The answers we seek using AI are called “outputs”. These outputs are derived from datasets that take the role of inputs. The results would take us in the wrong direction if there are any wrong interpretations of the datasets. Therefore we can assure that the efficiency of an AI model depend on the accuracy of its data. In the same way, since every field across the world are data-driven, AI algorithms are of dire need for decoding the information present in the abundant amount of data available. Several data platforms across the globe provide data as a service. But what is its future? To understand this Market Research Future conducted a study on “Data as a Service Market information by Deployment: by organisation Size, by End User, by Pricing Model and Region – forecast to 2030”.

According to the study, it was stated that the Data as a service market is anticipated to grow at a CAGR of 36.9% and it will reach up to 61.42 billion by the end of 2030. Data as a service will be able to effectively provide life storage of data, processing, analytics and so on. With the enhancements of the data sphere, the industries will be benefitted from the betterment of data agility. They will be able to reduce their time to insight and increase the reliability and integrity of data.

Catalysts for the growth of data as a service

The study states that the high-level security of data ensured by the models has benefitted the enhancement of data as a service during the COVID-19 pandemic. When enterprises began working from remote, the employees had become vulnerable to cyber-attacks. The demand for data security grew. Hence, security measures such as password and data logging became a part of protocols of the companies. 

When financial services are concerned, data as a service has been an aid in the matters of auditing and accounting. The auditors are getting hold of the informations regarding the clients than ever before, which enables them to have an in-depth understanding of their clientele and have further investigation on areas in need. The models carry out effective performances by removing data redundancy and streamlining operational costs. Reduced set-up time needed for otherwise time-consuming data process has turned tables in favour of the data as a service market.

Limitations and Challenges

According to Market Research Future, the data transferring process is time consuming. The time taken is directly proportional to the size of data and the frequency of the organisation. Also, limited bandwidth will slow the process of data transfer. To overcome these hurdles of data transfer, companies are trying to adopt data compression techniques and other computing strategies to increase the speed of the process. One of the major challenges in the data as a service is the process of data verification. Factors such as these made the companies lower their quality standards. Also, data as a service is not protected from information leaks.

Future of data as a service and AI

With most companies in the market adopting big data services, the rise in demand for AI has also been heightened. Investments and talent acquisitions have released open AI hardware and software, making one of the AI algorithm- Deep Learning the most demanded tech trends in recent times. The growth of new techniques and tools for the analysis of data has widened the scope of AI in the process. With highly efficient AI, the data will be able to interpret in the most structured way possible. AI and data as services will redefine the future of the global market in the coming years.