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|---Module:text|Size:Small---| The consolidation of cloud computing platforms, combined with analytics and artificial intelligence solutions, is redefining the way organisation’s structure information, scale analytical capabilities and integrate technology into their operating models. In a business environment shaped by market volatility, competitive pressure and increasing regulatory demands, this evolution has clearly become strategic.
In an interview with Executive Digest, Tiago Simões Director of Data & Analytics Solutions at Celfocus, analyses this journey, the main challenges associated with the adoption of these solutions and the trends set to shape the coming years, while sharing his perspective on the role of cloud in value creation, operational efficiency and executive decision-making.
Here’s the full interview transcription:
The adoption of Data & Analytics solutions has become a key pillar in the digital transformation strategies pursued by organisations in Portugal.
The democratisation of access to these cloud-based solutions enables both SMEs and large enterprise groups to introduce analytical capabilities into operational environments, making businesses increasingly data-driven by placing data at the centre of decision-making. It also enables the introduction of advanced artificial intelligence models, opening the door to service innovation, improved operational efficiency and the creation of new revenue streams.
This investment and ecosystem vision around Cloud Data & Analytics should be seen as a competitive enabler for businesses.
A cloud or hybrid data platform ensures scalability, processing power, efficient cost management and essential security controls, enabling teams and departments to gain rapid access to consolidated data. This accelerates the time-to-market for new use cases, improves decision-making and enables organisations to analyse data or activate AI agents capable of autonomously executing tasks with measurable business impact.
The use of cloud platforms simplifies the continuous processing of data streams, enabling immediate action.
This streaming data processing can be supported through event-driven architectures using technologies such as Apache Kafka for real-time processing, or through data lakehouse solutions capable of combining streaming and historical data within the same platform. In both cases, scalability is ensured by the cloud infrastructure, enabling organisations to automatically handle fluctuations in event volumes without requiring additional configuration.
Highly regulated sectors such as banking and insurance face the additional challenge of ensuring compliance with both cross-industry regulations, such as GDPR and the AI Act, and sector-specific frameworks including DORA, Basel and Solvency regulations. Alongside regulation, data sovereignty has also become a critical concern. This has led many organisations to adopt hybrid strategies, ensuring that while public cloud services are used, critical data remains hosted locally or within certified private cloud environments.
Another key consideration is the definition of multi-cloud or cloud exit strategies to address business continuity and resilience requirements while avoiding vendor lock-in. These strategies may increase architectural complexity through the adoption of cloud-agnostic technologies rather than relying exclusively on native cloud services.
To avoid unnecessary data accumulation, organisations should apply the same principles long recommended for on-premises analytics solutions: define the business problem to be solved, identify the data required for analysis and only bring that specific data into the platform.
To manage this process sustainably while avoiding unnecessary duplication, organisations should consider adopting a data product strategy, with clear definitions regarding content, ownership and cross-functional consumption. This approach enables stronger control over data quality and actual usage, supported by objectives and metrics such as KPIs and OKRs, making it possible to identify products that generate little or no business value.
The competitive advantage generated by these technologies is largely driven by agility and execution speed embedded within business processes.
AI-driven forecasting, personalisation and large-scale simulation capabilities can have an immediate business impact when supported by high-quality data and the right cloud infrastructure.
Examples include the real-time generation of personalised offers for individual customers, automated price adjustments based on internal or external information collected by AI agents, or simulations assessing the impact of changes in logistics processes to identify the most effective operational configuration for current business conditions.
These solutions allow C-level executives to make genuinely data-driven decisions by providing real-time access to key metrics, AI-based projections and simulations, and greater confidence in the information available.
These platforms also ensure that information is consistently shared across the organisation using the same business rules. As a result, both operational teams and executives rely on the same source of truth for analysis and decision-making.
Security and data governance are critical in fully or partially cloud-based data ecosystems. Roles and responsibilities regarding governance and security must be clearly defined, starting with the shared responsibility model: cloud providers are typically responsible for the physical security of hardware and foundational platform components, while organisations remain responsible for configuration, access management, data protection and secure usage.
Companies must ensure robust identity and access management, data protection and traceability, including encryption and anonymisation strategies, as well as the definition of data catalogues with sensitive data classification. Resilience plans should also be established, potentially including backup strategies and data portability approaches.
However, responsibility extends across the organisation, making data and infrastructure security literacy essential for all employees.
Organisational culture may, in some cases, be preventing companies from achieving the expected return on the significant investments made in analytics and AI solutions over recent years.
To unlock this potential, organisations must ensure that decisions are driven by data rather than intuition or legacy habits. This mindset shift must extend across the entire organisation, starting at executive level. It is equally important to improve data literacy and promote the sharing and consumption of data products across departments.
Another critical factor is addressing the uncertainty or resistance that these technologies may create. This requires demonstrating clear business value through concrete use cases and quick wins, securing executive sponsorship, creating incentives aligned with data usage and investing in tailored training programmes. In this way, technology becomes an enabler that frees employees to focus on higher-value activities.
Data architectures are evolving towards making the physical location of data transparent to users while ensuring a single and consistent access layer for each business concept provided by enterprise platforms.
The creation of enterprise semantic layers will effectively mask the complexity of hybrid and multi-cloud environments while ensuring the consistency required for business teams to consume and analyse data effectively.
It is also important to highlight the shift towards data product strategies, where each department assumes ownership of its own products while central data teams govern the overall platform through a federated model.
These solutions will place increasing importance on FinOps, observability and orchestration capabilities, progressively supported by AI agents capable of optimising cloud consumption based on business context and workload requirements.
We still see very different levels of maturity, ranging from organisations building consolidated cloud-based data ecosystems to companies that could already be considered AI-native.
The organisations leading this transformation are typically those operating under greater competitive pressure or stricter regulatory requirements, particularly within banking, insurance, energy, retail and telecommunications.
One of the most immediate developments will be the democratisation of data access and analysis through natural language interfaces, enabling business users without technical expertise to perform data analysis tasks that currently require SQL or Python knowledge.
At the same time, we will see the widespread adoption of Agentic AI, with autonomous agents becoming embedded within business processes, moving beyond support functions to actively executing and optimising operational activities.
Over a longer-term horizon and in specific use cases, access to quantum computing through cloud providers may also gain relevance, particularly within areas such as pharmaceuticals and optimisation, accelerating the resolution of complex problems from days to minutes or seconds.
