The Challenges of Building, Deploying, and Maintaining Cloud-Based Data & AI Platforms

As organisations increasingly rely on data to drive decision-making, enhance customer experiences, and improve operational efficiency, many are investing in Data & AI platforms to unlock the full potential of their data. These platforms can provide transformative insights, enable predictive analytics, and support AI-driven innovations. However, building, deploying, and maintaining such platforms is not without its challenges. Many businesses struggle with the complexities involved in managing these systems effectively.

Here, we’ll explore the major challenges organisations face in this space and how they can be addressed.

Data Integration and Silos

One of the most significant challenges organisations encounter is the integration of diverse data sources into a unified platform. According to a study by McKinsey, many companies have large, fragmented data environments, with data siloed across departments, systems, or geographies . This makes it difficult to access and analyze data holistically.

Siloed data creates inefficiencies, as teams spend valuable time searching for information, reconciling differences between datasets, or simply not utilizing data to its full potential. Without seamless integration, organisations cannot fully realize the potential of their AI models or data analytics.

Solution: Implementing a comprehensive data governance strategy and integrating data pipelines can help reduce silos. Leveraging cloud-native tools and APIs for smooth data integration is crucial to overcoming this challenge.

Talent Shortages and Skill Gaps

Another major obstacle is the lack of skilled professionals who can design, build, and maintain complex Data & AI platforms. According to a Gartner report, there is a global shortage of data scientists and AI specialists, and this talent gap is expected to persist for years to come .

As AI and data science are rapidly evolving fields, even existing employees may struggle to stay up-to-date with the latest technologies, frameworks, and methodologies. This skills gap often leads to organisations being unable to fully leverage their Data & AI investments.

Solution: organisations can address this challenge by investing in talent development and upskilling programs, as well as exploring partnerships with third-party managed services providers or leveraging cloud-native AI tools that abstract some of the complexity.

Security and Compliance Risks

Data security remains a top concern for organisations deploying cloud-based platforms. With sensitive data being transferred, processed, and stored in the cloud, the risk of breaches or data leaks increases. According to Forbes, cloud misconfigurations were responsible for 19% of data breaches in 2022 , highlighting the need for stringent security measures.

Additionally, many industries are subject to strict regulations such as GDPR, HIPAA, and other data protection laws. Ensuring compliance while maintaining agility in cloud-based systems is a delicate balance that organisations must manage.

Solution: organisations need to adopt a security-first approach, implementing encryption, access controls, and robust monitoring tools. They should also build platforms with compliance baked in, ensuring that regulations are adhered to from the outset rather than retrofitted.

4. Cost Management and Optimization

Cloud services offer significant flexibility and scalability, but they can also lead to ballooning costs if not carefully managed. Many organisations experience “cloud sprawl,” where multiple teams spin up services without central oversight, leading to inefficiencies and unnecessary expenses. In a report by McKinsey, organisations estimate that 30% of their cloud spend is wasted .

Additionally, optimizing performance while keeping costs under control can be challenging, especially in data-intensive industries like finance and healthcare.

Solution: Implementing cloud governance frameworks that include cost monitoring, resource allocation, and budgeting controls is essential. organisations should also consider using automated tools that provide cost optimization recommendations and leverage AI for predictive resource scaling.

5. Scalability and Performance Issues

Building a scalable platform that can handle increasing amounts of data and users without sacrificing performance is a critical challenge. According to Gartner, many organisations fail to plan for the future when designing their platforms, leading to performance bottlenecks as the platform grows . Poor scalability can result in delays, downtime, and loss of business opportunities.

AI workloads, especially those that involve machine learning model training and real-time analytics, can be particularly resource-intensive, further complicating scalability efforts.

Solution: Building platforms with scalability in mind from the outset is crucial. organisations should leverage cloud-native architectures, which allow for the dynamic scaling of resources based on demand. Additionally, incorporating microservices and containerization can help ensure that platforms can grow without hitting performance ceilings.

6. Keeping Pace with Innovation

The rapid pace of technological innovation in the Data & AI space can be overwhelming for organisations. New frameworks, algorithms, and tools are constantly being introduced, and staying on top of these innovations is a full-time job. Falling behind on the latest developments can lead to outdated models, inefficient processes, and missed opportunities.

In fact, a study by McKinsey found that companies who adopt AI and advanced analytics early tend to outperform their competitors by wide margins . But keeping up with this innovation requires continuous investment and agility.

Solution: organisations need to build an agile Data & AI strategy that allows them to incorporate the latest innovations while minimizing disruption to operations. This can involve adopting a modular architecture, using managed services that stay up-to-date with technology trends, and investing in research and development.

7. Operational Complexity

Finally, the operational complexity of maintaining a Data & AI platform can be overwhelming, especially as platforms grow in size and scope. Data pipelines, machine learning models, and analytics dashboards require continuous monitoring, updates, and troubleshooting. Without effective management, performance degradation or system failures can occur.

Forbes highlights that many organisations underestimate the resources needed to maintain these platforms post-deployment , leading to underperformance or even system outages.

Solution: Automation and monitoring tools are essential for managing operational complexity. organisations should also consider outsourcing some or all of the maintenance tasks to managed service providers, allowing internal teams to focus on more strategic initiatives.

Conclusion

Building, deploying, and maintaining cloud-based Data & AI platforms offers immense potential for organisations, but the challenges involved are numerous. From data silos and skill shortages to security risks and cost management, companies need to be proactive in addressing these hurdles to ensure long-term success.

By developing a comprehensive data strategy, leveraging cloud-native tools, and staying ahead of industry trends, businesses can unlock the full potential of their data and AI investments while mitigating risks. For many, partnering with experts or adopting managed services may be the key to overcoming these challenges and staying competitive in the digital age.

See how our Insight Factory solution solves this problem for clients around the world

See how our Insight Factory solutions solves this problem for clients around the world.