In recent years, as businesses have adopted increasingly data-driven models of operation, analytics has experienced a rapid evolution. While traditional BI remains critical to many organizations, it has become just one weapon in a growing arsenal that includes operational analytics and reporting as well as predictive analytics and other advanced models. The emergence of the data scientist, along with the introduction and standardization of increasingly sophisticated statistical modeling, has only served to hasten this evolution.

Meanwhile, the cloud has undergone an evolution of its own, emerging as a robust and viable alternative to deploying and managing infrastructure on premises—while providing potentially significant savings in infrastructure investment. The cloud offers many more infrastructure, software, and platform options than were available even a few years ago. And it can provide flexibility and speed of deployment to support modern development and management methodologies such as Agile. The changing BI and analytics landscape combined with these new capabilities in the cloud set the stage for a whole new class of solutions.

The Rise of BDaaS 

As outlined in our new research report, Comparing Big Data Platforms: Building a Data Foundation for the Future,  BDaaS is another name for cloud managed services, an emerging category of big data platforms. BDaaS providers such as Cazena, Altiscale, and Quoble install, manage, and operate big data platforms on behalf of customers. A BDaaS solution typically provides infrastructure, software, and operational services for customers. This liberates customers from the burden of running a big data infrastructure, enabling them to focus on what’s important to them: making business decisions using data. In a BDaaS environment, data engineers prepare data for analysis and business users query it; everything else is handled by the service provider.

Since BDaaS runs in the cloud, customers don’t purchase hardware or software; they simply pay a monthly subscription fee, which may be flat or pay per use, depending on the service. This requires BDaaS providers to pay close attention to customer satisfaction, ensure high levels of service and support, and incorporate the latest technological advances.

Relieving the Pressure

The appeal of BDaaS solutions is not lost on data warehousing teams, many of whom are scrambling to keep up with the frenetic pace of change using legacy data infrastructures that are beginning to show their age. Meanwhile, BI and Analytics teams have to make the best use of whatever data warehouse resources are available. Adding data scientists to the mix only increases the pressure and potential for internal competition. The various stakeholders spend time and energy vying for resources or building their own solutions, which can be the source of internal complexity and confusion.

Because BDaaS providers specialize in operating big data environments, they can do it more cost effectively than most user organizations. For the same, or less, total expenditure businesses can put an end to internal competition and provide the kind of data warehousing and analytics resources needed throughout the organization.

Time to Analytics: A New Metric for Success?

Further complicating matters for BI and analytics professionals is the “time to analytics” factor. This is defined as the lag time that elapses between beginning a big data project and actually running the first query. For some larger organizations working with legacy environments, the lag can be substantial—as much as 9-12 months. Such delays were hard to deal with a decade or so ago; today they are unacceptable. The cloud offers potential solutions to many of these challenges, but enterprises often lack the skills required to make the move or are held back by security or other concerns.

Because user organizations don’t have to procure, install, configure, and tune hardware and software, BDaaS addresses the time to analytics problem by getting the organization up and running with a big data solution in very short order (immediately, in some cases.)

New BI and Analytics Use Cases Powered by BDaaS

Across the board, organizations are using BDaaS to meet a wide variety of challenges in BI, analytics, and data science. BDaaS frees up resources, enabling these organizations to deliver more responsive and user-centric BI tools and interfaces, including self-service analytics environments. BDaaS provides the freedom to experiment and to spin-up short-term solutions such as data science sandboxes and other trial environments, which can raise significant logistical and budgetary issues when attempted using on-premises infrastructure. Depending on which BDaaS provider they engage, these organizations also enjoy the freedom to leverage different engines for different analytic needs, using Hadoop, MPP SQL, Spark, or others as required by the task at hand. Cazena, for example, provides this.

BDaaS also provides an easier and faster way to leverage new cloud data sources, including mobile data, Internet of Things / sensor data, partner data, data from social apps, etc. This includes purchased data sourced from data aggregators or other service providers, which is a growing and critically important new data source for many organizations. Keeping cloud-sourced data in the cloud significantly simplifies the process of accessing such data and accelerates time to value.

Along the same lines. BDaaS opens up new potential lines of business for some organizations. Businesses are increasingly looking for ways to monetize data as a new product or service or part of an established service. Such offerings can serve as vital new revenue channels or simplify provide a means of standing out with value-added services in a competitive marketplace. Cloud deployment makes it easy for businesses to explore various options for maximizing ROI on their data.

Coming on Strong

BDaaS is changing the game for BI and analytics professionals by reducing internal competition for resources and cutting the time to analytics lag down to size. Moreover, it is speeding and simplifying deployment of big data solutions while helping businesses to cut costs, expand services, and respond flexibly to rapidly changing market conditions. And that’s about as badass as you can get!