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Modern data management

Data management describes various techniques for collecting, aggregating, securing, and analyzing data sets. Modern data management integrates emerging technology, such as AI, into data management frameworks and processes.

Traditional data management	Modern data management
Data is structured	Data can be unstructured or structured
Centered on the on-premises data center	Centered on cloud or hybrid cloud computing
Growth restricted by capital expenditure (CapEx) investments	Scales via monthly operating expenses (OpEx)
Relies on manual processes of aggregation and analysis	Utilizes advanced automation and machine learning (ML)
Limited integration functionality	Advanced integrations via APIs and microservices
Storage options limited by on-premises hardware	Near infinite storage from cloud-based databases, data lakes, data warehousing, and data meshes
Difficult to scale	Scales on demand to accommodate increasing data volumes and business needs
Does not have a comprehensive governance framework	Robust governance policies to ensure compliance and data quality

Learn more: AI technology amplifies IT efficiency and customer experience

The risks of AI and data management

Data management

Security is a core risk of data management. AI, ML, and other modern technology access sensitive data via new methods, which could create security vulnerabilities that threat actors could exploit.

Data quality is another critical concern. Inaccurate, out-of-date, or faulty data restricts even the best large language models (LLM) and compromises the quality of insights and other AI output.

Compliance and privacy

AI models learn by digesting large quantities of data. However, privacy and compliance come into play if the training data is sensitive or regulated (such as healthcare data). Sensitive data could be used to identify specific individuals, thereby violating their privacy. AI tools trained on sensitive customer information risk exposing that information.

LLMs require constant oversight, especially during training. If a customer opts out of having their data stored by a company (as required by the GDPR) and their data has already been used in training an AI model, the AI may have to be taken apart and retrained without the customer data.

Also read: Does your business network need Juniper Mist NaaS with Marvis AI?

The challenges of infrastructure for AI

When organizations try integrating AI within their existing infrastructure, they may encounter unexpected challenges. The operation of AI systems demands considerable computational resources to be effective. Additionally, companies aiming to incorporate AI into their operations must also tackle several critical issues:

• Storage – Conventional storage solutions may not provide the necessary speed and flexibility required for AI and contemporary data handling needs. Many companies are exploring on-site storage solutions to mitigate AI-related security issues; however, these may not offer the scalability offered by cloud storage or Infrastructure as a Service (IaaS) models emerging to accommodate AI’s growing requirements.
• Networking – Beyond the challenge of storing AI’s requisite data is the issue of moving, accessing, and processing this data efficiently. Organizations require substantial bandwidth and minimal latency to match AI’s high-speed processing needs.

AI infrastructure

IT service providers are now introducing “AI-ready” solutions or providing “GPT-in-a-box” capabilities, enabling organizations to deploy AI technologies quickly. These solutions come with AI-dedicated hardware that is quick, highly scalable, and adaptable, countering the previously mentioned challenges related to performance, networking, and storage.

Edge networking

Edge computing is an approach that places computing and data storage near the data sources. This type of computing decreases latency and boosts the performance of AI applications and scenarios. Proximity is especially beneficial for AI applications that require immediate processing and decision-making capability.

Cloud-native AI

Utilizing AI infrastructure based in the cloud represents a transformative method for businesses of all sizes to harness advanced AI functionalities. Regardless of the size of the enterprise, this framework is crafted to grant access to cutting-edge AI technologies without the necessity for significant investments in on-site hardware. Through cloud computing, businesses can tap into extensive computational power as needed, allowing for scalability that adjusts to their demands. This approach not only makes AI technologies more accessible in terms of cost but also enhances operational flexibility and agility. AI tools hosted in the cloud ensure they can be accessed from any location at any time, overcoming physical boundaries and fostering a truly global workspace.

Is your organization AI-ready?

Starting with a fast, secure, and scalable modernized infrastructure is critical for effectively implementing AI across all levels of the technology stack. However, selecting the right infrastructure approach that fits your needs can take time and effort. As AI capabilities continue to evolve, organizations face a crucial decision. Should they invest in an on-premises system that provides enhanced security? Or opt for a scalable or hybrid cloud solution that converts capital expenditures into monthly operating costs? One thing is certain—a reliable advisor can help your organization achieve the desired outcomes.

OnX is no stranger to guiding clients through various digital revolutions, including AI. From cloud computing to secure access service edge (SASE), OnX has assisted our clients in staying ahead of rapidly changing technologies, helping them remain competitive, profitable, and efficient.

Reach out to one of our specialists today to learn about our AI Readiness Assessment.