One could argue that data as a service (DaaS) is a natural evolution of software as a service (SaaS). Both provide service remotely and both use the cloud. The key difference, of course, is that SaaS deals in software while DaaS deals in data. With the advent of machine learning and cloud technology, such as AWS, data has become a lot more relevant.
With data becoming more relevant, a lot of data-related terms have recently become a lot more prevalent in today’s world: DaaS provider, data science, data services, data management, data access, business intelligence, DaaS platform, data assets, data governance, data scientist, and data analysis. In the DaaS market, many types of data product are out there for data consumers. A good example or two of some service offering that follows a DaaS model might include master data and big data.
Master data relates to business processes and the data needed to manage any business organization. Types of data that can follow a DaaS model and fall within the master data category could include business intelligence, customer experience, and enterprise data. The science of organizing and applying master data is known as master data management (MDM).
Data delivered as business intelligence (BI) is a must for many organizations. Insights regarding competitors, investment opportunities, and market trends are great examples of business intelligence. If this business intelligence can be delivered in real-time, it offers a great competitive advantage. One could also argue that predictive analytics can be a form of business intelligence. Data delivered as customer experience (CX) is a must for any sales campaign so maintaining a positive customer experience with a DaaS solution is key for business growth.
In many ways, enterprise data is the epitome of master data because it’s so internal. Enterprise data is essential for any business model being applied to a large organization as it’s the best way for enterprise-level businesses to keep track of their business processes throughout the entire organization. As the ancient Chinese general Sun Tzu once said, “The man who knows neither his enemy nor himself will always be in peril.” Without the incorporation of enterprise data management or enterprise resource planning in a business model, a large organization cannot know itself.
One of the biggest challenges for software companies is handling the data complexity of big data since it involves the daunting task of gathering a large amount of data, especially unstructured data from a variety of sources. Making sure that software companies have the right data and are capable of maintaining data integrity is a proper test of any data management strategy. In order for this big data—which is often unstructured data—to be transferable into business insights that humans can actually use, a data virtualization strategy working within the UI would be a great first step.
Regardless, big data delivered through a DaaS environment is the best way to import a massive amount of data from a variety of sources. That is why software companies would regret missing out on the big data analytics markets. There are several DaaS vendors available for data sharing. You can always aggregate your data with a DaaS solution later.
Through the widespread use of APIs, many cloud services are available for software companies and individual consumers alike such as AWS (Amazon Web Services), Microsoft Azure, iCloud, Google Cloud, Alibaba Cloud, Salesforce, SAP, and Oracle. If that’s not enough, you can check the Cloud 100. Therefore, there is no need for software companies to depend on SaaS for their data solutions when they can simply use a DaaS and “cloud-up” instead.