Cloud adoption is growing across the board. Cloud data centres processed 60% of enterprise workloads in 2019 and will process 94% of workloads by 2021. The projected cloud traffic in 2020 is 14.1 zettabytes, up from just 3.9 zettabytes in 2015.

Analytics is no exception. More and more businesses realise the cloud is the best place to run enterprise-scale analytics. They flock to cloud analytics providers to store and process data in the cloud.

Cloud service providers deliver analytics in the public cloud model, private cloud model, or hybrid model. 

In the public cloud model, clients share server space and computing resources. The model is cost-effective for small and mid-sized businesses with a limited budget. But the risks of accidental data leakage make the model a non-starter for enterprises with sensitive data.

A private cloud BI model offers dedicated storage and exclusive computing resources. The model removes the drawbacks associated with the public cloud. But it is the most costly option and does not make financial sense for most small and mid-sized businesses.

The hybrid cloud-based BI model makes a trade-off between the public and private cloud model. The model uses a private cloud for sensitive data and public cloud resources for non-sensitive data.

Regardless of the cloud model, the provider offers ready access to the latest and most advanced data analytics tools. Businesses subscribe to these tools to explore, test, and analyse data. All the cloud models offer various other benefits.

1. A Unified Approach to Data

The rapid growth of Big Data propels the surge in popularity of the cloud.

The digital ecosystem of any enterprise generates huge volumes of data by the minute. Such data accumulate fast and become obsolete just as quick.

Enterprises accumulate data from various sources. Internal data, mostly structured, come from transaction records, CRM, field service platforms, support logs, and other resources. Social media, emails, tweets, images, videos, third-party data subscription services, and other sources generate tons of external data, mostly unstructured.

Much of the enterprise data accumulates at different locations, in different formats. Effective analytics requires consolidating such disparate data, cleansing it, and making it analytics-ready.

The cloud offers a unified approach to data management, making analytics easy. Cloud-based data analytic models aggregate data into a data lake, cleanse it, and organise it. It offers the infrastructure to apply strategies to separate poor quality or irrelevant data from relevant, live data.

Many cloud data analytics providers offer a distributed data model to process data on the edge, close to where the data resides. They leverage global colocation and interconnection platforms, to connect on-premises and cloud infrastructures. This reduces latency to less than a millisecond.

2. Affordability

The benefits of analytics in a data-driven world have been apparent for many years now. Yet many enterprises did not deploy it, because it is unaffordable.

A workable analytics platform has always been a time-consuming and resource-intensive undertaking. Storing and processing Big Data required huge investments in servers, storage, and power. For many businesses, such high upfront costs made analytics unviable. Even when they had the resources, opportunity cost analysis often led to the investment going elsewhere.

The cloud converts analytics costs from capital expenditure (CAPEX) to operational expenditure (OPEX.) Businesses pay the provider only for the database, software, servers, and tools they consume. Enterprise IT does not have to spend time, effort, or resources building up such infrastructure.

The cloud ties the expenses to the analytical process, making costs transparent and affordable. Businesses can consider if the insights on offer are worth the costs.

Enterprise IT does not have to spend time, effort, or resources building up such infrastructure.

3. Speed

The lack of high-latency connections over the public internet bogs down on-premises analytics. The cloud, in contrast, enables fast and unrestricted analytics.

Leading providers offer fault-tolerant platforms with 99.999% service availability. The redundant servers spread over multiple geographies, ensure availability at any time. Users get easy access to cloud analytics through the web interface or mobile apps. Enterprise IT does not have to spend time, effort, or resources building up infrastructure. Businesses may hit the ground running.

4. Easy scale-up or scale-down

The cloud offers an elastic scale, making the analytics process agile. The business may add data storage and analytic capabilities as needed. They do not have to block resources, provisioning for peak load always. Such flexibility aids short term and ad-hoc analysis.

Elastic scale aids effective decision-making in today’s fluid and fast-paced business environment.

5. Seamless Collaboration

Analytical insights serve their purpose only when shared with other stakeholders. The cloud makes information accessible to a broader, more distributed user base.

With the database and analytics software stored in the cloud, analysts get access from anywhere in the world.

Intuitive smartphone apps and web browser interfaces facilitate seamless access. Pre-built dashboards and flexible reports aid the delivery of insights. Custom visualisation capabilities, offered by many providers, enable recipients to customise views.

6. Advanced Security

The notion of the cloud being not secure is now passé. Any credible cloud analytics provider offers advanced security. Data encryption, granular admin control, vulnerability assessment and penetration testing delivers robust security.

Among the different cloud models, the public cloud is the most vulnerable. The risk of data leaks to other clients who share the cloud servers persists. But security protocols such as voice recognition and fingerprint authentication mitigate such risks. Multi-factor authentication and encryption ensure data integrity. Redundant servers protect against accidental deletion. ISO27K standards ensure data integrity.

7. Disaster Recovery

Traditional data recovery strategies cannot overcome today’s devastating cyber-attacks. Deploying duplicate storage, servers, networking equipment, and other infrastructure is difficult and expensive. Legacy systems take very long to back up and restore anyway.

The cloud infrastructure, with its redundant servers and anytime availability, offers in-built resilience. The cloud allows the enterprise to pre-empt data loss in the eventuality of natural calamities, power outages, equipment failures, or other disasters.

The global cloud-based business analytics software market will touch $57.055 million by 2023. As the Internet becomes more widespread and there is greater convergence in the cloud space, the potential and use of the cloud will increase leap and bounds from the current levels.