A few years ago, many companies turned to cloud and online service providers to preserve their virtual environments. They have thus quickly succeeded in overcoming the inertia and business obstacles that previously prevented them from implementing software solutions (SaaS), platforms (PaaS), or other "as-a-service" products.
What are the catalysts driving this shift to cloud-based analytics? Here are five explanations:
1. Keep pace with augmented analytics innovation
Augmented analytics innovation has accelerated dramatically over the past year and shows no signs of slowing down. From Natural Language Understanding (NLU) to Machine Learning, the results achieved by leveraging emerging technologies are a natural way to stand out from the competition.
Therefore, it is unsurprising that companies want to take advantage of it without delay. Ultimately, SaaS applications are the only way to keep pace with new product launches and updates.
2. Encourage work from anywhere
If one trend is likely to continue after the pandemic, it is the reorganization of workplaces. After working remotely for so long, some employees will be reluctant to redo the daily commute between home and office to conform to a "custom." From now on, everyone expects to be able to work from anywhere, especially from the place where they are most productive. SaaS solutions have played a significant role in maintaining business continuity for businesses during the pandemic. Still, their potential will become tangible in the coming months as they provide access to analytics from n' anywhere and on any device.
3. Foster collaboration
During the pandemic, the ability of businesses to share data has been vital in enabling them to gain complete visibility of the situation. This was crucial to fostering collaboration and finding standard solutions to the exceptional case. However, many public and private organizations cannot easily and securely share analytics with vendors and partner organizations.
Analytics in the SaaS model is the ideal solution to allow them to easily share more data between the different partners, all within a governed framework to guarantee data security.
4. Overcome barriers to entry
In the unstable environment that characterized the past year, analytics has demonstrated its ability to fuel agile decision-making. As a result, many companies that have not yet adopted analytics will start to do so. Interest this year. Typically, it is recommended that companies begin their analytics journey "start small" – starting with a well-defined and impactful project before aiming higher. However, for many of them, before SaaS solutions, the main obstacle was the scale of the initial investment, even for small projects.
If the project was successful, upgrading from a slight scope to an enterprise-wide service was time-consuming and expensive. Thanks to analytics in SaaS mode, the technological barrier to entry and the level of investment required for initial projects allow companies to accelerate ROI. Scaling to an enterprise-scale software solution can be a simple click away.
5. Improve reliability and scalability with SaaS
One of the companies' main assets lies in their data; the impressive increase in cybercriminal organizations demonstrates this. However, SaaS environments make it possible to reduce the risks associated with corporate data security because the possibility of error is much lower than when the servers are configured manually.
Additionally, given the level of certification that software-as-a-service vendors require to be viable, companies have no reason to doubt that the analytics software they acquire meets the very stringent security standards that ensure the security of their main asset. Therefore, they only need to look to suppliers with third-party certifications, such as SOC II or ISO27001.
The analytics industry is changing drastically, and the demand for data-driven agility and more distributed work practices is forcing companies to move away from traditional on-premises solutions. Analytics in SaaS mode will encourage rapid innovation, collaborative analysis, and the production of real-time lessons that characterize Active Intelligence, this new generation of data-informed decision-making.
Comments