Data Silos: The Hidden Barriers to AI Innovation
Note: While data silos can have various interpretations depending on the context, this article specifically focuses on data silos in the context of AI, where the isolated data is crucial for training AI models.
As you might be wondering, what exactly is a silo? In farming, a silo is typically a large cylindrical container used to store silage, or fermented fodder for livestock. But what we really mean by silo herein is isolation. These containers are used to isolate silage for protected storage.
The term “silos” in the context of data refers to the isolation of data, where information is kept separate and inaccessible due to barriers like privacy regulations and difficulties in data transfer.
Let’s dive deeper into these metaphorical containers in the context of data silos. With rising concerns over data privacy, people are more aware than ever, and governments worldwide have implemented various laws regulating how personal data is used. Today, we have several privacy regulations like GDPR, CCPA, PIPEDA, POPI, LGPD, HIPAA, PCI-DSS, and more. You can refer to this article to explore them further. By 2024, 75% of the world’s population will have their private data protected under modern privacy regulations.
And when companies fail to comply with these privacy regulations, the penalties can be significant. For instance, Meta had to pay $1.3B, Amazon was fined $781M, and Instagram faced a $427M fine, among other examples.
So far, we’ve discussed how privacy regulations can prevent data from being moved, such as to the cloud, or outside its original source. But there’s more to the issue of data immobility. Another major factor that contributes to data silos is network bandwidth.
Take autonomous vehicles, for example — they generate terabytes of data on hourly basis. Gathering all this data from multiple vehicles and transmitting it to the cloud would require massive bandwidth. Or consider wind turbines in remote locations, where network connectivity isn’t strong enough to send all the edge data to the cloud. This limitation in bandwidth is another significant cause of data silos, as it restricts the ability to migrate data efficiently.
From this we can conclude the two major reasons for the formation of Data silos are:
1. Privacy Regulations
2. Network Bandwidth
Data silos are isolated data repositories where data remains immobile due to privacy regulations or network bandwidth limitations.
Now that we understand what data silos are and how they form, let’s address the key question: Why are data silos an issue? As mentioned earlier, we are focusing on data silos in the context of AI.
Due to the existence of data silos, many AI applications involving model training suffer from incomplete access to data. This is because data cannot be aggregated from multiple silos into a central location for training, leading to several challenges:
- Insufficient Data Volume: In many cases, the amount of data stored within a single silo is too limited to train an accurate and robust AI model.
For example, in medical AI, training on data from just one hospital won’t capture enough diversity, reducing model performance. - Incomplete and Biased Datasets: Without access to diversified data from different silos, models trained on isolated datasets may suffer from selection bias. This leads to overfitting, where models perform well only on specific cases from the silo’s data but fail to generalize.
For instance, an AI model trained only on urban traffic data might struggle to perform in rural areas due to lack of diversity in its training set. - Privacy Violations: In some cases, unethical practices are used to circumvent silos, such as aggregating data without proper consent or using unauthorised means to access sensitive information. This can breach privacy regulations and erode user trust.
A high-profile example would be scraping social media data without user consent to train models. - Underutilised Data Assets: A significant amount of valuable data remains untapped because it’s fragmented across multiple silos. This leads to missed opportunities for optimizing models and deriving insights.
For example, in industries like finance, where data is scattered across departments or regions, critical insights that could improve fraud detection or customer experience remain siloed and unused.
In conclusion, it’s important to tackle the problem of data silos to unlock valuable data for innovation and break down the hidden barriers that hold back AI development. By doing this, we can create stronger and more effective AI solutions.
References:
[1] https://bluexp.netapp.com/blog/data-compliance-regulations-hipaa-gdpr-and-pci-dss
[2] https://www.enzuzo.com/blog/biggest-gdpr-fines
[3] https://www.statista.com/statistics/1175672/population-personal-data-regulations-worldwide/
[4] https://www.iotforall.com/addressing-data-processing-challenges-in-autonomous-vehicles
