How Data Science-as-a-Service (DSaaS) can help your business?

How Data Science-as-a-Service (DSaaS) can help your business?

Cloud providers started offering Data as a Service (DaaS) solutions in 2015 and these services complement products like Software as a Service (SaaS), Infrastructure as a Service, Marketing as a Service, and Analytics as a Service but they aim to organize and unify the essence of most of those other services – data. DaaS is a data management strategy that leverages data as a business asset for greater business agility.

It was a response to the explosion of Big Data and it helps businesses manage the massive amounts of data organizations have flowing through their systems while stopping vendors from locking customers into siloed solutions. A DaaS solution can compute massive amounts of data, while integrating, analyzing, publishing, or sending it to other analytics or data modifying programs for utilization, some of which can be done in real-time.

The DaaS approach, which may include data virtualization, data services, self-service analytics, and data cataloging, “focuses on provisioning data from a variety of sources on demand through APIs. Designed to simplify access to data, it delivers curated datasets or streams of data to be consumed in a range of formats, often unified using data virtualization,” as mentioned in the online report. In general, DaaS lets businesses access and orchestrate the increasingly vast and complex datasets flowing through a company’s systems so the most important data insights can be served up to users and customers alike.


The benefits of a Data as a Service (DaaS) are multifold, including substantial financial benefits in both helping monetize a company’s data as well as in lowering the cost of storing that data, as well as in gleaning insights from that data. The big tech companies have proven there is huge value in data if you can exploit it properly. The volume of data has increased exponentially over the past decade and most companies struggle to organize and operationalize their data. A DaaS solution can help companies wrangle their data, making it far more accessible and therefore more valuable.

Some DaaS solutions add a layer of data virtualization atop enterprise data that unifies data that might be siloed across an organization. Users can retrieve and manipulate data in real-time without requiring any technical information on the underlying data. This helps users act in data-driven ways, which reduces time to insight, saving money on analytics preparation.

In these fast-moving times, agile decision-making is more important than ever. A DaaS platform can combine structured, unstructured, and semi-structured as well as internal and external data into a comprehensive view of the business’s data warehouse.  Once this data is combined, it can be surfaced into an analytics model, show on a dashboard, or uploaded into an end-to-end API serving specific business use cases.

Utilizing a centralized administrative data hub that contains strict metadata governance, a DaaS can provide self-service data access that simplifies business user data access with an intuitive, self-service directory that makes finding data easier as well as more intuitive. When data is logged correctly, with all of its important traits highly searchable, business users tend to spend a lot less time searching for data than actually utilizing and acting on it, which is the very reason to collect the data in the first place.

With storage prices plummeting, data is becoming less commoditized today. Large repositories of structured, unstructured, and semi-structured data are creating vast reservoirs of known and unknown

datasets. It might take seconds to ingest data into a modern EDW, but it could take weeks to make that data available to the business user, who in some cases are completely unaware that the data is even in the system. In other cases, inventive business users might employ data workarounds to access data they have no right to access. When business users add their own data rules, multiple versions of “the truth” – and serious governance headaches – result. This can lead to a data governance nightmare. DaaS platforms were created to avoid all this.

DaaS data cataloging tools can help schedule the data discovery processes and intelligently inspect the underlying data, so that it can be understood, documented, analyzed, and actioned if needed. Links can be drawn between datasets and these can be connected to a business glossary, which can be disseminated throughout a company. A DaaS automates the data inventory process, leveraging smart semantics for auto-profiling, relationship discovery, and data classification. This gives data owners a detailed overview of their data while data users gain visibility into the data before consumption. All of this helps build a data “trust index”, which can be calculated, tracked, and reported regularly.


The “democratization of data is critical for any business wanting to turn data into real value. It represents a huge opportunity to monetize an organization’s data and gain a competitive advantage with a more data-centric approach to business operations and processes,” as per one online report. Data becomes the center of a business and a data-driven culture arises.

Data-informed strategies become the norm throughout the company, from the top C-suite member, through the executive ranks, down to the business users, and onto the clerk utilizing the data. With the deep data understanding, a DaaS service delivers, brands will be able to use predictive and prescriptive analytics to provide their customers with memorable and meaningful experiences that will drive strong and lasting loyalty.

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