We’re living in times of the Fourth Industrial Revolution where the boundaries between what is physical (or biological) and digital are starting to blur. Artificial Intelligence, analytics, digitalization, Internet of Things and other new technologies are all influencing companies economic activity. Businesses around the world are producing data that can lead to valuable insights, but due to the vast quantities of information they need a new way to filter it and be able to draw correct conclusions. That’s where Analytics as a Service (AaaS) comes as a solution.
AaaS merges data mining with predictive analytics and AI to offer necessary information. The combination of data analytics software with cloud computing results in a cost-effective online product that any company can access remotely for a regular fee. That marks a move from traditional services (that included costly servers, space and designated team) toward a digitalized world. And the trend is accelerating – according to McKinsey, the COVID-19 pandemic sped up digitization by three to seven years; what was considered best in class in 2018 is now below average. Even though businesses had to make cuts due to the pandemic, funding of digital and tech initiatives has risen and AaaS is a perfect response to their cost-effective needs.
Thanks to Analytics as a Service companies can make decisions in real-time using knowledge coming from CRM suites that process data from multiple sources. In retail it means combining information from websites, online stores, mailing lists, in-store purchases and other, in order to adjust stock, boost sales and revenues. In digital marketing it helps filter through all the data coming from website, social media, SEO and leads, to be able to adjust campaigns accordingly. The beauty and practical advantage of the model is that even non-IT professionals can analyze and present data and reach conclusions.
AaaS itself is segmented into groups depending on services, analytics type, deployment type and industry.
It offers all sorts of solutions and services – from data storing, visualization and reporting, to predictive analytics, AI (Artificial Intelligence) and ML (Machine Learning).
The types of analytics that it offers:
- Descriptive – what happened in the past
- Diagnostic – why something happened
- Predictive – what is most likely to happen in the future
- Prescriptive – what should we do about it
The deployment type can be a public cloud, private cloud or a hybrid of both. The choice depends on the needs and budget of each company, but regardless of the size of business they belong to various industry types:
- Government and Public Sector
- Banking, Financial Services, Insurance
- IT & Telecommunication
- Retail and E-Commerce
- Manufacturing
- Transportation & Logistics
The importance of Analytics as a Service is best shown by the size of the market. It’s value in 2018 was estimated around 9 billion dollars in 2018 and is expected to reach between 100 and even 126 billion dollars by 2026. As we mentioned already, the main reason for this demand is the vast volume of data produced by different channels, especially by social media and smartphones or other IoT’s. The growing need to measure and enhance customer experience bodes well for the future of AaaS. Obviously, there is also a negative factor influencing the market – data security concerns and the growing amount of cyberattacks are an issue that needs to be resolved.
