expertise and data you need to get started with spatial data science quickly. If you do decide to go ahead and build a POI dataset yourself, know that there is also a lot of opportunity cost in taking your FTEs away from projects to build this out. When you add up everything, it’s likely much cheaper to leverage a vendor than do it yourself.
Not all data is created equally. To make sure that your organization is provided with accurate, trustworthy and current data, SafeGraph hires talent specifically to solve complex engineering problems related to POI data. We also have a unique sense of culture and passion for curating places data that includes brand affiliation, spatial hierarchy, and other key business details. This allows us to have an extreme focus on quality, and provide truth sets for physical places that inform market analytics, investment research, site selection, and more.
POI data changes rapidly and continuously. To make sure you always have the most current data available for analysis, you need continuous capacity inside your organization that will take care of these responsibilities. Apart from engineering talent to manage data changes, you also need a robust R&D team to manage the future needs of the business, such as expanding coverage to new geographies or adding new attributes. While many organizations are creating their own data science departments, your IT architecture also needs to be scalable for the future to facilitate and create new data pipelines and applications to maintain, process. and analyze large quantities of spatial data. Do you have the capacity to scale after creating a whole new in-house data infrastructure, or do you instead prefer to focus on data analysis and leave the rest to a dedicated party? If the answer is the latter, you might want to choose a dedicated data vendor.
Building your own dataset that feeds into your own models may introduce unwanted bias. Because the data is created, maintained, and used inside your own organization, you might not be able to take a step back and critically evaluate your own data and methods. Instead, leveraging a third-party organization’s data ensures that the data is unbiased and does not favor any one industry or organization. This allows for the most accurate facts and outcomes for your business to continue to improve. Before making the decision to invest in creating your own data infrastructure and datasets, you might want to think ahead and answer the question of how to avoid potential unwanted bias in your data in the future.
Either way you choose to do it, implementing and integrating POI data into your organization’s workflows is a big investment of time and money. But when deciding whether to build your own dataset or buy one from a provider, consider how cost, quality, maintenance, and objectivity factor in. Know that creating your own data and infrastructure is a long-term investment that requires a lot of planning, money, and effort to succeed. Even then, data objectivity and quality are not automatically guaranteed. Instead, third-party vendors might be a good alternative to provide your organization the data they need for your data science and analysis to be competitive.
Schedule a demo with POI data experts today to learn how much time and money you can save.
