Analytics-as-a-Service (AaaS) provides subscription-based data analytics software and procedures through the cloud. AaaS typically offers a fully customizable BI solution with end-to-end capabilities, organizing, analyzing, and presenting data in a way that lets even non-IT professionals gain insight and take action. The ubiquitous rise of Big Data and the astronomic expenses of parsing these massive datasets is leading CIOs to opt for AaaS’ cost-effective web product over traditional licensed and on-premise solutions.
AaaS uses data mining, predictive analytics and AI to effectively reveal trends and insights from existing data sets. In the past, running analytic processes on data warehouses would require a sizable team of data engineers and data scientists.
With AaaS, Big Data cleaning, analysis and actionable insights are a scalable, affordable option for organizations at different stages of growth. From entirely web-based AaaS to hybrid versions that integrate with existing infrastructure, embedded analytics empower companies to make smarter decisions in real-time.
How can I use Analytics-as-a-Service?
Data-reliant companies from across industries are increasingly turning to AaaS for their analytic needs. Companies with more robust IT departments may look to AaaS for more simple descriptive analytics which their own data scientists can then unpack. On the other hand, companies with less highly-developed IT capabilities might use AaaS for more complex predictive and prescriptive analytics.
See it in action:
One common example of an industry that’s embraced AaaS is retail. The industry produces petabytes’ worth of data from thousands of touchpoints—websites, mailing lists, in-store purchases, mobile POS, and more—and must constantly parse it and understand it to boost their revenues. On-premise analytics for these companies can be costly, as they would require teams of data scientists.
See it in action:
Tools like CRM suites, which simplify the process of onboarding data from multiple streams, can help companies get organized and share their data more effectively. AaaS reduces the cost of analysis, and offers quicker comprehension, better insights, and more agile business processes.
From combining structured and unstructured data into a single data narrative to using Machine Learning and AI to extrapolate customer journeys, preferences and purchasing patterns, AaaS is informing future process and product decisions. Most importantly, it makes analytics accessible to any member of a team without requiring a deep understanding of analytics, or even of the technology behind it.