Data-as-a-Service (DaaS): An Overview

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As external data begins to gain importance in business analytics, data assumes a new role in global businesses. Now data is not only an organizational asset, but also a distinct revenue opportunity via data-related services offered under the umbrella term of “Data-as-a-Service” (DaaS). DaaS service providers are either replacing the traditional data analytics services or are happily clustering with existing services to offer more value-addition to customers.

The DaaS provider’s core competence lies in “curating, aggregating, and meshing” multi-source data to offer value-added intelligence or information. Typically, DaaS providers deliver “information” via a digital network, which is most often cloud-based. To this end, organizations may “buy, sell, or trade” soft-copy data as a DaaS service. IDC’s Data as a Service gives an overview of the demand-supply trends of DaaS services.

DaaS as an Emerging Market: Potential Market Size

Businesses of all shapes and sizes across the globe have suddenly caught on to the idea that DaaS not only promises unique revenue channels but also a path to “reshape the business world through competitive intelligence.” As data storage costs continue to fall, the demand for more data in circulation rises. According to MarketWatch, between 2019 and 2024 the DaaS market is expected to grow at a CAGR of 10 percent.

Currently, the effectiveness of big data is reduced due to data silos with little inter-connectivity. DaaS offers an instant solution for data-sharing within an enterprise and beyond. A Forbes post foresaw the power of DaaS as an emerging business opportunity three years ago. According to Big Data as a Service: What Can it Do for Your Enterprise? the BDaaS market “has the scope to grow to about $2.55 billion, which is 15 percent of the [total] Big Data market.”

The DaaS Business Model

Organizations offering DaaS services have the necessary infrastructure comprising Data Science, engineering, AI, computer science, and training facilities that are required to deliver value-added data services. Additionally, the operating business model must also ensure that the revenue generated from DaaS services exceed the initial investment and operational costs of running the business. The DaaS business is typically subscription-based, where the customer pays for a range of services or selective services.

This business model comes with its own challenges of data piracy. Without clearly knowing the license status or the usage agreements of sourced data, it is difficult for a business operator to succeed in this business. Generally, all DaaS business operators develop and use a License Agreement to preserve the intellectual property rights (IPR) of the data they sell, process, or analyze to protect the data from any type of copyright violations, subscription-rule violation, or usage violation. As in the case of all digital assets, finally the trust remains with the customer.

Big Data as a Service (BDaaS): What Is the Future?

The post Big Data as a Service captures the essence of this concept. The biggest benefit of BDaaS, as suggested in this post, is the “virtualization” of data center activities, which many businesses cannot afford. Thanks to cloud service providers, now the most advanced analytics and BI solutions are available for a monthly fee, which includes the convenience of large data processing, back-up data storage, and other back-end services. Big Data-as-a-Service (BDaaS), in a nutshell, offers easy data access, economical data storage and processing, and the convenience of a full-fledged data center facility without the burden of administration or operational costs.

The concept of BDaaS has dissolved the walls guarding data silos from easy access. Hadoop won the first battle for democratizing big data by making storage cheap; later, open-source analytics packages made big data analytics available to all. According to Bernard Marr of Forbes, the “forecast value of the BDaaS market is $30 billion.” Many BDaaS providers offer consulting and advisory services bundled within their packages.

A Mordor Intelligence Report titled Big Data as a Service Market — Growth, Trends, and Forecast (2020-2025), predicts the United States will most likely dominate the both global and regional BDaaS market in the next five years. Currently, BDaaS adoption is gaining popularity among the U.S.-based banking, professional services, and manufacturing sectors, as well as in federal agencies. In case of BDaaS, private cloud networks seem to rule, as all the services are strictly consumed within a dedicated infrastructure with stringent security benefits.

Cazena provides some use cases for BDaaS, where Data Lake-as-a-Service is used a single-point data repository, Data Mart-as-a-Service is used to extend the capabilities of data warehouses by moving workloads or users to the cloud, and Sandbox-as-a-Service is used to enable pilot-testing of new ideas and hypotheses.

A Datafloq article offers some interesting use cases for marketers across industry sectors. In a marketing use case, typical CRM data are combined with Hard-to-Find Data (HTFD) and real-time channels to target new customers.

The Difference between DaaS and Data Science-as-a-Service

Data Science as a Service describes how packaged Data Science is gradually becoming a popular concept among businesses with limited resources. Businesses that cannot afford an in-house data center frequently go to cloud service providers who offer packaged Data Science solutions. This service is basically emulates existing Data Science capabilities (analytics and BI) with outsourced services. This type of service is branded as “Data Science-as-a-Service,” which is distinct from DaaS, which extends the capabilities of data services through a data-sharing platform. In DaaS, data-enabled insights are packaged and sold as a commodity. In the future, even these services may get partially or fully automated due to advances in AI, leaving less scope for human storytelling.

Maximizing ROI from Organizational Data

In many companies, the data experts have collaborated with sales teams to collect, organize, store, and label data in a user-friendly fashion for potential revenue benefits. The noteworthy points discussed in this summit were success stories of corporate data-sharing and team work, data competitions to change mindsets, and plug-and-play data-service solutions. 

As a case study, at Workday, DaaS service is offered to share tenant data across all applications while preserving the integrity of individual tenant data. Since 2016, the billing and metering data sets at Workday have been hosted on a DaaS platform. The goal of this data-sharing platform is to keep the usage data of each tenant segregated, while allowing all Workday applications to interact with and share the configuration data of tenants as needed. The main face behind the Workday Cloud platform API is Erol Guney, Principal Engineer and Data-as-a-Service Architect.

Customer Data in the DaaS Era: New Possibilities

Enriching customer data with “digital profiles, life events, community information, transaction-based insights, customer preferences, sentiment scoring, and so forth” is the new business mantra. However, there’s one catch: the data till recently was spread across disconnected data sets. Now, with DaaS services, 360-degrees customer data is available and accessible across the business to enable on-the-spot discounts, product recommendations, and vastly improved customer experiences.

Research states that successful businesses outperform their competitors by “85 percent in sales growth and more than 25 percent in gross margin” by leveraging customer behavioral data. In a data-driven world, smart businesses are quickly cashing in on deep customer insights to develop customer-friendly products, services, and buying experiences.

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