Analytics-as-a-Service (AaaS): Building Scalable Data Infrastructure

In today’s fast-paced business environment, companies are always looking for ways to gain an edge over their competitors. Over the last decade, companies have increasingly adopted data and analytics as an avenue to make better decisions, drive more favorable outcomes, and become more competitive in their markets. Although there are endless examples of companies leveraging data and analytics to their advantage, there is no doubt that investing in proper analytics the right way can come at a cost. 

Cloud architecture, ETL tools, BI tools, and everything else to enable modern data analytics has a price tag and elements of complexity. This is where Analytics-as-a-Service (AaaS), also sometimes known as Managed Analytics Services and BI-as-a-Service, comes into play. 

In this post, we are going to dive into what Analytics-as-a-Service is, how companies utilize it to their advantage, and some other considerations of leveraging an AaaS provider. 

What is Analytics-as-a-Service?

Analytics-as-a-Service (AaaS) is an analytics delivery model in which external vendors provide their clients with subscription-based data analytics cloud software and services. Most often, AaaS providers provide end-to-end solutions for their clients to leverage the AaaS provider’s cloud infrastructure and expertise with access to powerful analytics to help drive business outcomes. 

Just as companies like Microsoft (Power BI), Salesforce (Tableau), and Sigma have Software-as-a Service (SaaS) offerings with their BI tools - Analytics-as-a-Service providers go one step further and provide their customers with end-to-end analytics services leveraging the aforementioned SaaS platforms. This often includes the hosting, licensing, and development of the different cloud platforms necessary for an end-to-end data organization. There are some advantages to this approach and some things you should consider before leveraging managed analytics or managed BI.

How AaaS differs from traditional in-house analytics

Companies that deploy the traditional in-house analytics model procure and maintain internal resources for their cloud architecture and BI platforms. Employees are allocated to help maintain, develop, and deploy solutions within their organization. When leveraging an AaaS provider, the end client relies on their AaaS provider for the cloud infrastructure, BI licensing, and the development of data solutions.

This also means that companies using in-house analytics must hire and train employees to ensure that their in-house solutions are correctly deployed and maintained. When leveraging an AaaS model, clients are not only leveraging the tooling and infrastructure, they are also leveraging the skills and expertise of their provider. In the next section, we will dive into both the benefits and the complexities of the Analytics-as-a-Service approach. 

Advantages of utilizing Analytics-as-a-Service

There are many advantages to leveraging Analytics-as-a-Service for your organization - let’s dive into some of the most important ones:

Cost efficient

Traditional in-house analytics requires a large up-front investment in cloud resources, licensing, and hiring to enable internal analytics services. Analytics-as-a-Service provides these resources as part of the subscription, eliminating that up-front cost. 

This can be particularly helpful for small to mid-sized companies with more limited budgets. Additionally, because companies do not have to hire large teams to enable their organization’s analytics capabilities, they also save on the large training costs often needed to effectively use the different SaaS tools required for modern data analytics. 

Reduced risk

Building and maintaining complex analytics products requires a high level of expertise and intense planning for software capacities and consumption. When leveraging AaaS, companies can offload the maintenance of systems, procuring capacity for software, and the operation of their platform which transfers the risk associated with these operations to the AaaS provider. 

While there is still risk involved for the provider, the client is now free of shouldering those risks associated with analytics operations. 

Ease of scaling 

Because AaaS providers are partnered closely with cloud platform providers, they are quickly able to scale their services to meet your company’s needs. Whether it is scaling up platform capacities or scaling out analytics solutions, the scale at which AaaS providers operate gives your team more assurance of successfully scaling your analytics operations. 

Leveraging expertise 

One of the biggest challenges facing in-house data teams is the ability to rapidly hire and retain talent that has expertise not only in their industry but in the applicable tech stack as well. Analytics-as-a-service providers service clients across multiple industries and often have teams specializing in different tech stacks, allowing them to allocate resources depending on your organization’s needs. 

This is a huge advantage for clients because the providers running their analytics services have most likely seen most of their use cases and challenges before, providing for an even more efficient scaling out of analytics at the organization. 

Things to consider before utilizing Analytics-as-a-Service

While there are many advantages to using Analytics-as-a-Service for your organization - there are also some downsides to consider before implementing.

Responsiveness 

Depending on how your AaaS provider allocates resources and teams, general responsiveness and agility may be lacking when compared to an in-house team. When leveraging in-house analytics teams you can rapidly reallocate resources to meet ever-evolving business needs. Depending on the SLAs with your AaaS provider, they may not allow the same sort of flexibility and responsiveness that an in-house team can provide. 

Control of environment 

While outsourcing your cloud infrastructure and analytics can ease the administrative burden on your organization, you also don’t have the same kind of control over your environment that some businesses may desire. Whether it’s a specific process of how data products are moved between Dev, Test & Prod - or how settings on SaaS platforms are to be tuned, some of the customization available to in-house teams can be lost when utilizing AaaS.

Business-specific problems

There can be cases when companies have very industry or company-specific use cases that require intense knowledge of the specific business or industry. Additionally, sometimes sharing or hosting data externally can be in violation of company policy. In either of these use cases, utilizing in-house teams could be more efficient and effective given the unique nature of the business needs. 

Data privacy and security concerns with AaaS

Outsourcing data analytics workstreams that are essential to the success of a business can be a tough decision for business leaders. Whether it is fears of storing data in someone else’s cloud instance or losing some of the control of your data, concerns about data privacy and security are very real and understandable. There are many ways in which Analytics-as-a-Service providers safeguard their client’s data.

Many of the same principles of data security apply whether utilizing an in-house team or an AaaS provider, but when outsourcing data analytics workstreams, it is of even greater importance. When selecting a provider, it is important to choose one that operates in compliance with recognized data protection standards. 

Discussing security classifications of different data, routine audits and assessments of security, data encryption, and SOPs for data incidents are all suggested topics to discuss with any potential AaaS provider. 

Understanding the Analytics as a Service (AaaS) landscape

There are many examples of Analytics-as-a-Service providers available to organizations. Many of the normal players in the services space such as Deloitte and Accenture offer similar services. Additionally, smaller companies such as DataDrive and Analytics8 are emerging as more boutique firms in the space.

The Analytics-as-a-Service market is rapidly growing due to the increasing complexity and innovation of the data and analytics market. The pace at which things are progressing is difficult for companies to keep up with, so leveraging companies specializing in the AaaS is becoming increasingly common.

Common industries and use cases for managed analytics services

While in-house analytics teams are still the standard across many industries, Analytics-as-a-Service is becoming more and more appealing to organizations. Industries that require massive amounts of data and complex data integrations, such as Retail and E-Commerce, Telecom, and Logistics, have all been big adopters of AaaS providers. These companies are often drawn to the appeal of savings in the form of reduced analytics staff while still being able to leverage best-in-class analytics.

Heavily regulated industries, such as Healthcare and Financial Services, have tended to shy away from utilizing AaaS models due to the caution around security and compliance. Despite this, AaaS adoption in these industries has picked up due to advancements in security and compliance. 

Conclusion

Analytics-as-a-Service is an emerging business model quickly gaining traction in the fast-paced and rapidly changing data landscape. The scalability and cost efficiency of the AaaS model is often enticing for companies looking to gain a competitive advantage through analytics without the overhead of massive investments in cloud technology, licensing, and headcount. 

Along with the benefits, organizations should also consider the downsides of not having an in-house data team that can quickly adjust to real-time demands from the business, as well intimately understand business nuances.