To generate higher-quality insights, clients often ask us to augment their data with open data. Open data is freely accessible data, often created by government institutions. These institutions analyze data on, for example, citizens’ consumption behavior or living conditions, and publish their results as a service for the public (whose tax money funded the analyses). The open data is aggregated to, for example, the regional level (e.g., average income per neighborhood). This protects individuals’ privacy but also allows for analyses of regional differences. Furthermore, much of the open data gets updated periodically and can therefore also be used to uncover trends over time.
Open data can lead to insights that were not possible with in-house data only. Companies may want to enrich their data for several reasons:
- to ensure the correctness of their own data, for example, by checking customer addresses against a public address database;
- to get more insightful dashboards, for example, by adding demographic statistics to market segment visualizations;
- for strategic purposes, for example, to find the most valuable location to open a shop;
- or to feed extra features into prediction models, for example, by adding neighborhood-level demographic statistics to predict customers’ preferred marketing channels.
We created an inventory of open datasets to help companies fill in the gaps in their data. These datasets contain information on a wide variety of phenomena: demographic statistics at different regional levels, addresses and geolocations, mobility solutions, company characteristics, news articles, open legislative texts, weather data, data on the production and consumption of electricity and other utilities, crime statistics, etcetera. We update the inventory whenever we encounter high-quality open data and also include some high-value paid access datasets.
The open data inventory proudly stands its ground in our problem-solving toolbox. It has already helped us in projects with:
- a telecom player, to strategically upgrade their infrastructure across the country;
- an energy provider, to predict the best contact channel for each customer, based on, e.g., regional education levels and professions;
- a real estate valuator, to predict prices more accurately;
- a garden and DIY retailer, to predict propensity to buy, based on neighborhood garden coverage;
- a bank, to score companies on sustainability efforts, based on, e.g., data on CO2 emissions.
Do you have a problem that you struggle to solve with in-house data only? Contact us to find out how we can help.
