What Is Data as a Service (DaaS) & How It Helps Your Business Grow

Hey there!

Data is the fuel that powers business growth in today‘s digital age. As an organization, you likely deal with vast amounts of data on a daily basis. But building and managing your own data infrastructure can be complex, expensive and time-consuming. This is where Data as a Service (DaaS) comes in – a new model that helps businesses like yours leverage data in impactful ways without major infrastructure investments.

What is Data as a Service?

Data as a Service (DaaS) refers to the provision of data on-demand via a subscription model by DaaS providers. It is a cloud-based delivery model that enables organizations to access curated datasets remotely through APIs without having to manage the underlying infrastructure.

With DaaS, data providers take care of sourcing, cleansing, managing and delivering data to users in a readily consumable format. DaaS eliminates the need for you to build and maintain expensive data centers and analytics systems. You pay only for the data you require rather than investing in expensive hardware and software.

According to MarketsandMarkets, the DaaS market is projected to grow from $1.5 billion in 2019 to $12.0 billion by 2024, at a compound annual growth rate of 50.1% during this period. This rapid growth demonstrates the value DaaS unlocks for organizations by making data easily accessible.

How Does Data as a Service Work?

DaaS leverages cloud computing technologies to deliver data on-demand through APIs and web services. Here are the key components of a DaaS architecture:

Data providers – Organizations that aggregate data from disparate sources, process it, and make it available to users through well-defined APIs and dashboards. Providers include both large cloud platforms like Amazon Web Services and Microsoft Azure as well as specialized DaaS companies.
Cloud infrastructure – The database systems and analytics engines used by data providers to store, process and deliver data are hosted on public, private or hybrid clouds. This eliminates the need for you to provision any infrastructure.
APIs and dashboards – Data providers expose a range of APIs for you to access the processed data in standard formats. Visual dashboards are also provided for easy data analysis without needing technical skills.
Data consumers – Organizations like yours that subscribe to DaaS services and leverage ready-to-use data via APIs/dashboards for various business use cases like analytics, reporting, AI/ML model training etc.
Data integration – Tools provided by DaaS providers to integrate data from various sources into your own databases and applications if needed. This enables combining external DaaS data with your internally generated data.

DaaS architecture (Source: XenonStack)

As you can see, DaaS provides a simplified way to leverage data without investing in complex data infrastructure. Next, let‘s go over some of the key benefits you stand to gain by adopting DaaS.

Key Benefits of Data as a Service for Your Business

DaaS opens up immense possibilities for you to utilize data and analytics more effectively. Here are some of its most notable benefits:

1. Flexibility and scalability

Pay-as-you-go pricing allows usage scaling up and down based on your changing data needs.
You can access more data volume and processing capacity on demand without any infrastructure limitations.
Eliminates the need for you to make large upfront investments.

This makes DaaS ideal for organizations with dynamic data requirements that vary based on campaigns, new product launches, growth in users, etc. You get data exactly when you need it and pay only for what you use.

2. Faster access to data

Get near real-time access to processed datasets without delays in provisioning infrastructure.
Data from multiple sources is delivered via unified APIs without the need to build customized connectors.
DaaS providers continually ingest the latest data from thousands of sources.

This enables your business users and data scientists to get data faster for analytics and modeling. You are no longer limited by lengthy IT processes for accessing new data.

3. Reduced costs

No expenses related to hardware/software acquisition, maintenance and upgrade.
No need to hire dedicated data engineers and analysts, saving significantly on labor costs.
Pay only for the amount and time period of data usage rather than overprovisioning resources.

According to a Total Economic Impact study by Forrester, companies using Snowflake’s DaaS solution saved 72% on infrastructure costs and 65% on labor costs. The savings can be reinvested in other parts of your business.

4. Enhanced data quality and governance

DaaS providers continuously screen, transform and enrich data for accuracy before making it available.
Strict data security measures and compliance with regulations geared towards enterprise needs.
Usage of data is governed through service contracts.

You avoid issues resulting from low-quality data like incomplete reports or erroneous analytics. The burden of ensuring data quality and compliance is handled by the DaaS provider.

5. Improved analytics productivity

Ready availability of clean, unified data frees up significant time earlier spent in preparing data.
Your focus shifts from data management to higher-value analysis and quickly generating insights.
Easy-to-use dashboards enable self-service analytics by business users.

Data preparation can account for up to 80% of the effort before analysis. DaaS alleviates the tedious and time-consuming aspects of data-related work, enabling your team to deliver insights faster.

6. Democratization of data access

Business users without technical expertise in data management can leverage DaaS for self-service analytics.
Easy data discovery and exploration empowers employees to leverage data.
Valuable insights no longer limited to analytics experts.

This leads to data-driven decision making permeating across the organizational culture. More insights get generated through wider data access.

Clearly, DaaS opens up tremendous possibilities for your business to utilize data like never before. But it also pays to be aware of some potential challenges and mitigation strategies.

Challenges of Adopting Data as a Service

While DaaS offers many advantages, you need to be aware of some key considerations when it comes to implementation:

Data security

Storing data with third-party DaaS vendors raises valid data privacy and security concerns. Proper due diligence of their security measures is required.

Mitigation: Ask detailed questions around encryption, access controls, breach notification policies, and compliance audits. Include strong security terms in the service contract and get guarantees on prompt breach notifications.

Vendor lock-in

High data transfer costs and integration complexities may restrict moving from one DaaS provider to another. This can limit future flexibility.

Mitigation: Architect integration of DaaS using open APIs and standard data formats. Perform regular technology evaluations to prevent over-dependence on one vendor.

Integration with legacy systems

DaaS data needs to be integrated with your in-house databases, analytics platforms and other business applications. APIs and data models may not align perfectly.

Mitigation: Involve IT teams early and invest time in reconciling data structures. Use ETL tools to normalize and map data for easier integration.

Network bottlenecks

Large volumes of data transfer between DaaS platform and your site can cause network latency impacting performance.

Mitigation: Implement intelligent data caching, compression and movement planning to throttle traffic. Upgrade network bandwidth as data needs grow.

Compliance considerations

DaaS systems must comply with data governance regulations like GDPR, CCPA etc. for the regions you operate in.

Mitigation: Clarify responsibilities contractually to ensure regulatory compliance and obtain audit certifications. Restrict data access to only compliant DaaS platforms.

Vendor viability

There is some risk of service discontinuation if the DaaS provider shuts down. Possibility to migrate data and switch vendors should be assessed upfront.

Mitigation: Choose established vendors with robust business models. Seek service guarantees and assess provider financial health periodically.

With adequate planning and partnerships with reliable DaaS providers, these risks can be effectively managed.

Common Data as a Service Use Cases

DaaS supports a diverse array of data-driven use cases across industries. Here are some examples:

Business Intelligence

Market, competitive, pricing data for sales intelligence
Customer demographic, sentiment, behavior data for marketing analytics
Financial performance data for reports and dashboards

AI and Advanced Analytics

Structured and unstructured datasets to train machine learning models
Real-time data feeds for self-learning predictive analytics systems

IoT and Real-time Analytics

Streaming data from sensors, equipment and devices for monitoring
Applying analytics for tracking, anomaly detection, predictive maintenance

Data Warehousing

Cloud data warehouse for storing enterprise datasets
Integrating data from multiple sources for business reporting
Self-service BI capabilities for business users

Master Data Management

Reference data like customer, product or supplier master data
Managing data integrity and quality standards
Single source of truth across the organization

Embedded Analytics

Supplying data and analytics capabilities embedded into applications
Enabling analytics-driven features for software products

The use cases span both big data analytics like IoT, AI and ML as well as broader operational reporting and visibility through BI tools.

Let‘s look at three example DaaS use cases in more detail:

Marketing Analytics

DaaS empowers your marketing teams to incorporate external consumer demographic, social media listening, competitor intelligence and other datasets with campaign data to uncover richer insights and optimize spend across channels.

You can also combine first-party sales data with market research data sourced through DaaS to understand customer segments and buyer journeys better. Instead of relying solely on internal data, access to external datasets helps uncover blind spots.

Predictive Maintenance

Combining real-time IoT sensor data from industrial equipment with historical fault data and machine learning algorithms can enable you to predict failures before they occur.

DaaS provides easy access to mass volumes of high-velocity streaming data from sensors and tools to run distributed analytics at the edge. Predictive models trained on the data can identify anomalies and alert operators.

Customer 360

Creating a holistic view of your customers requires integrating data across CRM, purchases, web activity, transactional history, demographics and more.

DaaS serves as a cloud-based data hub bringing together datasets from across your organization as well as incorporating enriched third-party profile data. Applying analytics on this unified customer 360 view provides contextual insights for hyper-personalization.

As you can see, virtually any use case involving large, fast or combined datasets can benefit from DaaS. The cloud delivery model makes the economics and scale of big data accessible to businesses of all sizes.

How DaaS Compares to SaaS

SaaS and DaaS are two popular cloud computing models. Let‘s look at some key differences:

Parameter	SaaS	DaaS
What is provided?	Applications hosted in the cloud	Data provisioned via cloud services
Target users	End business users	Data scientists, analysts, developers
Technology	Web/mobile apps, APIs	Databases, data warehouses, lakes
Pricing model	Per user/month, usage-based	Based on data volume consumed
Role of provider	Manages applications	Aggregates, processes, manages data
Role of consumer	Uses applications	Consumes data through APIs

While SaaS provides access to software applications, DaaS focuses on just providing data. SaaS is an end-user computing model while DaaS serves to empower technical roles across the organization.

Both models provide convenient on-demand access to IT resources through a subscription rather than ownership model. The choice between SaaS and DaaS depends on your specific requirements – software functionality versus data for analytics. Many organizations adopt both SaaS apps as well as DaaS.

Why Leverage Oxylabs for Web Data Extraction?

Oxylabs provides robust and scalable data extraction solutions for gathering web-based data. Instead of investing time and resources into building internal scraping tools, you can leverage Oxylabs‘ infrastructure for your data collection needs.

Here are some of the key benefits of using Oxylabs:

Powerful data extraction APIs

Oxylabs provides prebuilt APIs in Python, Java, Ruby, PHP and NodeJS for scraping data from websites, APIs or databases. This accelerates your development.

Cloud-scale web scraping infrastructure

Their distributed network of over 1 million residential proxies and browsers enables high-performance web scraping at scale. You get enterprise-grade data extraction capabilities without operational overheads.

Expert support

Get access to expert assistance from Oxylabs to guide your implementation, customize extraction workflows and troubleshoot any issues.

Compliance knowledge

Oxylabs has extensive experience navigating data regulations, acceptable use policies and legal risks related to web scraping. They can provide guidance on mitigating compliance exposure.

Trusted by leading companies

Oxylabs‘ customer base includes Fortune 500 companies and innovative startups across diverse sectors like retail, finance, healthcare and technology.

Before embarking on any web data extraction initiative, be sure to consult your legal team and a provider like Oxylabs with specialized data sourcing expertise.

Key Takeaways on Adopting Data as a Service

Here are some important tips to guide your DaaS strategy:

Evaluate potential use cases where external datasets can drive business value – marketing analytics, machine learning and customer 360 are prime examples.
Research leading DaaS providers based on data sources, tools, security measures and service reliability. Look beyond just hyperscale cloud platforms.
Start with a targeted pilot project to validate DaaS benefits before larger-scale adoption. Focus on high-ROI use cases.
Assess gaps in your internal skill sets – data science, analytics, infrastructure – that DaaS can help address cost-efficiently.
Plan integrations between DaaS data and your internal systems early. Use standards, optimize networks and leverage integration tools.
Monitor service levels, data usage and costs closely. Scale up and down based on changing needs.
Use DaaS as a launch pad to become more data-driven across sales, marketing, operations and customer engagement.

Conclusion

Data as a Service opens up agile, scalable data leverage for modern organizations without infrastructure burdens. It unlocks an enterprise-wide analytics foundation. But DaaS should be adopted keeping in mind considerations around security, contracts, integrations, costs and use case priorities.

Approached strategically, DaaS allows you to extract exponential value from data through access to the right datasets. This can ultimately translate to higher operational efficiency, lower costs and reduced time to data-driven insights for your business.

I hope this guide helped you gain clarity on DaaS benefits and best practices for getting started. Please feel free to reach out if you need any other details as you explore adopting DaaS in your organization.