The analytics industry is no different. In 2020, we saw huge adoption of SaaS, with Gartner predicting public cloud services will be essential for 90 per cent of data and analytics innovation by 2022.

So, what is the catalyst behind the shift towards cloud-based analytics? Here are the key drivers:

1. Innovating with augmented analytics

Innovation around augmented analytics has grown immensely over the past year and it looks set to continue. From the introduction of natural language understanding to machine learning, the ways in which organisations are using these technologies is enabling them to develop a competitive advantage. However, to take advantage of the latest technologies, SaaS applications are critical, given the rapid rate of new releases. Traditional processes that see organisations conduct assessments and reviews on new upgrades are preventing businesses from evolving their analytics at a pace that will allow them to achieve Active Intelligence from their data. 

2. From “work-from-anywhere” to “analytics-anywhere”

“Work-from-anywhere” has been a talking point all throughout the pandemic and it’s likely to remain, post-pandemic. People no longer want to commute to the office every day, instead, they’re opting to work remotely in environments that see them be most productive—be it at home, a coffee shop or elsewhere.

It’s no secret that SaaS has played an important role in supporting business continuity during the pandemic, but its ability to enable work-from-anywhere is just the beginning as it can also drive, “analytics anywhere”. 

3. Data should not operate in isolation

Throughout the pandemic, data has been critical to how health and government leaders fight the spread of the virus as well as to how organisations develop their business continuity plans. Unfortunately, many organisations utilise their data in isolation and are not set up to enable the secure and simple sharing of analysis. There are, however, organisations where this is not true. And these are the ones that gain the most value. Tasmanian Health Service is an example of how data sharing can prove fruitful.

The Tasmanian Health Service is responsible for the operational analytics within the Department of Health and Human Services and developed a number of dashboards during the pandemic. The dashboards were made available to more than 600 clinicians and other health staff to drive data-informed decisions across the entire organisation, spanning multiple hospitals across the state. 

4. Lowering the barrier to entry

As alluded to in the case of the Tasmanian Health Service, analytics has great potential in informing agile decision-making, particularly in the environment we have been living through this past year. This will undoubtedly see many more organisations adopt data analytics moving forward. Typically, when starting on their analytics journey, we recommend organisations find a well-defined project and then scale up once a return on investment (ROI) is seen. However, pre-SaaS, the challenge for many was the sheer scale of upfront investment needed. With SaaS analytics, the technical barrier to entry and level of investment required for initial projects helps organisations see the ROI quickly. The process of then moving to enterprise-grade software can be as simple as the click of a button.

5. SaaS maintains security

An organisation’s data is its greatest asset, so securing it is imperative. SaaS environments lower the security risk to an organisation as there is far less room for error than when servers are manually configured. Furthermore, the certifications that SaaS software providers must achieve to become a viable vendor mean that organisations can be confident the analytics software they’re procuring meet the stringent security standards required to keep their greatest asset safe.

The next generation

The analytics industry is undergoing significant transformation as the appetite for data-informed agility, coupled with distributed working practices, demands a shift away from traditional solutions. SaaS analytics underpins the rapid innovation, collaborative analysis and real-time insights that characterise the next generation of data-informed decision-making and Active Intelligence.