Even though machine learning offers a lot of opportunities, it is not something that can solve everything, and comes with limitations that need to be addressed.
Introduction
As the era of advanced technology unfolds, the integration of machine learning and location data has become increasingly prevalent. This last in a series of three blog posts aims to shed light on the benefits and disadvantages of utilizing machine learning algorithms in the context of location data analysis. By understanding both the potential benefits and limitations, we can make informed decisions regarding the implementation of these technologies.
Disadvantages
Machine learning and location data integration face challenges of historical bias and unpredictability due to changing relationships and unforeseen events. Additionally, a shift in mindset is required to address the benefits and disadvantages arising from the fundamentally different methodology used in these products.
Historical bias
The relationships trained are usually based on some historical ground truth. That means that the end product is to a large extent influenced by historical relationships. However, if relationships change models require retraining to ensure that predictions stay up-to-date and are not drifting.
Some things are unpredictable.
Even though current developments in AI make machine learning look like the solution to almost everything, it is important to keep in mind that a lot of things are unpredictable. There is no model that can foresee a pandemic and predict the pandemic’s impact on stores. In addition, a model can only learn existing relationships within the data. Events or behavior that was either not in the training data or does not have a relationship within that data is unpredictable.
The shift in mindset.
Even though the resulting products might look the same, they are coming from a fundamentally different methodology. That leads to challenges for both the commercial side and the product user to ensure the benefits and disadvantages are properly addressed.
However, when we openly address machine learning shortcomings and educate those properly, the benefits will outweigh those disadvantages.
Benefits
Combining machine learning with aggregated data enables the development of future-proof, privacy-friendly products adhering to ethical standards. This approach also leads to robust and trustworthy location data products, independent of GPS sources, resulting in higher quality, reduced data volume, and lower costs. Moreover, machine learning facilitates new product innovation by combining diverse datasets and contexts, unlocking previously unavailable possibilities in the location data industry.
Ethical and privacy-friendly
Combining machine learning with aggregated data on the 1st-party side will allow for building future-proof privacy-friendly products following strict ethical standards.
Robust and quality product
Building a location data product that is not directly dependent on GPS data sources will make the product way more robust and trustworthy. In addition, since the product can be based on various high-quality data sources the end product can come on average with higher quality.
Less data volume and costs
Machine learning can work on way less data compared to what is needed currently to build location data products. This allows independence of supply sources but also removes unnecessary storage of vast amounts of data. In addition, costs for data processing and maintenance are comparably cheaper with a machine learning infrastructure.
New product innovation
After the improved privacy, maybe one of the biggest advantages is the possibility for new product innovation. Machine learning in its nature does combine different datasets and contexts and, thus, allows for building products that are currently unavailable in the location data industry.
Summary
This article and series of posts explores the pros and cons of integrating machine learning algorithms with location data analysis.
While machine learning presents opportunities for future-proof, privacy-friendly products and innovation, it is important to address its limitations, including historical bias and unpredictability.
However, by acknowledging and educating about these shortcomings, the benefits of robust and trustworthy products, reduced data volume and costs, and new product possibilities outweigh the disadvantages.
