Business Intelligence in the Cloud
“By 2026, 75% of organizations will adopt a digital transformation model built on the cloud as a foundational platform.” Gartner
This figure reflects a profound shift: the end of on-premise BI as the default standard. Discover everything you need to know about this revolutionary approach to computing.
For years, on-premise Business Intelligence was the uncontested norm. Hosted locally and deeply integrated into internal infrastructures, it gave companies complete control over their data — a key advantage at the time.
But today, the landscape has shifted. Data flows continuously from countless sources: IoT, ERP/CRM, MES (Manufacturing Execution Systems), business applications, and operational platforms. Decisions must now be made in real time, often collaboratively, in a dynamic and highly competitive environment.
For CEOs, CIOs, CFOs, and CMOs, the challenge is clear: gaining a reliable, immediately actionable view of the business. Waiting weeks for a customized report is no longer acceptable. Decision makers need the ability to explore, analyze, and act without depending on IT at every step.
In this context, cloud BI is not just a technological evolution; it represents a true paradigm shift. By moving to the cloud, organizations embrace a more flexible, faster, and connected approach. They discover a model where infrastructures automatically scale with demand, scalability becomes standard, and decision-making tools are accessible at every level of the organization.
This transition unlocks numerous benefits but also raises critical strategic questions: How should data governance be rethought? How can security be ensured in a distributed environment? How can the “black box” effect often attributed to cloud solutions be avoided?
In this article, we guide you through the heart of this major transformation. Our goal: to understand the foundations, challenges, and opportunities of cloud BI to adopt it effectively and, most importantly, to leverage it to its full potential.
1. From On-Premises BI to Cloud BI : understanding the transition
1.1 The Limitations of the On-Premises model in today’s context
For decades, on-premise infrastructures have formed the backbone of BI systems. Hosted in internal data centers, they provided full control over data, systems, and security. IT teams built custom architectures, combining data warehouses, complex integration processes (ETL), and tailored reports. This robust approach allowed organizations to base their decisions on solid foundations.
For further insights, read our previous article on On-Premises BI, where we explore its heritage, local power, and the inherent limitations of this model.
Yet, in an era of exploding data volumes and where rapid analysis has become a key competitive factor, this model shows its limitations. Imagine a marketing manager waiting weeks for a customized report, with every request routed through the IT team. These delays hinder the agility needed to seize opportunities quickly. Deployments are long, expensive, and scaling is challenging. Every new data source or business requirement often demands heavy development and constant technical intervention.
Faced with these constraints, the cloud emerges as a flexible, scalable, and on-demand solution.
1.2 The Cloud : a technological and business revolution for BI
What is Cloud Computing?
Cloud computing is a technology that allows organizations to store data, run applications, and leverage computing powerall via the Internet. Whether it’s storage, big data processing, networking, or advanced analytics, these resources are hosted and managed by specialized providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform.
The term “cloud” refers to the interconnection of multiple devices located in different places. The shape of this vast network resembles a cloud.
The cloud meets today’s demands by offering ready-to-use platforms capable of handling massive volumes of data from diverse sources in real time: IoT devices, SaaS applications, social networks, ERP systems, and more.
Beyond technology, the cloud also transforms business practices, promoting user autonomy, cross-departmental collaboration, and faster decision-making..
Netflix: Cloud-Scale Business Intelligence
Netflix perfectly illustrates a successful transition to the cloud. Faced with exponential growth in data generated by millions of users, the streaming giant migrated its infrastructure to AWS (Amazon Web Services). This move enabled:
- Dynamic scalability: Instantly adjusting resources to handle massive traffic spikes, for example, during the release of a new series.
- Integration of new data sources: Combining viewing data, user interactions, and feedback to enrich analyses.
- Agile and collaborative BI tools: Providing marketing, editorial, and operational teams with real-time dashboards and insights.
During the launch of hit series, Netflix could analyze viewing behavior in real time, adjust personalized recommendations, and optimize marketing campaigns. This ability to turn data into actionable insights demonstrates the strategic power and value of cloud BI, not only for operational performance but also for the company’s growth and competitiveness.
Moreover, cloud solutions have made significant progress in security and compliance, reassuring organizations that were previously concerned about the risks of outsourcing their data. The cloud also enables cost optimization by scaling resources on demand, avoiding heavy and rigid investments.
Thus, cloud BI is more than a mere technical outsourcing, it addresses business needs for agility, collaboration, and continuous innovation. Understanding this transition is essential to seize its opportunities… and to anticipate its challenges.
Understanding Cloud models to choose the right strategy
For many organizations, moving to the cloud is no longer a question ; the real challenge is deciding which cloud model to adopt. Behind this single term lie several architectures, each with its strengths, limitations, and best-fit business applications. Here are the four main models you need to know to build a cloud strategy aligned with your objectives.
Public Cloud : agility and scalability at lower cost
Shared and secure, the public cloud (Azure, AWS, Google Cloud, IBM Cloud) provides rapid access to powerful resources without heavy investments. Ideal for accelerating projects and handling activity spikes.
Private Cloud : maximum control and security
Fully dedicated to a single organization, the private cloud offers complete control over configuration and security. Suited for regulated industries or environments requiring fine-tuned customization.
Hybrid Cloud : the best of both worlds
Combines public and private clouds to run critical workloads privately while leveraging the public cloud’s power for peak demand. Useful for meeting compliance requirements such as GDPR.
Multicloud : strategic diversification
Combines multiple clouds from different providers without centralized management. Allows distribution of applications and data based on performance, location, or regulatory constraints, while reducing dependence on a single vendor.
In summary: There is no “perfect” cloud, only the one that aligns with your strategy, data, and objectives, balancing performance, security, cost, and flexibility.
Types of Cloud Computing services
Cloud computing is divided into three main service categories: SaaS, PaaS, and IaaS. Some providers offer these services in combination, while others provide them independently.
2. The technical pillars of Cloud BI
Cloud Business Intelligence is not just about moving tools to the cloud. It relies on deeply reimagined technical pillars, designed to deliver agility, performance, and interoperability in an ever-evolving industrial landscape.
2.1 On-demand scalability and performance: Infrastructure that adapts to the pace of Data
Imagine a connected factory where data continuously flows from IoT sensors, and every minute counts to anticipate a failure. With an on-premise infrastructure, managing these activity spikes can quickly become a technical and financial headache.
In an on-premise environment, every peak required oversized infrastructure, installed and maintained locally. In the cloud, scalability becomes elastic: resources automatically adjust to the load, whether it’s processing a sudden surge of IoT events or running analyses across multiple years of operational data.
Thyssenkrupp + Azure: a winning alliance for smart maintenance
This is precisely the success story of Thyssenkrupp, a major player in the elevator, materials, and engineering industries. By migrating its analytics systems to Microsoft Azure, the group was able to:
- Centralize data flows from hundreds of factories,
- Optimize predictive maintenance through a consolidated view of machine performance,
- Provide technicians with real-time dashboards, enhancing operational responsiveness.
In practice, when a sensor detects an anomaly on critical equipment, an alert is instantly generated in the cloud. This mechanism allows technicians to intervene even before a failure occurs.
-Concrete results:
- More precise and targeted interventions
- Reduced costly machine downtime
- Distributed operational intelligence at a global scale, strengthening the company’s competitiveness.
2.2 Openness, integration and autonomy: The Cloud as a catalyst for a unified Data ecosystem
Cloud BI platforms natively offer strong interoperability with business tools, industrial sensors, ERPs, and SaaS applications. Thanks to open APIs, preconfigured connectors, and built-in data pipeline tools, it becomes possible to unify historically siloed data, finance, production, logistics, quality within a single decision-making environment.
But the value goes beyond technology. The cloud also drives the democratization of data access. A production manager, without advanced technical skills, can now independently explore key indicators from production lines, quickly identify a bottleneck, and alert the relevant teams significantly reducing response times. IT still maintains governance and security, but is no longer the bottleneck.
The result is a true cultural shift: data no longer flows vertically in silos, it permeates horizontally across the entire organization. This cross-functional circulation of information has become a prerequisite for any industry striving for responsiveness, quality, and continuous innovation.
In the era of Industry 4.0 and the digital enterprise, Cloud BI is not just an enabler, it is the backbone of the future factory, where real-time data fuels smarter operations, predictive maintenance, and agile decision-making.
Spotify: A global music experience powered by the Cloud
With more than 574 million monthly active users and over 50 million tracks available, Spotify is one of the most popular streaming services in the world. To deliver a seamless and personalized experience to its users, Spotify migrated its entire infrastructure to Google Cloud Platform (GCP).
-A Strategic migration to the Cloud
In 2016, Spotify announced its decision to migrate 1,200 online services and 20,000 daily task executions to the cloud, impacting more than 100 teams. This migration freed Spotify from the constraints of managing its own data centers, allowing the company to focus on innovation and enhancing the user experience.
-Tangible Benefits of the Migration
- Scalability and performance: Handling more than 8 million requests per second thanks to GCP’s global infrastructure.
- Data analytics: Leveraging GCP’s analytics tools to gain real-time insights into user behavior.
- Advanced personalization: Using AI and machine learning to deliver tailored music recommendations.
- Cost reduction: Optimizing infrastructure spending by relying on GCP’s managed services.
-Key Figures
- Monthly active users: 574+ million
- Available tracks: 50+ million
- Requests processed per second: 8 million
3. Promises, benefits… and areas for caution
The transition to cloud BI is often presented as an obvious choice: faster, more flexible, more connected. And in many ways, these promises hold true. But every structural shift also brings new challenges. For industrial companies, it is not only about adopting a technology ; it is about aligning the entire organization with a new decision-making model.
3.1 Tangible benefits for industrial companies
The first gain is agility: production cycles are shortened, analytical environments are easier to deploy, and business users can access insights without systematically depending on IT.
The second is scalability: the cloud enables organizations to handle exponential data growth without heavy hardware investments. Whether a company adds new sensors to its production lines, integrates supplier data, or connects simulation tools, the infrastructure adapts.
Finally, cloud BI fosters collaboration. Teams can share dashboards, alerts, and real-time analyses,whether across departments or with industrial partners, suppliers, or subcontractors. This facilitates joint decision-making, a key factor in an ecosystem where the value chain is increasingly interconnected.
ChatGPT: The power of the Cloud driving AI
Behind ChatGPT’s intuitive interface lies a massive infrastructure. OpenAI, the company behind the service, spends an estimated $100,000 to $700,000 per day to keep the system running. This equates to at least $0.0003 per word generated a cost justified by the complexity and computing power required for cutting-edge artificial intelligence.
The choice of Microsoft Azure is no coincidence: Microsoft owns 49% of OpenAI and has invested $10 billion in the company. Azure provides the infrastructure needed to run high-performance computing (HPC), store enormous volumes of training data, and ensure global availability.
At the core of this architecture is the Voyager-EUS2 supercomputer, unique in the public cloud. With a performance of 39.531 petaflops, it can execute 39 quadrillion calculations per second, enabling ChatGPT to process billions of queries quickly and efficiently.
The cloud also brings flexibility and resource optimization: when demand for ChatGPT surges, Azure automatically allocates additional computing power. Conversely, during periods of lower usage, resources are released to reduce costs — offering a model that is both scalable and cost-efficient.
This partnership between OpenAI and Microsoft perfectly illustrates how the public cloud can support large-scale AI applications — combining high performance, global availability, and cost optimization. These are essential criteria for any enterprise seeking to harness AI at scale.
3.2 Key challenges to anticipate: sovereignty, security & Governance
Moving to cloud BI comes with its own set of challenges.
- Data Sovereignty : For industrial players operating across multiple jurisdictions, data location is a strategic concern. Sensitive information (production, R&D, compliance ) must comply with local regulations on storage and transfer.
- Security : While cloud providers offer advanced protocols, security remains a shared responsibility. Strict access management, systematic encryption, and continuous monitoring are critical to safeguarding mission-critical assets.
- Data Governance : Transitioning to the cloud often requires redefining roles, processes, and policies. Who has access to what? At what level of granularity? Under which standards of quality, traceability, and compliance? Without solid governance, the power of the cloud can quickly turn into decision-making chaos.
In short: the cloud delivers power, but also demands discipline. The success of cloud BI lies at the intersection of technological performance and organizational rigor.
Many communication platforms rely on the cloud. This includes email services and messaging applications such as WhatsApp.
WhatsApp : The Cloud behind global instant messaging
With approximately 3 billion monthly active users, WhatsApp is the most widely used messaging application in the world. Behind this simple interface lies a sophisticated cloud infrastructure capable of handling an astronomical volume of communications.
Since 2022, WhatsApp has introduced the WhatsApp Cloud API, a cloud-hosted version of its enterprise API on Meta’s servers, enabling businesses to communicate with their customers at scale without managing their own server infrastructure.
This evolution offers several advantages:
- Elastic scalability: Resources adjust in real time to absorb traffic spikes, for example during global events or periods of high activity.
- High availability: Geographic redundancy ensures service continuity even in the event of a regional outage.
- Enhanced security: End-to-end encryption guarantees the confidentiality of communications.
Continuous updates: Businesses benefit from new features and software updates without having to manage deployments themselves.
Thus, WhatsApp perfectly illustrates how the cloud enables a critical, secure, and always-available service to be delivered on a global scale.
The shift to cloud-based Business Intelligence marks a decisive turning point in how industrial companies leverage their data capital. It is no longer just a matter of infrastructure, but a complete reinvention of practices more agile, more collaborative, and more business-driven.
Behind the promises of scalability, accessibility, and speed, cloud BI introduces a new paradigm: data as a strategic asset. It requires a careful alignment of technology, robust governance, and a step-by-step upskilling of teams.
For industrial organizations, this shift is neither a trend nor an option. It is a powerful lever for performance, innovation, and resilience in markets that are constantly evolving.
Yet, like any profound transformation, success demands a clear vision, disciplined execution, and bold ambition : the foundations for building the truly data-driven enterprise of tomorrow.
References :
- « Analytics in the Cloud » dans un ouvrage Springer sur l’impact disruptif du cloud pour l’analytique.
- Cloud Computing : Qu’est-ce que c’est ? Comment ça fonctionne, DataScientest.
- David Ryabchikov, WhatsApp Statistics: Usage, Demographics & Growth Trends.
- Giants in the Cloud, Giga Cloud.
- Google Cloud and Spotify Expand Partnership to Help Unlock Creator Potential and Reach Fans, PRNewsRise.
- Juan Pedro Tomas, Industrial IoT case study: Thyssenkrupp transforms elevator maintenance, RCR Wireless News.
- Michael Ridder, Thyssenkrupp Rolls Out MAX in Germany : World’s First Predictive Elevator Maintenance Service, press release.
- Moe Abdula et al. — The Cloud Adoption Playbook : stratégies concrètes de migration cloud, gouvernance, culture.
- Naveen, WhatsApp Cloud API: Everything You Need to Know About It (2025), Kommunicate.
- On-premise vs cloud, Zoho.
- Piyush Singh, Spotify’s Cloud Journey, Scaling from Startup to Global Audio Leader.
- Rick Sherman — Business Intelligence Guidebook: architecture BI, intégration de données, cycles projet.
- Ruparelia, Nayan B., Cloud Computing, MIT Press, 2016.
- Zhao, Sakr, Liu & Bouguettaya — Cloud Data Management: gestion SLA, bases de données cloud, performance.
