Data as a service tools leverage the cloud to store, process, integrate, and analyze data, so it can be more easily used for maximized business value.
Overview
With today’s enterprises generating terabytes upon terabytes of data every day—at ever-increasing volumes—effectively managing that data has never been more important. And the cloud is often the best setting in which to manage it. Data as a service (DaaS) platforms are some of the most effective solutions for this critical responsibility.
Data as a service (DaaS) systems use the flexibility and limitless resources of cloud deployments to store, process, integrate, or analyze mission-critical enterprise data.
DaaS doesn’t give business users application functionality without local installation—like software as a service (SaaS)—or an app development environment, as with platform as a service (PaaS). Instead, a DaaS solution frees data from the data center’s constraints.
Data integration, data storage, processing, and analytics operations all take place in the cloud with DaaS. Such a platform can also be a conduit for data that organizations need but can’t host themselves due to a lack of storage or compute resources.
DaaS is much older than some of its cloud as a service counterparts, and isn’t necessarily as well known. But as organizations have started catching on to the value a DaaS provider can offer, this cloud-based offering has become one of the more interesting data trends.
The market for DaaS is expanding at a 36.9% compound annual growth rate (CAGR), according to projections from Market Research Future. Its value is projected to reach $12 billion in 2023 and rise to $61.42 billion by 2030.
A typical DaaS solution starts with a tool that gathers data from a variety of sources, including cloud-based data lakes, databases containing operational data, data warehouses, file systems, apps, and other external data.
Once all of that data has been gathered, it will be extracted and passed through a virtual data layer. The virtualization layer transforms data sets or streaming data, allowing it to be consumed in a variety of formats, regardless of the original format at the time of extraction.
Then, data is moved through various services and application program interfaces (APIs). The data will be reviewed for security issues before going through various steps of API management. These steps may include representation, analysis, orchestration, registration, caching, and documentation. API management tasks are often automated via artificial intelligence (AI) or machine learning (ML) algorithms.
Finally, the DaaS platform delivers the data to the end users who need it—business users, customers, or third-party partners—via a number of tools. These may range from microservices interfaces and dashboards to web-based or mobile apps.
DaaS solutions can offer significant benefits to the business processes of any enterprise-scale organization. In a nutshell, these tools help solidify the value of data as a concrete business asset.
But the DaaS framework can also come with certain challenges that data teams and company leaders may have to face. So it’s critical to be mindful of both sides of the coin here.
Because DaaS is based in the cloud, it offers the inherent freedom of that environment—in this case, the flexibility to only pay for exactly what resources are needed to handle a particular data task, like a data analytics workload. As requirements change, DaaS users can scale down or up to match their circumstances.
A DaaS solution can allow data consumers to have ready access to critical data in real time, regardless of their location, the limitations of local infrastructure, or the devices they are using.
Because of the always-on data availability that DaaS helps facilitate, mission-critical enterprise applications can consistently perform either as well as or better than end users expect them to.
There’s typically no need for anyone on the enterprise customer’s end to handle updates, data management, troubleshooting, or other tasks that would be common with a software-based system. These responsibilities are all but exclusively handled by the provider of the DaaS solution. This may also help cut costs, as you may not need to retain staff who specialize in tasks the DaaS can handle.
DaaS can enable high-level analysis through predictive analytics and prescriptive analytics. This can allow enterprises to better understand their customers, partners, and users within the business, so they can make more informed decisions regarding external and internal matters.
Apart from the potential for cost savings, DaaS platforms can also help generate revenue for the company. This may be direct, through sales agreements with partners, or indirect, via the various operational improvements and increased customer satisfaction that become possible when data is more accessible and useful.
Data can sometimes become less secure and private once it leaves the realm of on-premises and enters the cloud. Businesses must implement significant security measures to minimize this risk, ranging from high-level encryption—for the data in transit—to cloud-native tools like firewalls as a service (FWaaS) that protect the data once it's at rest.
Maintaining the privacy of sensitive data is a major concern for individuals and governments alike, more so than ever before. Enterprises using any DaaS platform—especially those that do a significant amount of business through the sale of collected data—must be extremely careful that this data service operates within the bounds of data sovereignty and privacy regulations of all relevant jurisdictions.
Implementing a DaaS platform may mean that it is only compatible with modules within the platform or separate applications from the same DaaS vendor. If your enterprise has other data tools that it has become accustomed to, this may also be problematic for the new DaaS solution. It’ll be important to find a DaaS platform that offers as much interoperability and flexibility as possible to mitigate this issue.
Given the large volumes of data generated by any enterprise-scale organization, it can be difficult to establish a consistently high level of governance between the DaaS environment and data that stays on-premises. Periodic checks for data integrity and quality are necessary to help maintain proper data governance.
When implementing a DaaS system, if the strategy behind this project doesn’t account for how it will affect the entire company, an enterprise may not maximize the value of the solution. Buy-in and participation from stakeholders across the company, including the C-suite, is a must, to better ensure optimal data accessibility—also known as “data democratization”—for all business users.
The best way to illustrate how DaaS can benefit modern enterprises is to look at organizations—such as these Teradata customers—that have used such platforms to turn their data into one of their most valuable assets.
Maintaining a smooth-running supply chain is integral to any manufacturer, but especially one in the hypercompetitive automotive industry. BMW Group needs to move 30 million parts per day to maintain its production schedule, and those components come from all over the world by rail, road, air, and sea. Critical data is generated at each link in the supply chain.
BMW uses DaaS in the form of a robust data analytics platform, which allows the automaker to access and analyze key details regarding suppliers, pricing, production, shipping, and more. The visibility granted by DaaS lets supply analysts monitor production in real time, and when problems arise, they’re seen more quickly so they can be mitigated.
Though it is one of North America’s largest banks—with approximately 17 million clients across 36 countries—Royal Bank of Canada (RBC) knew it needed to distinguish itself in the marketplace by offering exceptional customer experiences. Meticulous analysis of client data would be necessary to drive this effort.
RBC used DaaS and analytics tools to scale data science projects across the organization and automate various data management tasks, which helped improve decision-making and generate insights at scale to form detailed profiles of customers. In addition to other successful data projects, the bank built an ML-driven recommendation engine for commercial account managers to derive information most relevant to their clients.
Groupon relies just as much on its partner merchants as it does the consumers who use its mobile app or website as a convenient source of local discounts. And without the ability to make data actionable, customers won’t see intriguing deals, and merchants have no incentive to provide those deals.
The organization originally stored data on-premises. This became cost-inefficient as sudden market changes forced it to increase storage or compute power by buying additional hardware and infrastructure. By migrating to the cloud and adopting various DaaS tools, Groupon enabled itself to scale more easily in tune with demand, while cutting-edge analytics ensured customers encountered the most relevant deals—and merchants got potential repeat customers.
As data volumes—and the number/diversity of data sources that are essential for enterprises to go about their day-to-day business operations—increase, the importance of DaaS tools will only grow. Rising demand for real-time data analytics is also driving interest in DaaS.
Teradata Vantage can be the cornerstone of your enterprise’s DaaS strategy. It unites data integration, processing, and analytics under one platform, while also being compatible with leading data storage tools like Amazon s3 and Azure Blob. Moreover, Vantage is flexible across the cloud and on-premises infrastructure, ensuring no portion of your data becomes siloed.
To learn more about Vantage and Teradata, check out Gartner’s latest Critical Capabilities report. Teradata ranked highest in all four analytical use cases for cloud database management systems (DBMS)—data warehouse, logical data warehouse, data lake, and operational intelligence.
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