AI models must be trained on high quality, complete data. A lot of it. Backups are a treasure trove of this type of data, but are they ready to train AI?
As more organizations integrate AI into business processes, they learn that AI models must be trained on massive amounts of quality data to produce accurate outputs. Where that data comes from varies, but one emerging option might already be in your organization's storage.
AI models gain knowledge by taking in data, forming hypotheses, making educated guesses and refining their approach to future decisions based on the resulting outcomes. To hone that knowledge and train an effective AI model, you need vast stores of high-quality, up-to-date data that are well organized and easily accessible.
Some organizations source training data for AI models through government databases or reliably crowdsourced efforts, but others are turning to their own data backups. After all, that data is just sitting there, untapped, waiting to serve a role beyond potentially restoring lost data. Why not use data backups to train AI models? Is it even a good idea, or are you better off sourcing your training data elsewhere?
Evaluate your backups
First of all, yes, it's possible to use data backups to train AI models. However, as with anything, there are nuances to consider when evaluating whether or not your backups should train AI models.
Data backups are typically created for one primary purpose: to protect against data loss. In the event of a natural disaster, servers storing data might be destroyed or damaged, leading to data loss or corruption. A data backup — usually stored offsite — can then be used to restore the lost or corrupted data, returning it to its original state.
AI needs high-quality data, which means it must be complete, consistent, and timely, among other dimensions of data quality. If a data backup is merely a copy of data as it exists, it might not be complete and ready for consumption by AI models. Off-site copies of data might not be updated frequently and can require additional time and effort to cleanse and transform the data. Similarly, archived backups or those kept on cold storage can be outdated, unorganized and not easily accessed by the organization, making them unsuitable for training AI.
However, most organizations have learned by now that one backup copy is not enough to protect data. At minimum, the general recommendation is to follow the 3-2-1 method, which requires three copies on two different types of storage media with one kept offsite. If an organization follows this rule, it might have a no-frills backup stored offsite to protect against complete data loss, but it should also have at least one accessible, frequently updated and tested backup available that is better suited for AI training.
Benefits of using backups to train AI
Assuming the AI model training data backup process is set up properly, there are several advantages to using backups for this purpose.
Faster access to high-quality data
Accessing vast stores of relevant, high-quality data can be challenging for those who want to start training an AI model sooner rather than later. However, backup data that’s well organized and prepared for consumption might be a resource you already have. Backups do not have much purpose beyond having a copy, and they should be highly relevant to the business, unlike third-party or market data sets you’d have to pay for to train an AI model. Using backup data could help you start training an AI model with a significantly faster ramp-up time.
Better learning efficiency
Because backup data is likely highly relevant to the business and its needs, training an AI model with it should lead to more efficient learning. The AI model won’t need to deal with as much irrelevant data, noise or incomplete data, enabling it to better fine-tune its decision-making and predictive capabilities over time. And because the model is using backup data, it won’t have to disrupt regular data processes and steal bandwidth from other computation-intensive resources.
More accurate data
Ideally, backup data is fully complete, organized and up-to-date. When these dimensions of data quality are met, the AI model can train on high-quality data, and the higher the data quality, the more accurate and informed its decision-making will be. One common issue with AI models is inaccuracy, so using backup data could help solve this challenge and deliver a superior model with more accurate, reliable, and trustworthy decision-making capabilities.
Better protected data
Backed-up data typically already has a layer of protection built in, since it’s meant to serve as a secure replacement of data in the event of a disaster or breach. Creating additional backups, especially immutable backups, for AI model training can help ensure continuity for the model and the business as a whole. In addition, backed-up data should make compliance easier for AI data governance — you’ll know exactly what data you’re using to train your model with, where it came from, and what state it was in at the time of consumption.
Are there drawbacks?
Many AI initiatives are failing, and a lack of effective training is a major cause. There are benefits to training AI models with backup data, but doing so requires significant oversight. Otherwise, there are several ways it can go wrong.
It's paramount to use the most up-to-date data possible, which is one of the biggest hurdles to using backed-up data.
Data is not prepared for consumption
Data must be organized and in a format that AI models can consume and analyze, such as a time-series format. If the backup data is not in the right format or the data quality is insufficient, it will take significant time and effort to clean, prepare and transform it. This can extend the training process, and any errors in the data can affect the accuracy --and, therefore, the usefulness-- of the model.
Data is not diverse enough
The information in your data backup might be highly relevant to the business, but it comes with a potential downside: Your model might not be getting a wide range of data. This can limit the model's accuracy, resulting in a narrower range of outputs that don't account for as many possibilities. If this is the case, you might need to bring in new data or different data sets to help expand the model's capabilities.
Data is outdated or no longer relevant
There are several types of data backups an organization might use, and some backups are better than others. If your organization is using an older data backup in a fast-paced data environment, the AI model might be trained on data that's no longer hyper-relevant. This can affect the quality of the AI's output and lead to decision-making that doesn't accurately reflect reality. It's paramount to use the most up-to-date data possible, which is one of the biggest hurdles to using backed-up data.
Data is subject to compliance regulations
Using backup data could raise security, privacy, and ethical concerns, especially as AI data governance becomes more prevalent and stringent. If you plan on using backup data for AI model training, you must disclose this fact. Customers who are not aware of this or don't sign off on it might have their privacy and trust breached, which can erode the business's reputation. Given this, it’s vital to make sure all backup data used for AI model training complies with security standards and privacy policies to protect data, customers, and sensitive or proprietary information.
Jacob Roundy is a freelance writer and editor specializing in a variety of technology topics, including data centers and sustainability.
