POI data as a commodity

POI data is everywhere. From open-source platforms like Overture Maps Foundation to Google Maps on your phone, it’s accessible and abundant. Businesses rely on it for everything from site selection and logistics to customer experiences and competitive analysis. 

However, despite its widespread availability, businesses need data that is accurate, clean, and usable. The problem isn’t access. It’s quality. 

Why should you process POI data?

POI datasets are like crude oil—they need to be refined. Imagine an oil company selling crude oil directly to consumers. No refining, no processing, just crude, unfiltered oil. It’s not very usable unless you know what you’re doing. The bottom line is that consumers don’t need oil; they need fuel. The same applies to POI data. Businesses need something refined, processed, and ready for use. 

POI datasets can be full of noise and inconsistencies, such as duplicates and outdated information. These can have real business consequences, such as misguided decision-making, wasted budget, and lost opportunities. 

You can’t rely on a single data source

Relying on POI data from just a single source isn’t enough. That’s not to say the data source is bad; on the contrary, there are many reliable sources available. However, no one news source tells the full story. No single dataset gives a complete, unbiased view of locations.

• Google Maps? You can look at their POIs database (albeit one POI at a time) or obtain the data through Google Places API. Still, it’s built for general navigation, not high-precision business intelligence. That’s like using a cooler box as your home fridge. Great for a park picnic, but not so great for storing all your refrigerated produce. 
• Open-source datasets? Unlike Google Maps, open-source datasets are built for high-precision business intelligence and have a growing community of contributors. However, relying solely on one open-source dataset isn’t enough; inconsistencies will still exist as the open-source format can be exposed to errors in quality control. Frequent errors include duplicates and the freshness of the data. Data riddled with mistakes like this can render it obsolete, delivering little to no value for business insights.
• Commercial POI providers? That’s us. This group aggregates and sells POI data and often relies too heavily on its own data, which can be limited and outdated. Furthermore, without proper investment in the data cleaning process, providers end up in the same boat as open-source datasets: quality issues and inaccuracies. 

The key to reliable POI data isn’t just gathering it—it’s cleaning, merging, and structuring it into something usable. That’s where the value lies. 

Echo Analytics: The refinery 

Echo Analytics doesn’t compete with open-source data and mapping platforms. Instead, we enhance and refine POI data, doing the heavy lifting of cleaning, validating, and standardizing it.

• We aggregate POI data from multiple sources.
• We clean, de-duplicate, and enrich the data for accuracy. This secures that no information is missing regarding a place's category, address, contact information, etc. For the data to be accurate, it must be complete with all the information about a POI. 
• We deliver high-quality, business-ready POI data—so you don’t have to waste time fixing it.

What does it mean to have accurate POI data?

At Echo, our POI datasets have a 95% accuracy coverage for 80 million POIs, 16k brands, and across 210 countries and territories. Accuracy is much more than your POI's exact longitude and latitude coordinates. An accurate dataset is comprehensive information about places at a granular level. This includes the volume of data available, the accuracy of brand information in the data, the extent to which all the data is present, and the recency of the data being used.  POI data is only as valuable as its quality.

The risks of poor POI data

Bad data, bad decisions

Every business wants to utilize location intelligence for analyses such as site selection, consumer analysis, optimizing point of sale systems, competitor analysis, and more. To give context to these insights, you need POI data layered with other geospatial insights like footfall data. If you can see that people spend time at a certain location but can’t identify where that place is, then the insight is useless. POIs are essential, but if the data guiding these analyses is wrong, that impacts decision-making too. 

Poor POI data isn't just an inconvenience; it's a high-risk business problem. Inaccurate, outdated, or incomplete location data can lead to missed opportunities, wasted investments, and operational inefficiencies. Let’s break down the hidden risks of bad POI data and the consequences businesses face when they rely on it.

Risk #1: Costly decisions based on inaccurate data

You can’t make data-backed decisions with inaccurate data. Decisions such as where to open a new store, where to allocate resources, or how to plan real estate development involve POI data. There are several consequences when this data is unreliable. 

Retail site selection failures: When a company opens a new location based on POI data that misidentifies nearby competitors or overstates foot traffic, it results in poor sales and eventual store closure. 

Misguided real estate investments: An investor purchases commercial property based on outdated POI data, only to discover key businesses listed in the dataset no longer exist. 

Inefficient logistics and routing: A delivery company plans routes based on POI data riddled with duplicates, leading to confusion, delivery delays, and increased costs. 

Bad data leads to bad decisions. And when those decisions involve major financial investments, the costs add up quickly.

Risk #2: Operations drainage of cleaning data in-house

Manually cleaning and verifying POI data is a time-consuming responsibility that can often be underestimated. This is especially true for businesses that want aggregated data from multiple sources to improve the quality of their datasets. DIY-ing POI data comes with an operational risk. 

Data engineers and analysts will pour valuable hours manually de-duplicating, cross-referencing, and fixing errors. They’ll be faced with inconsistent formats and conflicting records, making automation difficult and forcing manual intervention. Every hour they spend on cleaning the data is an hour not spent on strategic analysis and decision-making. 

The opportunity cost of handling data cleaning in-house is often greater than the cost of acquiring high-quality, pre-processed POI data from a trusted provider.

Risk #3: Damaged customer experience and reputation

The role of POI data in internal decision-making can have an equal effect on customer experience and brand loyalty. When businesses rely on bad POI data, customers can feel the effects in several ways: 

• Incorrect business listings: Customers search for a store that doesn’t exist or has moved, leading to frustration and a negative brand perception.
• Service failures: A food delivery company relies on bad POI data, sending drivers to outdated addresses and causing missed or delayed orders. Or a consultancy advises a client on strategic decisions based on inaccurate data

Your consumer expects a seamless and reliable experience. Businesses that rely on bad data risk losing trust and customers. 

POI data is only as valuable as its quality

We’ve made the point clear: Every business that relies on POI data needs the highest level of quality. 

When you rely on single-source POI data, you run the risk of not having a full picture. When you attempt to clean and aggregate from multiple sources, you also risk losing valuable time and money. If you trust inaccurate datasets, you impact your reputation and the customer experience. Bad data is bad for business, no matter how you try to frame it.

A quick self-assessment checklist: “Is your POI data hurting your business?”

1. Has your data led to bad business results?
   
   1. Site selection failures?
   2. Missed opportunities due to outdated data?
   3. Delays and increased costs?
2.  Are you spending too much time manually fixing your data?
   
   1. Are your engineers/analysts spending excessive time de-duplicating, cross-referencing, and fixing errors?
   2. Is the opportunity cost of in-house data cleaning greater than the cost of acquiring high-quality, pre-processed POI data?
3. Is your customer experience suffering due to data issues?
   
   1. Are there incorrect business listings leading to customer frustration?
   2. Are there service failures due to bad POI data? E.g., delivery delays.
   3. Is inaccurate information damaging your brand reputation?
4. How many sources of data are you using?
   
   1. Are you aware of the limitations and potential biases of a single source?
   2. Are you missing out on a more complete picture because the data isn’t aggregated from multiple sources?

Unlock the full eBook

Want to explore how Echo Analytics processes and cleans POI data? Unlock the full eBook and discover our data processing workflow, the impact it has on businesses, and why the future of POI data is in its processing and collection.