Exploring Data as a Service (DaaS) in Data Engineering and Data Science

As we know that cloud services came into the picture, during the late Y2K, and we started speaking PasS, SaaS, IaaS, etc. I hope you all know those three main pillars of cloud computing with the suffix “as a service” and each has its own degree of capabilities and responsibilities. The digital industries are growing anything like that by adopting and bringing new services to grow and enhance.

This is happy on both sides, I mean business development at the organisation side and opportunities for IT sectors to provide services with new roles and portfolios and increase their revenue. In this article, we will discuss one another “as-a-service” specific to the Data domain.

Introduction

Just understand infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS). Here DaaS is nothing but Data-as-a-Service (DaaS) solutions, by architecting a variety of business solutions for Data-as-a-Service to securely -manage the DATA properly.

Just I am recalling the Quote.

                                            “The world’s most valuable resource is OIL”,

Yes! OIL is valuable but DATA is invaluable – What is mean? invaluable is always an adjective and it means “PRICELESS” “ESSENTIAL” and “VITAL”. Keeping this quote in the mind will move on in this article.

OIL and DATA have some similarities, I mean the raw oil cannot be used directly for kind of usage hope you are aware of this, in the same sense that the data itself rather, the value has been created from data after undergoing multiple processes and handling them in the right way and sense to obtain-inevitable insights and convert them business-opportunities, we couldn’t achieve this since this data is in siloes in nature

Is DaaS is Data Management Strategy

Yes! DaaS is a data management key strategy of collection, data storage, processing, and data-deployment model on the cloud, it all cloud providers are following all these stages to deliver a variety of data-analytics service which includes BI, Data Science Platform, API, and other downstream Application(s).

We handle massive data workloads and process large data on the cloud, after every successful enhancement in cloud computing, the reasonably unlock the cost-effective technology for large-scale data management processing and analytics solutions. So, without question, DaaS become a more comfortable zone for Data and its major activities are in the cloud environment.

Is DaaS like SaaS, and how it is different?

If you ask, DaaS is like SaaS, my answer is YES, but it has its flavours.

As we know that SaaS breaks the classical strategy by removing the Software installation and managing and monitoring them locally, in another way DaaS has taken care of Data storage, processing Integration and serving operations in a cloud environment. This strategy has been handled in an extradentary way by several cloud service providers (Azure, AWS and GCP).

Remember that cloud computing provides high computational speed and storage capacity, which is not in scope while dealing with the same on-premises. Top cloud providers are providing low-cost cloud data storage and extensive bandwidth based on demand. Certainly, we can build massive cloud-based platforms for the fastest, large-scale data management and processing perspective.

Already I have mentioned DaaS is a cloud strategy used to simplify the critical data accessibility for different domains, it would be Manufacturing, Health Care, Pharma, Banking and Financial Services and promising services could be finding the affordable outlay and inducted into business. The outcome of the DaaS can be provided useful data and it can be supplied to business users on demand and pull the insights, irrespective of geographical and specialized domains-based divisions between providers and their consumers.

DaaS Architectural overview

The below diagram shows the DaaS architectural view. Providers and Consumers are residing on both sides – left and right respectively.

Deciding the relevant data from a consumer point-of-view is DaaS’s first and foremost-thump rule.

Before them integrating, data needs to be cleaned up and integration is a major task by applying the business rules. So, we heavily adapt the ETL or ELT based on the nature of data and requirements.

Followed by this there would be the simple to complex level data integration which would be stored in Data Storage and exposed to BI, Data Science, and downstream applications with API, sometimes to external DW systems.

Benefits of DaaS

When successfully implementing any solution(s), it can promote the whole business and lead beneficial to their customers. Let’s discuss the most meaningful benefits of DaaS.

(A) Data Storage and Accessibility

Data storage and its management is an exclusive benefit after implementing DaaS successfully.

• Data accessibility across the global
• Capability of Data movement
  • Data can easily move from several applications.
  • Data can be moved from one platform to another.
• Optimized cost benefits with a DaaS solution
  • Data Processing
  • Data Management

•     Downtimes are extremely-insignificant (Cloud providers will take care of this)

• Cloud infrastructure maintenance is Not in our scope.

(B) Lowering Expenses and Operational efforts

Eventually, DaaS solutions help to reduce the below factors.

• The operational expenses – saving resources and time.
• Taking exclusive data-driven decisions faster manner.
• Extracting valuable insights.
• Deriving customer behavioural patterns.
• Enhanced services, innovative data products etc.,

All this happens by leveraging the wide range of data acquired from different sources.

(C) Facilitating Innovative Solutions and Decision Making 

The DaaS enables better strategic decisions and innovative solutions through effective data management. Leading multiple innovative analytical products and solutions for hidden problem statements.

• BI tool.
• Data Science/ML/AI solutions.
• Exposing API to an internal or external application or DW.

Same time it provides data access to the right stakeholders at right time aligning with Data Quality and Governance aspects.

(D) Data-driven Culture

DaaS would introduce a new culture into the organisation by making awareness of the importance of data and the quality of the data for the entire journey right from consumption to service layer and straightening the overall data in all layers of its architecture and providing exclusive information to every team based on their business needs. With the help of the below pillars, business users can be aware of all these aspects and support generating quality data and valuable insights.

• Data Quality
• Data Privacy
• Data Policies
• Data Governance

(E) Data Modernization and Monetization

Data Modernization and Monetization are rigidly coupled and bringing modernise the data landscape to help whole enterprises become data-analytics-driven towards monetising Org-data. the major focus of modernization rejuvenating the enterprise data to deliver their business goals, and strategy and bringing possible cost reduction with the latest technology bandwidth.

Without question, DaaS can direct “Data Monetization”, this is nothing but the process whereby we use to carry during DaaS implementation, and the data would create a significant economic benefit.

Challenges of DaaS

Always space for challenges would exist in technology and its services, with No exception while dealing with data, Since I have mentioned earlier their DATA has similarities with OIL and in the recent scenario, there was a lot of data protection aspects, policies, and compliance around in the global market like GDPR, PII, CCPA and CPRA. During the DaaS implementation we must focus on security, privacy and governance are the most common concerns. Since we’re moving the customer and organization-specific data into the cloud and there, might be issues and concerns over the highly-sensitive personal and corporate data.

How to handle the situation, yes! There are multiple ways to handle this by sharing encrypted data and moving on to the cloud and making sure data is a reliable data source. And providers giving the promise on these parts.

• Data Protection (GDPR)
• Data Policies (PII)
• Data Compliance (CCPA, CPRA)
• Data Governance

So, well-defined Data Governance and Policies, surely streamline security and keep away from threats.

Conclusion

So, we have discussed the different characteristics of DaaS in and out. Let me rephrase and summarise below and I am sure you have gained some knowledge on this service.

• DaaS is a critical data management process.
• DaaS uses the cloud service for different operations.
  • Data storage
  • Data Integration
  • Data processing
  • Data analytics capabilities and many more
• DaaS enhances business agility with the power of DATA, which is generated by the business day-to-day.
• DaaS is a perfect option for a business to expand its business strategy with available a large volume of data.
• DaaS is the traction tool to find innovative data products and expensive solutions.
• DaaS facilities unstructured business data into structured data for intelligence solutions.

We should remember that when you implement DaaS correctly, then only the service provider and the customer would get the benefits.

• Benefit aspects we have discussed below
  • Data-driven Culture
  • Data Storage and Accessibility
  • Data Modernization and Monetization
  • Expenses and Operational efforts
  • Innovative and Decision Making

Hope you all enjoyed this article and will come up with more interesting topics in DATA and its utilization standpoint. Thanks for your time! -Shanthababu