What is Analytics as a Service (AaaS)? Benefits and Challenges

Business Analytics Technology

Every organization today wants to harness the power of big data and draw deeper insights to make better decisions. Businesses are aware of the advantageous position data analytics can put them in and give them a competitive edge in the market. But despite that, the sheer volume and complicated nature of data can intimidate organizations, making it difficult to unlock its full potential. The only drawback of analytics is the high cost of investment associated with on-premises software, which must be endured by organizations. Analytics as a Service offers its users all the capabilities of on-premises analytics software but eliminates the expenses associated with it.

Businesses are constantly seeking new ways to extract insights and make informed decisions that are empowered by data. Analytics has become critical in understanding customer behavior and driving business growth. This is where Analytics as a Service (AaaS) steps in, offering a comprehensive solution to dig deeper into their data.

Understanding Analytics as a Service:

Analytics as a Service (AaaS) refers to the outsourcing of data analytics tools to an external company that provides analytics based on pay as needed basis. Businesses are not required to build and maintain an in-house analytics infrastructure from scratch and commit to huge long-term investments. They can easily hire companies to run analytics on their data and significantly reduce their overall investment without missing out on the plethora of benefits. It assists businesses of all sizes to access highly advanced analytics capabilities for a small fraction of the price. This service has created a wider reach and access to big data analytics and business intelligence.


  1. Cost Effective: Companies can do away with any substantial upfront investments in hardware, software and data specialists. Instead, organizations only need to pay for the services they require, which lowers costs and can potentially make monthly costs predictable. It provides smaller businesses and startups with an opportunity to gain access to advanced analytics tools and expertise that would have otherwise been out of reach.
  2. Scalability: Analytics as a Service offers no constraints to the growing data volumes in the ever-changing business landscape. It is massively scalable because the analytics is based on the cloud and can leverage its scaling properties. Additionally, if an organization wants to experiment with new analytics techniques without making any substantial long-term investments in it, analytics as a service is beneficial in such cases.
  3. Leverage Expertise: An organization that offers analytics as a service employs experienced professionals who possess the skills to handle complex data analysis tasks effectively. Companies that cannot afford to hire and train an entire in-house team can benefit from the knowledge and expertise of external analytics professionals. Hence, companies can concentrate on their strategic projects and key competencies while leaving the analytics workload to the experts.
  4. Faster Implementation: The general architecture of AaaS systems is cloud-based, facilitating rapid deployment that speeds up the processes. Organizations can rapidly implement analytics on their data and overcome the need for large-scale infrastructure setups or software installations that are highly work-intensive and expensive.
  5. Advanced Analytics: With Analytics as a Service, users would have access to a wide range of analytics tools and techniques which includes machine learning, sentiment analysis, predictive analytics and data visualization, among others. Once organizations have such powerful tools at their disposal, gaining deeper and more accurate insights from their data is easier.


Even though analytics as a service offers a wide range of benefits, it is not without its challenges. To ensure the successful implementation of analytics on their datasets, organizations have to navigate through an array of complex processes.

  1. Data Security – The use of analytics in an organization poses the risk of exposing sensitive information. The data processed on the cloud is vulnerable to unapproved access, data breach, etc. Therefore, businesses need to carefully look at the security measures offered by the Service provider. There may be a need to implement additional security measures.
  2. Performance Issues – When working with massive datasets, latency or delays in processing may set in. Transferring data from the cloud and back may take significant amounts of time and so, companies may need to invest in optimizing data transfer processes and high-performance software.
  3. Data Integration – For analytics as a service to work on the cloud, the data which may be stored in many different formats needs to be integrated. Before any data is analyzed, it must be first cleansed, standardized and transformed.


It is becoming increasingly evident that businesses may increasingly opt for analytics as a service for the vast benefits it provides. With the emergence of an “as a service” business approach to analytics, smaller businesses and startups can now be on a level playing field with established firms and have a chance to compete. As the demand for data-driven insights continues to grow, Analytics as a Service will undoubtedly play a crucial role in shaping the future of business analytics.

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