In recent years, a new consumer product has emerged on the market due to technological capability improvements. This product type is arguably best known through its software iteration: "Software as a Service (SaaS)." 

SaaS is a product where consumers can purchase cloud-based tools in the software that isn't locally downloaded but outsourced to a cloud. 

These tools aren't exclusively related to data –for example, one can use all of Adobe's programs, such as photoshop and the like online instead of installing them –this "on-demand" software makes it so users can seamlessly use their essential software programs across devices at all times. 

The convenience of such “as a service” products is patent –its benefits aren't exclusive to any specific industry. They ultimately provide a way for workers, managers, academics, and creators to readily access their tools whenever the need arises.

Consider this new technology in opposition to the traditional way of using software: looking for the device where a software program is downloaded to use it or having to wait excessive periods to install software if the device can't be found. 

In the world of Data Science, Data-As-A-Service (also known as DAAS) will provide similar streamlined convenience for data workers, providing data scientists with on-demand tools for managing vast quantities of data.

Data-As-A-Service for Data Scientists

The revolution of the 20th century in computer science was the invention of personal computers, their ability to store massive amounts of data locally, their permission for wide-scale knowledge work, and, of course, the internet! 

It is too early to tell what computer science will look like in 2123, just as smartphones were inconceivable in 1907. However, it’s currently clear that at least one of the revolutions in computer science in the 21st century is cloud computing. 

Cloud computing expands data storage from the finite local level of a hard drive to the seemingly infinite global status of the internet. In addition, tools can be stored in clouds online –to the point where people can do high-skill jobs sitting at a coffee shop on their phones.

Because of the cloud, all the tools we need to succeed at business, school, or any endeavor are always in the palm of our hands. 

For data scientists, data-as-a-service is, in part, the ability to use cloud computing to access complex analytics tools on demand.

Inspiration and creativity are necessary for data science. But, as is well known, these are matters that often occur on-the-fly –taking walks, for example, is highly associated with creative impulsivity. 

Hence, one of the primary benefits of DaaS for data scientists is a greater level of autonomy through the minimal set-up it takes to access necessary tools on the go –a flexibility that no doubt contributes to a higher level of innovation. 

For instance, Google Analytics can send one a phone notification if salient data changes occur and flip over to an app like Smartlook to do what's relevant with that data to the site it pertains to –for instance, perhaps one can create a data model to send to a data analyst for interpretation and report generation. 

DaaS and Data Monetization 

Software as a Service, just like Data as a Service, involves a component of monetization. Just as SaaS providers sell their software to users for their use, DaaS providers, in addition to trading software, sell the tools data scientists need to monetize the data they collect and organize. 

Data is ever-present in the modern world, and it is, in fact, a hotly sought-after commodity. As a result, data is bought and sold by individuals, corporations, and small businesses on a massive daily scale. 

DaaS often mediates these processes –likewise, where large amounts of data are stored online, data-as-a-service is a potential for buyers. 

This is because businesses do not lack an abundance of data. Instead, they seek relevant data –and sometimes, when data scientists clear out what stored data is irrelevant, not much remains, making external data consumption through purchasing data sets a viable alternative. 

Data scientists do not have as a job description "buying and selling data," but that's theoretically a potential task they could handle because of their skill set –it could, for example, be a lucrative side hustle for full-time data scientists. 

Working with DaaS as a Data Scientist

There are various ways data scientists will increasingly find themselves using or involved in data-as-a-service in the years to come.

For example, the cloud-software programs needed to conduct or sell DaaS products and services need developers and engineers. Most importantly, these tech workers must have a deep literacy in technical data jargon. 

Therefore, data scientists would make a no-brainer addition to any team developing data as a product program. However, sometimes even AI requires someone to manually manage the datasets it uses to function –updates will always be needed, as new data is constantly being generated and old data sifted. 

These programs –especially if they are "open access" and therefore available to the online public—would significantly benefit from data scientists with a good understanding of machine learning, where a program can build upon a pre-existing dataset from new inputs –i.e., from the user's using the program.

An excellent example of this technology currently is Chat GPT –which is more like an advanced version of Google than a sentient robot. 

Data scientists can benefit from Chat GPT, just as all knowledge workers can –as things stand, AI does not pose a threat to data scientists or knowledge workers but will foreseeably augment their work, making it more accessible, more flexible, and more rigorous –i.e., it will help us produce better results. 

Chat GPT for data scientists is an example of DaaS technology because one can ask for assistance on various tasks with reliable feedback. Indeed, ChatGPT, when asked, lists a remarkable number of ways it can augment work for data scientists: 

• Automation of data collection and pre-processing.

• Deployment of machine learning models for real-time predictions.

• Development of data visualization tools for better data insights.

• Integration of the data pipeline with business systems and processes.

• Design and implementation of experiments to test hypotheses and evaluate model performance.

• Collaboration with stakeholders to define and prioritize business problems to solve using data.

• Building and maintaining infrastructure for large-scale data processing.

• Creating and implementing security measures for sensitive data.

• Developing and training machine learning models on diverse datasets.

• Designing and implementing A/B tests to optimize the user experience.

• Performing data analysis and presenting findings to stakeholders.

• Providing technical guidance and mentorship to junior data scientists.

• Staying up-to-date with the latest advancements in data science technology and techniques.

• Participating in the development of data-driven products and solutions.

This ability to augment data science work can save data scientists a lot of time, opening them up to focus more intensely on higher-level tasks. But, more importantly, it will clear their minds to tackle jobs of higher priority. 

For example, AI can't work with stakeholders, clients, and business leaders to clearly understand what data models mean, how they relate to their short and long-term ambitions, and how to make a rational decision in light of such models. In contrast, this is the job of a data scientist and data scientist duo. 

Furthermore, AI can only partially take over a data scientist's tasks. For instance, it cannot come up with innovations on the fly while taking walks; it lacks the broader cultural, business, and institutional context to act through the human-centered tasks of data scientists. 

In other words, while something like ChatGPT can relieve data scientists of the burden of doing repetitive tasks, the most important aspects of a data scientist's role will not be automatable anytime soon –only augmentable. 

Such automation and augmentation can be used in the service of monetizing data. Therefore, DaaS –for example, automates data collection and pre-processing such that a specific type of data is flagged for collection (perhaps a data type known to be lucrative or valuable for you or your clients.) 

Further Steps in A Data Science Career Using DaaS 

The future of work is wrapped up in data –so much so that the historian Yuval Noah Harari has coined the term “dataism” for the phenomenon of data being ever-present in the modern world. 

Not only that, but dataism also encapsulates monetization and overall increased accessibility to data-related tools through cloud computing. 

In other words, now more than ever, the tools, resources, and data necessary to become a data scientist are readily accessible. 

Becoming a data scientist is a worthwhile pursuit not only for those looking for a lucrative career path. It is best suited for analytic thinkers, math whizzes, and those who can combine their rigorous analytical skills with the soft skills needed to work with clients who use data but do not understand it. 

Hence, communication skills are just as necessary to data science as the different coding languages one will use. 

If you want to become a data scientist and learn the skills necessary to embark upon data science, click here to learn more about university or certificate programs, resources, and other career information.