More and more organizations are moving to cloud infrastructure to modernize their operations. This has given rise to the importance of the already invaluable data analytics services. Now, Data as a Service, or DaaS, is also becoming a popular solution. It can help streamline the process of data integration, management, storage, and analytics.
Companies that choose to use DaaS can benefit significantly, as it will help improve the agility of the organization’s data workload while increasing reliability and integrity and reducing the time it takes to get to the insights.
Data as a Service (DaaS) is among the fastest-growing trends in the big data industry. According to research by Verified Market Research, the DaaS market size was USD 14.1 Billion in the year 2022, and it is expected to reach USD 142.7 billion by 2030. The CAGR of 29.31% starting from 2023 to 2030 makes it an important industry to monitor.
In this post we explore what DaaS is and how it can be best utilized by businesses.
What is Data as a Service?
Data as a Service (DaaS) is a data management strategy that utilizes the cloud to store, integrate, process, and analyze data using the network connection. It is a new way to think about data.
Data as a Service is made to assist organizations that have big data and turn it into invaluable insights that get them one step closer to their business goals. DaaS is ideal for big data analytics and when the organization does not want to invest crucial time and money in creating a solution from scratch.
DaaS provides users with access to a platform that contains all the tools they would need to analyze their data effectively and efficiently.
An Overview of How Data as a Service (DaaS) Works
Data as a Service (DaaS) is a comparatively new business model that offers organizations an opportunity to get value out of their data. DaaS is an alternative to the traditional approach of Infrastructure building and management used for storing and processing large volumes of unstructured data.
The DaaS model helps organizations leverage the power of cloud computing services such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. All they need to do is subscribe to their DaaS services instead of building an infrastructure for storage and processing from scratch. The use of DaaS allows for greater flexibility in terms of resource allocation and cost savings due to economies-of-scale associated with using large-scale public clouds rather than private clouds.
Data Analytics Services - DaaS best practices
Data as a Service (DaaS) is the cure to the issues that arise with the use of a traditional data lake. It enables a flexible way to manage your data and offers many benefits over the traditional approach.
Data lakes have been here for a while, but many organizations still struggle with them due to their complexity and cost structure. Building data lakes requires significant investments in hardware, software, and expertise to deploy effectively.
The use of such technology might be necessary when working with large amounts of unstructured data, but the smaller projects where structured data sources are available, the use of these additional resources is pointless. Data analytics service is available to businesses that want to leverage advanced technology to process their data.
Project-based V/S Platform-based approach
When it comes to data analytics services, you must consider Data-centric versus model-centric approach. Then, you must choose if you want a platform or project-based approach. Here is the difference between the two:
Platform-based approach
The platform-centric approach offers a framework and best practices that have worked for several organizations’ various departments.
- With a platform-based approach, you have an initiative with clear outcomes.
Empowered stakeholders
Equal responsibility division between source and group organization
Lowering maintenance cost
Project-based approach
- The project-based approach is more expensive
It requires the databases to be made from scratch for every project.
Restricts the use of the same tools and existing data lakes to one project.
Can lead to data silos, where only limited team members have access to data.
Data as a Service: Your Gateway to an easy and cost-effective way to manage Big Data.
DaaS makes it easier to manage data, so you can focus on developing insights for your business. You can access powerful analytics software with DaaS at any time without investing in the infrastructure or software licenses.
Conclusion
Data analytics service work because it allows businesses to utilize big data without shelling out an unreasonable amount of money. So, if your data needs are within limits, utilize Data as a Service. However, if you need a more bespoke approach to your business data, opt for custom solutions using an IT company.
MoogleLabs is an advanced analytics service company here to help you meet all your data needs within budget, as per the platform or method of your choice. Get in touch with us today, and we will help you turn your data into insights.