Data-as-a-Service (DaaS)
For businesses, DaaS is a boon that alleviates the burden of in-house storage management. For data scientists, it could be an intriguing and remunerative place to launch your professional life.
If you are a pursuing career in data science, DaaS could be a promising and lucrative field to build your area of specialization.
Companies’ dependencies on cloud computing to carry out important business functions is growing at a rapid rate, a phenomenon that is likely only to escalate as data-driven business practices continue to proliferate and the capacities of data mining softwares continue to improve. This means the need for dedicated specialists is growing among businesses, a trend that is likely to continue if not intensify as technology continues to evolve.
Indeed, many working in big data see cloud computing as the future of data storage and management, meaning businesses across industries are beginning to adopt cloud-based database architecture. New businesses, meanwhile, are immediately building cloud computing into their operational models. In short, cloud computing is having a moment, though it’s not one that’s likely to end anytime soon.
To learn more about DaaS, from its uses and benefits to the career paths that might be available to you in this growing field, read on.
What is Data-as-a-Service?
Put simply, Data-as-a-Service is an approach to data management and processing systems that relies upon independent providers using cloud-based software to oversee a company’s data collection systems. This means delivering applications to end-users through the cloud, removing the need to install and oversee these systems locally.
DaaS acknowledges the difficult and labor-intensive work of database management, providing a solution that improves functionality while offsetting strain on a company’s workforce.
Different businesses can rely on DaaS in different ways depending on their particular needs. Processes offered by DaaS providers include the following:
- Data cataloging
- Data virtualization
- Self-service analytics
- Data services
Until recently, cloud storage was too costly and too limited to be a viable option for large companies. However, as cloud technology has improved, it is both more accessible for businesses and significantly more advanced, designed to handle massive data workloads and key functions including data analytics, integration, and processing.
How Data-as-a-Service Helps Businesses
Beyond improving management efficiency, DaaS has been shown to pose numerous other advantages to businesses when implemented and managed effectively. Some of these results are clearest to businesses over time.
Areas Where DaaS Can Be Useful
DaaS is implemented to help businesses with a huge variety of processes that impact their day to day functions. These include the following:
- Operational efficiency
- Sales and marketing
- Inventory management
- Manufacturing optimization
- Overall business strategy
This means that for many businesses, embracing DaaS is not a question of “if” but a question of “how.” A good DaaS team should be able to recommend an approach that is tailored to a business’ particular needs and goals.
Some successful adoption of Data-as-a-Service examples can be found in companies that require users to access unique and specific datasets in order to carry out their jobs. By mitigating the processing time of this process and allowing individuals faster access to the data they need, these companies are able to cut right to the chase, completing their work more quickly and giving time to take on even more.
Benefits of DaaS
Every business has different needs, but it is likely that DaaS can be of great use to a huge variety of them. Some of the top benefits of Data-as-a-Service include:
- Improved functionality. First and foremost, Data as a Service architecture is designed to maximize a business’ efficiency and core competencies to keep up with the high volumes of data that advanced technology can capture. This means reduced time-to-insight, improved data reliability and integrity, and increased agility for data workloads. Businesses that incorporate DaaS services do so primarily for this reason, saving them the trouble of taking on the skilled work of data management within their own ranks.
- Lower costs: Harnessing DaaS as part of a company’s business model can help identify operational inefficiencies and set appropriate resource allocations for their digital services. DaaS can also allow data-driven decisionmaking to rise to the forefront of a company’s strategy. This means that when used correctly, DaaS can help guide a business to cut out resource-wasting practices and avoid designing plans that will take the company in the wrong direction.
- Improved data monetization: At the same time that DaaS can reveal illuminating cost-saving strategies, it can also use data-driven strategy to help reveal areas for increased monetization. In recent years, most companies have more than enough data to work with but are still struggling to leverage their data to the fullest extent of its value. Working with a DaaS team can improve data accessibility, carrying the potential to make this process easier for businesses.
- Automated maintenance: One of the key features of a DaaS plan is maintaining and updating platforms so that end-users do not have to take responsibility for it themselves. For a business, this means minimal interference to workflow and ensures that the sometimes complex work of tool and service updates are overseen by experts.
- More informed decision-making: The primary function of data mining is improving a company’s insights into their business’ strengths and weaknesses. By harnessing DaaS, companies are investing in making the most of their data to gain clearer and more effective strategic information from their findings. This can mean combining data sources for a more thorough understanding of where the business stands as well as delivering data for purpose-built analytics with end-to-end APIs to serve specific business use cases.
As you can see, DaaS provides numerous advantages to businesses who may otherwise be looking within their own ranks to take on the challenging and labor-intensive work of managing cloud storage.
Career Paths in Data-as-a-Service
If you are considering a career in big data, a job in the world of DaaS could be a fantastic area to use your expertise in data science. A skilled, specialized role in a market that is only growing, DaaS data scientists are likely to find numerous highly valued roles when looking to start their careers. Indeed, because DaaS is not industry specific (and in fact can be of use to a huge variety of businesses), this means that as the years go on it is likely that more and more businesses will continue to adopt Data-as-a-Service to keep up with the times.
As cloud computing becomes more prominent, many master’s programs are including it as a concentration to allow focused students to kick start their DaaS careers right away.
Titles for roles specializing in DaaS can vary depending on where you work. They can include the following:
- Systems engineer
- System administrator
- Cloud systems administrator
- Data architect
Data Scientist Salary Range and Job Growth
The time couldn’t be better to enter the fast-paced field of big data, which has grown exponentially over the past several decades and is showing no signs of slowing down in the future. In fact, the Bureau of Labor Statistics projects an anticipated growth rate of 36% for data scientists by 2031, greatly outpacing most other professions. These numbers are part of what’s inspiring so many young professionals to pursue advanced degrees in data science, opening up these bountiful opportunities.
The other factor that is drawing talented individuals into the world of data science is the impressive salaries many of these jobs offer. Indeed, data scientists perform crucial functions for businesses – including but not limited to the complex responsibilities of DaaS specialists – and are valued highly for their work. According to the Bureau of Labor Statistics, data scientists earn a median annual salary of $100,910, significantly higher than the national average. In the top-paying states for data scientists the salaries are even higher, with California offering a mean annual salary of $133,110 and New York offering a mean annual salary of $122,540.
Taking the Next Steps to Pursue a Career in DaaS
If you’re eager to take next steps toward pursuing a lucrative career as a Data-as-a-Systems expert, your educational background will be the most important factor in shaping your career. Typically, those who work in big data (including those outside of DaaS roles) are expected to hold master’s degrees in Data Science, oftentimes with specializations in whatever role they would like to pursue in the long run. If you do not yet hold a bachelor’s degree, there are programs that offer concentrations in Data Science to better prepare you for the rigors of a master’s program.
For more information about data science programs across the country, take a look at our comprehensive guide here. These include master’s, PhD., associate’s, and bachelor’s programs as well as data science bootcamp and certification opportunities.
If you’d still like to learn more about the variety of career opportunities available for data scientists, our Data Science Career Overview guide will break down the many distinct roles and employment opportunities available in the field.
For an index of our many guides for data science students and professionals, visit our Data Science Resource Center.
