The Data-as-a-Service, or DaaS market, is expected to grow at a rate of nearly 40% every year, reaching a market size of $51.9 billion USD by 2025. Clearly, data-as-a-service is a technology solution of great interest to the business community.

Besides being relieved of the responsibility of data management, data-as-a-service offers companies many additional benefits.

DaaS allows for a single, up-to-date source of truth, eliminating the confusion caused by out-of-date data; data can then be used in analysis or AI applications with greater confidence, reducing the risk of incomplete or misleading results. It can also improve accessibility to information across the organization, eliminate data silos, and improve collaboration between teams and functions.

While data analytics can create competitive advantages for early adopters, many companies are having difficulty successfully implementing an analytics program. The main challenges to successful data analytics include: 

• Volume: The sheer volume of data available is difficult to manage, interpret, and use.
• Cleansing: Ensuring that data is accurate, current, and suitable is a job in itself, with data cleansing and preparation taking up to 40% of an analyst’s time.
• Homogenization: Data accumulated from different sources must be formatted and normalized so that it can be used collectively.
• Storage: Creating and managing the infrastructure required to store data onsite can be a resource-intensive proposition.
• Security: Balancing the need to secure data with the need for it to be accessible to users can be difficult to manage.
• Labor shortage: There is a shortage of skilled and experienced data scientists, and this limits the organization’s ability to harness data science.

DaaS can help an organization cope with the challenges of data management.

Besides being relieved of the responsibility of data management, data-as-a-service offers companies many additional benefits.

DaaS allows for a single, up-to-date source of truth, eliminating the confusion caused by out-of-date data; data can then be used in analysis or AI applications with greater confidence, reducing the risk of incomplete or misleading results. It can also improve accessibility to information across the organization, eliminate data silos, and improve collaboration between teams and functions.

However, there are some concerns about adopting DaaS, and companies considering DaaS solutions must be sure to evaluate a vendor’s ability to support the company’s operational requirements and long-term business needs. These concerns include:

• Features: Ensure that your company receives the correct amount and type of data in a usable format. This is the primary concern with implementing data-as-a-service.
• Scalability: As your business grows and changes, your data needs will grow and change as well. A data-as-a-service vendor should be able to scale to meet current and future needs of the business.
• Privacy: Sensitive data sent to a third-party raises concerns about privacy. When selecting a vendor, it is important to ensure that any data shared will be kept private.
• Security: Transferring data to and from a DaaS vendor means that precautions must be taken to guarantee that information is protected during the transfer, at the vendor location, and at the storage location.
• Compliance: There are a growing number of regulations governing data storage, usage, accessibility and protections. With a third party involved, it is critical to clarify which party owns responsibility for each aspect of data management.

Data-as-a-Service can provide a number of benefits to an organization that is implementing an analytics program, or that wants to maximize the value of an existing program. With some planning and organization, DaaS can enable a company to receive usable data that has been cleansed and formatted, to optimize the use of analytical talent and achieve strategic goals.

https://www.prnewswire.com/news-releases/big-data-as-a-service-market-size-worth-51-9-billion-by-2025-grand-view-research-inc-300919412.html