Building a Connected Data and AI Platform on Cloud

The trifecta of buzzwords: data, AI, and cloud. Businesses have exceptionally high expectations from these industries today because of their potential for transformation and wealth generation. The amount of data is growing exponentially, artificial intelligence is being driven by new disruptive technologies for IT departments, and the cloud is providing the tools to manage complexity quickly.

The choice of whether to build your own data platform in the cloud is frequently nothing short of a Cornelian dilemma when the time comes to design one that can support all of your AI projects. This article examines cloud-based data and AI platform options in greater detail, examining their claims of high performance and low cost, as well as cybersecurity threats and regulatory issues.

The cloud has been a major topic of concern for IT Departments for a while now. Data projects had previously been “on-premise” architectures (i.e., data stored on company servers and thus located in organizations’ data centers) that are widely used in some application sectors such as CRM — specifically due to Salesforce’s appeal — or core business peripheral functions.

The largest cloud-related restriction is typically technological and relates to the amount of data that must be managed by data information systems (IS). Information systems often work in batch mode (i.e., batch processing of data means large amounts are processed for each task), not in real-time, and are data-intensive, meaning that there are many terabytes TB and even petabytes PB of data. However, as network capacity grows and new databases and data management systems have real-time processing capabilities, this constraint is losing some of its validity.

In actuality, our culture is what is really holding back the use of cloud computing today. For many organizations, it still feels like a large step to let their data (i.e., their war chest, perhaps all of the company’s expertise) be housed by a service provider. Another challenge is entrusting this data to a major participant in the public cloud, such as Amazon AWS, Microsoft Azure, or Google Cloud Platform.

Beyond the sensible and justified worries about the urgent matter of data security, the discussion is rapidly becoming philosophical rather than a logical argument.

The Siren-Like Call Of The Cloud For Data And AI Initiatives

The benefits of the cloud are undoubtedly vast and quite alluring, particularly in relation to data projects:

  • Cost: usage-based billing and a lower TCO (Total Cost of Ownership), particularly due to lower architectural management expenses
  • Infrastructure: scalability, flexibility, resiliency, and container management
  • Methodology: agile solutions and a lightning-fast project launch that leads to scalability.
  • Applications: a variety of open solutions (i.e., marketplace systems) are available, as well as proprietary ones that are connected to the cloud operator.

Additionally, using cloud-based artificial intelligence frameworks is destined to become standard procedure. It is difficult to argue against the fact that the most effective and efficient algorithms are those developed by companies like Google, Facebook, IBM, and Microsoft and pre-trained on millions of user interactions and photographs.

Thus, the cloud appears to be the Golden Age of Data and AI initiatives. It has been the starting point for the development of countless start-ups and their metamorphosis into unicorns (Netflix, Blablacar, and N26, are 100% digital troublemakers in the banking industry). It serves as both an innovation accelerator and a scaling-up support.

Platforms For AI And Data in The Cloud: Data Security and Protection Are Essential

But let’s avoid overindulging in technical delights by heeding the seductive call of cloud operators without giving the situation a second consideration. Data security and protection must be given great attention when moving data storage to parties outside the corporation.

The Geographical Placement of Data Is Important in Our Virtual Environment

First off, make sure the data center hosting your data is situated in Europe if the data is even the tiniest bit sensitive. Since the GDPR went into effect, this sounds like common sense, but if we want to assure effective protection for all other sorts of essential data, it should also apply to personal data as well. A resolution of the issue by the governments may result from the sovereign cloud topic frequently being on the political agenda.

The Diplomatic Conflict Between the Provisions of GDPR and The Cloud Act

Real geostrategic combat is presently going on in the regulatory sphere. While Europe works to uphold the GDPR’s protections,  two months before the GDPR went into effect the U.S. approved the Cloud Act (Clarifying Lawful Overseas Use of Data, 2018), a contentious law that clarifies the use of data abroad and calls into question the sovereignty of private information.

In particular, the wording permits access by U.S. law enforcement to information kept on U.S. providers’ servers, regardless of where those servers are physically located. As a result, you should take this circumstance into account when selecting a cloud service provider to house your data. In any instance, prior to signing a contract with a foreign cloud operator, it may be helpful to evaluate the risks involved and seek legal counsel.

Reversibility and Cloud Security: Exercising Prudence Should Always Be Based on Faith

The first thing you should do if you’re considering developing your data and AI architecture in the cloud is to make sure you have a plan for getting out. The reversibility study will help you ask all the right questions and, in the end, better use the cloud environment and solutions. It will also help you plan the last-resort action to take in the event that the cloud service is problematic or unacceptable.

Another important thing to keep in mind is that data security should never be taken lightly, and that issue needs to be dealt with when establishing your data platform, whether it is in the cloud or not. The most sensitive data must, at the very least, be encrypted, which implies that legacy data must be mapped and categorized beforehand. Organizations that are cautious can build up hybrid architectures to spread data between the cloud and local storage in their data centers, or share data across many clouds.

A Platform for Data And AI: Cloud Or Not?

The benefits of the cloud for data platforms are undeniable, and the cloud’s inherent properties are invaluable for initiatives involving artificial intelligence. Furthermore, the belief that traditional infrastructure is more secure than the cloud is merely a legacy bias

However, you should take the following safety measures before moving your data and AI platform to a cloud service:

  • Find information in Europe — perhaps in Belgium, Switzerland, or the Netherlands — based on the degree of sensitivity.
  • Thoroughly examine the regulatory, legal, and contractual ramifications.
  • Keep a very careful watch on the security of your data as it is kept and transferred around planning for reversibility from the very beginning of the project.

And there you have it, a great formula for creating a dependable Data architecture that can support all of your company’s AI activities.

It’s Time To Transform

Companies that use data to make choices stand out. They utilize insights to gain a deeper understanding of their consumers and improve their businesses to better serve them. They may also identify and capitalize on fresh possibilities faster than their competition. All of this adds up to quicker and more consistent growth. According to an Accenture study, businesses that use data and AI grow their profits 5 times higher than those that do not.

Organizations can generate more value, better understand and engage with consumers, operate more effectively, and innovate in ways that were not before imaginable with a contemporary platform and foundation of corporate data and AI, along with the correct data culture.