Data analytics is a comprehensive solution for enterprises to convert their data into a valuable asset. Here, we’ll discuss the analytics as a service (AaaS) model and explore how a business can benefit from adopting this model to make informed data-driven decisions.
In today’s data-driven world, a business cannot afford to ignore the advantages of leveraging data and insights to boost its revenue and enhance the customer experience. Data is not just a by-product but an asset to every organization. By using the latest data analytics and business intelligence tools, you can unlock the true potential of your business data and make informed decisions at all levels. Data analytics is no longer optional but a necessary part of all industries.
According to Fortune Business Insights, the global data analytics market was $64.99 billion in 2024 and is expected to be $82.23 billion in 2025 with a projected CAGR (compound annual growth rate) of 25.5% to reach $402.70 billion by 2032. The same report shows that most industries have adopted data analytics in some form. The IT industry has the largest market share at 20% with healthcare, BSFI (banking and finance), retail, eCommerce, manufacturing, transport, and logistics (supply chain) also having prominent shares.
There are various ways to integrate analytics into your business. Cloud-based analytics as a service (AaaS) has gained popularity for its cost-efficiency and ease of use in creating self-service systems. But what does analytics as a service mean? How does this delivery model help your enterprise in 2025?
Let’s find out in this blog.
The analytics as-a-service model is a cloud-based solution where the related IT infrastructure, tools, and applications are hosted on the vendor’s server. Businesses pay for using these tools and the relevant services provided to set up the connections and troubleshoot the systems when necessary. Analytics as a service is also known as managed analytics as a service of BI as a service (BIaaS).
Simply put, analytics as a service (AaaS) is a subscription-based model where you hire specific or end-to-end data analytics solutions from service providers. You use the resources, tools, technologies, and expertise of the service providers to derive meaningful analytical insights for decision-making. The data analytics platform is hosted on a cloud like Azure, AWS, or Google Cloud. The experts will integrate the tool with your existing data systems to provide insights through the dashboards.
But what if you want to revamp your systems?
The same data engineering company will provide end-to-end solutions to streamline data flow and connections between different tools to create a flexible and scalable IT infrastructure on the cloud or on-premises.
Infrastructure as a service (IaaS) in cloud computing is a preferred choice for many organizations as it reduces the need for heavy on-premises hardware and migrates all major systems to the cloud. This allows your employees to work remotely and collaborate with others from different regions. Additionally, cloud services are future-proof and can be easily upgraded or downgraded to suit your needs. You only pay for the technologies and server space you add to your business account.
The hosting, licensing, and other aspects are managed by the service provider. Popular business intelligence platforms like Power BI and Tableau can be used as web applications by integrating them with Azure PaaS services or other cloud-based solutions. PaaS stands for Platform as a Service, where the data analytics tool is hosted on a remote cloud and accessed by employees authorized to use it as part of their job.
When you opt for end-to-end analytics as a service delivery model, you hand over the responsibilities of data collection, data migration, ELT/ ETL, data warehousing/ data lake, data analytics, data visualization, and support services to a third-party offshore provider like a data engineering company and spend your resources on your core functions.
But why should you choose analytics as a service to build a data-driven business model? Check out the benefits of using AaaS for enterprises:
By hiring a service provider to build, deploy, and manage the data-driven model, enterprises can reduce the pressure on internal teams and allow them to focus on growing the business. There’s no need to hire more employees on the payroll to take up the additional work. Talent gap issues can be addressed without spending more money on recruitment and training. This reduces the workload on HR teams.
By hiring a data warehousing company for AaaS solutions, an organization can ensure its limited resources are not distributed among various demands. The service providers use their own resources to deliver the desired outcomes for businesses. In exchange, you pay the company for the services it provides. This will prevent budget concerns and a lack of availability of resources for important projects. You can use analytical insights and have a competitive edge without compromising other departments or growth areas.
Building, maintaining, and regularly upgrading the AaaS framework is not an easy task. It requires domain-specific expertise as well as knowledge of the latest tools and technologies. Moreover, you should know exactly which tool to choose based on your current situation and future prospects. Taking up such a complex project entirely on your own and working on it from scratch is highly risky. A mistake could cause losses in millions, as well as excess consumption of other resources and delays. All these risks can be minimized by partnering with a service provider.
As stated in the previous point, the greater the risk, the higher the possibility of monetary loss. Even large enterprises and multinational firms have to deal with budget restrictions. Analytics as a service is a cost-effective solution as it reduces the need for extensive research and development and in-house systems. You can pay for customized infrastructure as a service (IaaS) solutions to use a robust cloud-based IT infrastructure to run your business systems. This also reduces the need for replacing hardware and upgrading data storage centres periodically.
A common concern for most businesses is the talent gap when they have to adopt new technologies. The question of whether to outsource, recruit new employees, or train existing ones becomes a tricky situation. That’s where analytics as a service can help. A third-party service provider offers access to expertise while providing basic training for the employees to use the technologies for day-to-day activities. It’s a win-win situation, more so since you don’t have to spend time and money on recruitment.
Businesses don’t always stay the same. As you attract more customers and increase the market share, you will have more transactions and activities. This means an increase in internal data. The data management systems have to accommodate these developments. In short, you want a data-driven model that can be scaled as required. Yet again, data lake services and AaaS solutions rise up to the challenge. The service providers will scale the systems as and when required. Moreover, the pay-as-you-go model ensures the overhead costs don’t increase drastically.
Imagine waiting for days and weeks to access the insights for a query. In our fast-paced world, a business cannot survive for long if it uses such outdated analytical methods. Everything has to happen quickly, in near real-time or real-time. Employees should get a response to a query in minutes, if not seconds. For this, you need advanced and powerful analytical tools. These are built on AI and ML algorithms and drive critical decision-making. Since the entire process is managed by the service provider, AaaS ensures real-time analytics and visualization reports through customized dashboards.
What works for a competitor may not deliver the same results for your business. Customization and tailored solutions are a must for an organization to achieve its goals. Moreover, every department in the business uses the same analytical tool differently. For example, the insights required by the sales department and the finance department will be different. Analytics as a service makes it easy to customize the dashboards based on these requirements and KPIs. With the data and tools hosted on cloud servers and the process managed by expert service providers, the business can be assured of quick and relevant insights.
Customer satisfaction and experience are among the key reasons a business invests in new technologies. Analytics as a service promises exactly that by creating reliable systems that enhance overall efficiency and performance. For example, data lake consulting services for data and analytics management can result in a robust and secure data architecture offering real-time actionable insights. This results in happy employees, customers, and stakeholders. Your internal and external customers will find it rewarding to be a part of your business. Eventually, it increases your brand reputation and accelerates success.
Analytics as a service (AaaS) provides one-click analytical reports and insights by giving access to advanced AI and ML-based analytical tools. You can integrate the tools with SaaS, PaaS, and IaaS applications in a quick time by partnering with an experienced service provider. Hire data engineering consulting services to set up a robust, flexible, and scalable AaaS framework in your enterprise and derive accurate insights for real-time decision-making. Revamp your business by adopting the data-driven digital-first approach to achieve your goals and boost revenue.
Data analytics is a key component for a business to grow and expand in competitive markets. The actionable insights provided by analytical reports streamline business processes, reduce unwanted expenses, enhance customer experience, and increase ROI. Cloud-based data analytics organizations gain a competitive edge and strengthen their brand reputation in the market.
Read the links below to learn more about data analytics solutions.
You can use the AaaS framework to reduce the overhead and analytics costs by using existing cloud-based systems, tools, and technologies instead of building the whole thing from scratch and maintaining it on-premises. This eliminates the need for an initial large investment. Moreover, the subscription-based model allows you to pay for the services you use.
Yes, you can still benefit from AaaS even if you already have an internal data team. That’s because, with analytics as a service, you gain access to the expertise of professionals from outside your business. They not only bring a fresh perspective but also help in making the systems more scalable and introduce new technologies to your team.
The following are some key benefits of switching to an AaaS delivery model this year:
Analytics as a service makes it easier to use advanced tools and derive real-time insights.
The best way to ensure data privacy and compliance when using AaaS platforms is by conducting regular audits to identify weak areas. This helps in strengthening the data governance framework. The audits can be conducted by internal teams or by the service providers.
Yes, AaaS can give faster insights as the platform is hosted on a cloud server. You don’t have to build everything on your own. Existing tools and apps are customized and integrated with your data lakes and other systems to send real-time insights to the dashboards. It is also less expensive.
Consider the following factors when you select an analytics as a service provider:
Have in-depth discussions with the service provider and choose the one that understands your requirements and provides tailored solutions that align with your objectives.
Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer
