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Applying AI and Machine Learning to Data Storage

There are a growing number of data storage requirements to fulfill as company operations become increasingly digitized. They might include specifics for how long to keep information before discarding it, where to retain the data and how to ensure it stays secure. Fortunately, artificial intelligence (AI) and machine learning can make it easier to meet storage needs. Here’s a closer look at why that’s the case.

Machine Learning Algorithms Help Group Data for Proper Storage

One of the biggest challenges associated with modern companies collecting more data is that the people handling that information can’t deal with its sheer volume. Companies may receive new data so fast that employees need help to store or process it correctly. Inconsistencies may also result, such as if two people have different opinions about which database to use for keeping a particular file.

Machine learning algorithms can help maintain data storage requirements by recognizing certain aspects of the incoming information. You’ll see this in action by using an AI-driven email service. Many options on the market learn to put emails in the right folders by learning from users’ past interactions with similar messages to understand data storage requirements. Such solutions can tell the difference between communications from a person’s boss and an advertisement they’ll likely find uninteresting.

In such cases, incoming messages are automatically stored in folders like Priority and Promotions based on inherent characteristics. This all happens in the background, so users often only know about it if they check the contents of each folder. However, many people like this approach to data storage when their goal is to have a clean inbox.

Machine learning algorithms can also store data in the right categories, making it easier for people to analyze and file away later. Researchers from Greece’s University of Piraeus built a transaction-classification model that helped predict cash flow for small-to-medium-sized entities. They believe their work could improve models currently used for fraud detection and money management.

AI Improves Existing Methods

Artificial intelligence can process gigantic amounts of data in short time spans, so it’s often useful for people who want to enhance existing versions of products. Consider the case of a company that sought a solution for installing a data storage unit into an enclosure. A service provider met the challenge by creating additional components, including custom brackets and a cable-management system. Some companies add AI to the equation, letting algorithms rule out unsuitable designs and spotlighting those most likely to work.

AI can also improve storage by making data centers more sustainable. However, here’s where things get tricky. Training one AI algorithm can generate as much carbon dioxide as the lifetime of five automobiles. However, that doesn’t take away from the successes that an AI algorithm could bring when making a data center greener.

It could identify the most carbon-intensive processes in the data center and suggest what managers could do to improve them. AI algorithms helped Google reduce cooling needs at its facility.

The world has such a tremendous need for data storage that these specialized facilities will only become more prominent and widespread. Fortunately, using algorithms for their management is a step in the right direction for sustainability concerns.

There’s also a product from SeMI Technologies called Weaviate. Its AI-powered data search engine does not require exact matching to produce results. The combination of data storage with machine learning makes searching faster, and people associated with the company believe their product will change how users work with information for the better.

AI Can Help Leaders Use Dark Data

Dark data is information a company gathers and stores but doesn’t use. The lack of usage often occurs because the information is in an unstructured format that is not yet usable. A related issue is that executives may not even know how to find all their businesses' dark data.

However, AI can reduce these instances. Intelligent algorithms can spot patterns and reveal aspects of the information that humans wouldn’t otherwise notice. It can also flag potential risks companies exacerbate by keeping too much dark data.

Another thing to remember is that people can’t extract value from dark data. Thus, it’s often worthwhile to invest in an AI solution to help. That unlocks potential at the business and allows representatives to pursue formerly hidden avenues to drive profits.

It’s also easy to see the potential of AI in industries like construction, which often have multiple sources of dark data stored in different places. Project managers that don’t know how to find information or are unsure it exists may not have all the content needed to make well-informed decisions.

AI can also stop companies from having too much dark data by learning storage requirements and automatically classifying the information accordingly. Then, rather than having a surplus of dark data, business representatives have content they can use to make decisions and improve the company.

Using AI Products to Maintain Data Storage Requirements Related to Security

With cyberattacks increasing, many company leaders have decided to use AI and machine learning to keep their information secure and ensure it aligns with data storage requirements. Cybersecurity service providers offer products to help. One comes from Cohesity, a company specializing in data management and backup solutions.

It recently launched a software-as-a-service solution called Datahawk. This AI product has three components that work together to secure data from hackers. The one most relevant to data storage is a classification engine that automatically finds and categorizes information across large arrays.

It supports better cybersecurity since many company representatives don’t know where their data resides. When that’s the case, they can’t protect it, either. The classification engine also assists with storage requirements related to compliance. It includes built-in policies for many well-known and widely used data privacy frameworks.

The Data Visibility product from Forcepoint serves a similar purpose. It finds, classifies and categorizes unstructured information, regardless of its current storage location. Users can then apply other Forcepoint offerings to prevent data exfiltration. The company indicates the tool’s AI automation features for data management have better than 95% accuracy across 70 fields. Also, performance should improve over time because the algorithms learn through use.

Use AI to Meet Data Storage Requirements and Enhance Management

The examples here show why machine learning and AI can become powerful tools for adhering to data storage requirements within an organization or making it easier for people to find the information they need. Finding the best solution and implementing it takes time and effort, but the results are often long-lasting and worthwhile.

Emily Newton is the Editor-in-Chief of Revolutionized Magazine. She has over three years experience writing articles for the tech and industrial sectors. Subscribe to the Revolutionized newsletter for more content from Emily at https://revolutionized.com/subscribe/
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