The evolution of Software as a Service (SaaS) in the lab and beyond

In the last 1015 years ago, software companies overwhelmingly adopted the cloud-hosted, consumption-based software as a service (SaaS) model. There are several reasons for this assumption. Software companies had to keep up with changes in technology that allowed more services to be streamed. SaaS brings software vendors reliable revenue from consumers who now pay monthly or annually for something they used to buy once or twice a decade. Consumers get the flexibility of a more customizable service. At the same time, sellers reduce manufacturing costs because they no longer have to pay for physical media, packaging or shipping.

Adoption of SaaS in labs also brings benefits to end users. The loss of ownership can be offset by efficiencies or by offsetting capital expenditures on IT, infrastructure and cybersecurity support. The bundling of Systems in a cloud environment lower costs and allow smaller companies to adopt them.

It’s important to note the difference between cloud technology and SaaS. Although all SaaS are cloud-based, cloud environments also include infrastructure as a service (IaaS) and platform as a service (PaaS), among other niche models. The cloud is there in the background, but SaaS is not the cloud.

A brief history of SaaS

So what is SaaS? SaaS applications began as a natural progression from the earlier Local Area Networks (LANs) model developed in the late 1960s. On a LAN, a powerful mainframe computer or server hosts independently packaged software for access by terminals throughout an organization. The software on the physical server could be purchased by the organization and run on the server until it became obsolete.

This model of software hosting quickly became inefficient on a large scale. Software programs have simply outgrown local LAN servers as processors have become more powerful and the programs themselves more complex. In 1965 Gordon Moore postulated that the number of transistors on microchips and thus the computing power would double about every two years. SaaS became the answer to processing needs.

The rest of the 20thth Century was a golden age for SaaS. Software and the servers it ran on became almost unimaginably powerful, generating ever more complex data (which, of course, needed to be stored). The growth in computing power continued into the early years of the 21st centurySt Century, and while advances in hardware design were occurring at the same time to further push the frontiers of computing power, Moore’s Law is dead now.

Distributed computing with purpose-built processors was a viable short-term solution. However, it won’t be able to keep up with the insatiable demand for data. Right now, like the Wizard of Oz, I have to ask you to “don’t pay any attention to the man behind the curtain!” The answer to what happens when computer hardware can’t keep up with data demands is another story! (Spoiler alert: it is quantum computing.)

SaaS applications in the lab

Labs are not immune to SaaS adoption. Laboratory budgets are getting tighter; existing staff must do more with less. The ability to remove some infrastructure burdens is an attractive proposition. Because of this, all major laboratory information management system (LIMS) vendors offer SaaS options, and there are some LIMS that are offered strictly as SaaS. The validation of cloud-based laboratory software enables the use of these systems in regulated environments.

LIMS isn’t the only lab software moving to SaaS. Chromatography data systems (CDS), electronic laboratory notebooks (ELNs), and laboratory automation or connectivity software are also common SaaS offerings. Much of the reporting and data analysis can be done remotely when applications are hosted in the cloud. In the early days of the COVID-19 pandemic, such applications allowed organizations to reduce the number of on-site workers in the lab for everyone’s safety.

Storing laboratory data in the cloud has opened the eyes of organizations to the possibilities of unlocking the business value of this data. Lab data is no longer kept in a dusty notebook in a warehouse. Anyone with the appropriate credentials can access the data and use it to solve problems and design new products.

However, accessing more data increases the entropy of the system. Imagine that the second law of thermodynamics is a cat and the possibility of unlimited cloud storage is a bag of catnip. You can begin to understand how the amount of lab data has exploded since SaaS became a mainstream model. So far, as data has expanded, so has the capacity of SaaS solutions.

Laboratories today store data in LIMS, ELNs or CDS. If these systems are connected at all, the connection is made in a predefined, structured way. To get the most out of laboratory data, an unstructured data environment (a lake or warehouse) is preferable. In response to this big data expansion, companies are developing advanced data and analytics capabilities to work with their data.

The common tools for exploring the large amounts of data that SaaS can store and output are artificial intelligence (AI) and machine learning (ML). The life sciences in particular rely on these tools to deal with genetic data sets and large-scale clinical studies. These tools are also being used increasingly in engineering design, environmental monitoring, and oil and gas exploration, to name a few.

Future directions for SaaS in labs and across enterprises

More and more companies use specific applications of SaaS. Backend as a Service (BaaS) is being further expanded and offers building blocks for new applications. containers as a service; desktop as a service; environment as a serviceYou have the idea; There’s an alphabet soup of potential cloud services with plenty of room for growth.

CSound-based integration platforms as a service (iPaaS) offerings will allow companies to connect all their different tools, applications and informatics systems to a single data source in the cloud. These platforms can enable seamless, real-time data sharing across multiple sites, enabling more efficient research. Machine Learning as a Service (MLaaS) will enable more companies to access unknown insights from their large data sets.

The increasing proliferation of cloud services naturally requires better cybersecurity. You can find data to support the arguments that SaaS is either more secure or less secure than on-premises solutions. To understand the arguments, it can be helpful to look at this topic in terms of the difference between privacy and security. Security is about protecting data from theft; Data protection is about the responsible handling of this data. On-premises systems ensure data protection because your organization remains in control of the data; at least until the system is hacked. Cloud-based systems require some loss of privacy as your data is now stored on someone else’s server. However, cloud-based systems can boast a higher level of security (although they are not immune to hacking). An organization’s tolerance for privacy and security sometimes varies across lines of business.

What does the future of SaaS look like? It’s impossible to know for sure. But what seems certain is that SaaS is here to stayin the laboratory and in organizations.

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