As technology continues to advance, so does the amount of data generated on a daily basis, both internally – through marketing, sales, production, and finance, among others – and externally, from sources like the internet of things.

Storing and analysing all this data requires a dedicated system that can integrate a variety of data types, as well as provide information and insight. Not all traditional, on-site data warehouses are still up to the task. Because of this, cloud data warehousing has emerged, which has opened doors for organisations of all sizes and types.

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Like a traditional data warehouse, a cloud data warehouse is a computer system dedicated to storing and analysing data to find patterns and correlations that lead to information and insight. Data warehouses also store and integrate data from multiple sources in varying formats. However, cloud data warehousing is based in the cloud, not in a traditional, on-premise location, and as such can be bought from and managed by a vendor in an as-a-service product.

With that in mind, here are four business benefits of cloud data warehousing.

1: Cloud data warehousing meets current and future needs

Flexibility is an important factor of cloud data warehousing. Organisations will have the ability to scale compute and storage independently, pending the company’s needs. So, if a business needs more storage today, they won’t also be forced to add more compute. But if their situation changes in the future, they can adjust however needed.

2: Data is accommodated and integrated in one place

With the help of data analytics, semi-structured data has the capability to provide next-level insights beyond what traditional data can provide. But semi-structured data must be loaded and transformed before an organisation can analyse it—this is a process most traditional data warehouses can’t handle, but one that cloud data warehouses can.

This ability to support diverse data without performance issues ensures all of an organisation’s data can be loaded and integrated in one location. This not only increases flexibility, but it also means all data can be managed and maintained in one system, reducing costs.

3: Cloud data warehousing saves money

Between licensing fees, hardware, set-up, management, securing and backing up data, and more, conventional data warehouses can cost millions. Not to mention building one that can hold the variety and volume required by today’s standards ups the cost even more.

However, using cloud data warehousing as a service (as it is commonly used today) can cut costs significantly, while keeping all the same features. Relying on service providers to maintain systems and only purchasing the amount of support needed helps organisations stretch their budgets further and avoid paying for unnecessary features.

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4: Data is secured at rest and in transit

An important aspect to analysing and storing data is keeping that data safe. In addition to the use of vendor-conducted penetration tests to check for vulnerabilities in the system, modern data warehouses do this through confidentiality and integrity measures.

Confidentiality practices prevent unauthorised access to data, and are usually done through role-based access control, which only allows those permitted to access the data to do so, and multi-factor authentication, which requires users to enter a code (usually one sent to a mobile phone) and ensures a stolen username and password can’t be used to access the system.

Integrity measures guarantee data isn’t modified or corrupted, and entails the use of encryption practices and encryption keys to protect data from unauthorised prying eyes.

It takes a year and almost £3 million pounds to contain the average data breach, according to a 2018 study by the Ponemon Institute. And despite growing cloud adoption, many IT professionals still highlight the cloud as the primary area of vulnerability within their business.

To combat this and lower their chances of experiencing a breach, some companies are turning to AI and machine learning to enhance their cloud security.

AI, or artificial intelligence, is software that can solve problems and think by itself in a way that’s similar to humans. Machine learning is a subset of AI that uses algorithms to learn from data. The more data patterns it analyses, the more it processes and self-adjusts based on those patterns, and the more valuable its insights become.

While not a silver bullet or a panacea, this approach shifts practices from prevention to real-time threat detection, putting companies and cloud service providers a step ahead of cyber attackers. Here are some of the benefits.

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Big Data Processing

Cybersecurity systems produce massive amounts of data—more than any human team could ever sift through and analyse. Machine learning technologies use all of this data to detect threat events. The more data processed, the more patterns it detects and learns, which it then uses to spot changes in the normal pattern flow. These changes could be cyber threats.

For example, machine learning takes note of what’s considered normal, such as from when and where employees log into their systems, what they access regularly, and other traffic patterns and user activities. Deviations from these norms, such as logging in during the early hours of the morning, get flagged. This in turn means that potential threats can be highlighted and dealt with in a faster fashion.

Event Detection and Blocking

When AI and machine learning technologies process the data generated by the systems and find anomalies, they can either alert a human or respond by shutting a specific user out, among other options.

By taking these steps, events are often detected and blocked within hours, shutting down the flow of potentially dangerous code into the network and preventing a data leak. This process of examining and relating data across geography in real-time enables businesses to potentially get days of warning and time to take action ahead of security events.

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Delegation to the Automation

When security teams have AI and machine learning technologies handle routine tasks and first level security analysis, they are free to focus on more critical or complex threats.

This does not mean these technologies can replace human analysts, as cyber attacks often originate from both human and machine efforts and therefore require responses from both humans and machines as well. However, it does allow analysts to prioritise their workload and get their tasks done more efficiently.