DaaS, data as Service
An overview of the DaaS model: benefits, challenges and main applications in organisations
[Published in: Digital Biz Magazine]
Technology provision models have evolved towards subscription and pay-per-use services, supported by the capabilities of the cloud and the functional versatility of the providers. In this respect, companies can find infrastructure and an extensive catalogue of software to support their business needs. It is not surprising, therefore, that in a scenario of massive information consumption, the concept of data as a service (DaaS) has also emerged.
The data as a service (DaaS) model provides an evolutionary perspective of the information storage and supply business, based on the access to quality data with the added value inherent to cloud services. Roughly, in this model, the hosting, part of the processing and sometimes the intermediate layer analysis, too, is replaced —or complemented— by a DaaS subscription. This allows us to obtain the necessary information.
This model revolutionises the way companies approach the development of analytical solutions and artificial intelligence models. In addition, and this is important, by offering access to vast, diverse and up-to-date data sets, the DaaS approach allows organisations to customise their solutions more accurately and efficiently. But how far do the benefits promised by this model really go and how is it implemented in organisations?
Data as a service globally
According to data from Grand View Research, the data as a service model is booming and the size of the global market proves it: for example, last year the estimated turnover was close to 14.36 billion dollars and it is expected to grow at a year-on-year rate of 28.1% from 2024 to 2030.
The integration of artificial intelligence and machine learning in DaaS platforms is transforming the way companies analyse and use data. AI-optimised analyses offer deeper insights and better predictive capabilities. This allows organisations to anticipate trends and make informed decisions. Thus, this type of model increases companies’ efficiency and competitiveness in an increasingly global and therefore more demanding market.
DaaS revolutionises the way companies approach the development of analytical solutions and AI models.
This growth is largely driven by the organisations themselves and their technological partners, who are seeking to meet their constantly evolving needs. This gives rise to new demands in areas such as security, regulatory compliance and the ability to have scalable and interoperable models that require the integration of any type of data, regardless of its nature or business value.
For example, an IDC report estimates that, by 2025, 80% of the world’s data will be unstructured and this will pose an enormous challenge for all those companies that want to take advantage of their information but do not have the right solutions to extract real value. Here, models such as data as a service become very important.
Benefits and challenges
This type of cloud-native model provides easier access to all kinds of information regardless of their origin. Moreover, through capabilities based on artificial intelligence and machine learning, it is possible to extract patterns and trends from unstructured data sets, which marks a turning point for those organisations whose data lineage is not of optimal quality.
In order to assess whether a model of these characteristics can provide this or any other of the expected benefits of DaaS, it is necessary to be aware of the structural capacities that each company has so to adopt them, something that in turn presents several challenges.
Gartner estimates that companies can save up to 30% on storage and data management costs after eliminating infrastructure overheads. However, quantifying the potential payback in this area will require a case-by-case assessment of the location of data sources and whether the existing architecture is supported by a depreciated infrastructure, as well as the performance it is delivering.
Proper use of DaaS frees up time for the most qualified profiles to develop new initiatives or data-driven services.
As for agility in making decisions and scalability, data on demand make information more accessible and flexible for their analytical exploitation. This enables faster and more qualified decision-making that mitigates risk and provides competitive value.
To this end, it is necessary to bear in mind that, in order not to incur additional risks, a governed and secured environment must be in place. This needs to be accompanied by a feasibility plan that considers integration with existing systems and processes and that sheds light on the need for additional investments.
In this regard, it is important to consider that this type of service creates dependency on third parties, and it is advisable to address strategies to avoid vendor lock-in. It is therefore advisable to establish agreements that include continuity and migration plans. The guarantee of this output flexibility and data quality are two of the main criteria for selecting a provider.
Resistance to change
Finally, it is important to point out that the adoption of this model results in an increase in productivity and promotes innovation. Like any approach that simplifies processes and eliminates routine, lower-value tasks, the correct use of DaaS frees up time for more skilled people to develop new initiatives or data-driven services.
This is perhaps the most important challenge: tackling the resistance to change of the most reactive users. For this, the most effective strategy is usually based on the development of their personal skills and their involvement in the benefits that DaaS can bring to the business or to the performance of their activity.
In conclusion, no model is equally valid for all companies, and it is essential to analyse the starting situation and the goals of each company to assess their potential benefits.
DaaS is a new alternative that not only provides access to information but also empowers the creation of more customised and efficient analytical solutions and artificial intelligence models. By combining the flexibility of cloud services with the richness of data, it becomes a strategic ally for companies seeking to gain a competitive advantage in a marketplace increasingly driven by a good use of information.
