During recent years, technology provision models have evolved towards subscription and pay-per-use services, always supported by the capabilities of the cloud and the functional versatility of providers. Along these lines, companies can find infrastructure and an extensive software catalog to support their business needs. This scenario of massive consumption of information has led to the rise of models Data as a Service (DaaS).
“Data as a Service” provides an evolutionary perspective of the information storage and provision business based on access to quality and added value data, using a Cloud service. In this way, hosting, part of the processing and, sometimes, the analysis in intermediate layers are replaced or complemented by a DaaS subscription, which allows obtaining the information that is needed ready to be used.
This model revolutionizes the way companies approach the development of analytical solutions and artificial intelligence models. By offering access to vast, diverse and up-to-date data sets, DaaS allows organizations to customize their solutions more accurately and efficiently.
Thus, aware of the current situation of the technological market and customer demands, Innova-tsn experts have identified doubts, challenges and benefits that may arise when adopting this approach:
- Where does the data come from? From various sources: the provider’s own data, third party data or generated by customers.
- Benefits. Saving time and resources: pay per use in infrastructure, storage and processing. Access to high-quality data, greater agility in access, more potential for innovation and greater capacity in customizing solutions.
- Risks. Potential supplier dependency. Data security and quality may be compromised if the vendor does not provide the required guarantees. It is vitally important to select a suitable partner based on existing needs.
- Who is it for? For any company that needs data to make more informed decisions, even companies with their own storage systems.
- How does it work? Once the subscription is contracted, organizations gain access to the data through an interface or API.
- Data examples. Demographic and socioeconomic data, market data from different industries at different levels of aggregation, data from sensorized environments, data from social networks, financial and transactional data, genomic data, etc.
- Suppliers. Companies specialized in data and large hyperscalers such as AWS, Azure or GCP.
- Solution customization. DaaS allows you to segment information, enrich and access new data in an automated, updated and recurring way. A suitable partner therefore facilitates the creation of highly personalized AI models adapted to the specific needs of each organization.
- Streamlining development. By working with preprocessed data, DaaS accelerates the development process and enables faster iteration of analytical models.
- Innovation. It facilitates the discovery of new insights of business, allowing more time for the development of innovative solutions based on data.
In conclusion, DaaS not only provides access to information, but also powers the creation of more personalized and efficient analytical solutions and artificial intelligence models.
