How to enrich your data fast and effectively?

How to enrich your data fast and effectively?

How to enrich your data easily, safely and costly with spatial data and technology

Data enrichment is a strategic process of enhancing existing datasets with additional, relevant information.

Have you ever wondered how you can enrich your customers’ or suppliers’ database with data like: average income, crime rate, population age, etc.?


Enriching data’s main benefits are:

🎯Targeted Marketing and Personalization

🎯Enhanced Decision-Making

🎯Improved Customer Relationships and Retention



The main three steps to enrich data are:

1️⃣Data Collection and Identification

2️⃣Integration and Data Cleansing

3️⃣Enrichment and Analysis

🤔 While it looks risky, costly, and timely, and sounds like it demands new tools to incorporate into your organization, it’s not always the case.

Spatial SQL is a hidden treasure that exists in almost any database or cloud warehouse. ☁️ Whether commercial or open source. Utilizing it is fast, powerful, and without a need to change the existing organization’s infrastructure and resources. 


The basic process is:

  1. Verify your data is already spatially aware (i.e. customers’ addresses have coordinates) If not enrich your data as spatially aware:  Add coordinates for addresses. (The term called  “Geocode”)
  2. Load or connect to spatial data (i.e demographic data, parcels, risk area) which is usually an open data
  3. Make the data enrichment: join it to your data with a simple spatial SQL query
  4. That’s it. Your data is enriched. You can now analyze your enriched data, use it in marketing campaigns, and much more  (i.e. each customer’s mean income, education, crime rate, address, etc.)


💡There is a faster, non-costly solution, that will utilize the organization’s current data infrastructure and resources with no need to implement new tools or infrastructure and it will be accurate and safe.


🧐 We suggest using the Spatial data enrichment method with the power of Spatial SQL.

Spatial data is mostly open and regularly updated by formal public organizations.

It can be used easily and regularly as a data enrichment basis for the organization’s data enrichment process, and even for solving and analyzing spatially related business questions.


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