The Rise of next-generation data platforms and its impact on IT decision-making

Deriving value from data is arace that businesses cannot afford to lose, whether in India or the rest of the world. Gartner forecasts the spending on analytics software in the country to reach USD 495 million this year, an 18.2 percent increase from 2022.

Data platforms are a crucial element of any enterprise's competitiveness. It connects the organisation's data science teams with the ever-growing amount of data from an increasing variety of traditional and unstructured sources.

Next-generation data platforms provide opportunities for businesses to gain an edge over their competitors. But to do so, business and IT leaders must fully understand what sets them apart from previous generations of data platforms, how it can impact their operations and processes, and how they can make the most of its benefits.

Key features of next-generation data platforms

The next generation of data platforms use cloud-native architecture to utilise the powerful resources of cloud computing. Many of these platforms are serverless, enabling continuous availability of services, zero downtime, and higher data-processing speeds.

Next-generation data platforms can handle structured, unstructured, and semi-structured data. Enterprises can access more data formats from a wider variety of sources such as IoT devices, social media, and streaming data. Users no longer have to redefine data models just to add new data types and fields to a database.

Using microservices and containers, these data platforms are flexible and allow for easy configuration and reconfiguration. Capacity is scalable so users can easily increase resources when needed. They can then easily reduce both amount and speed once peaks of activity are done. Enterprises do not have to spend on database capacity beyond what is regularly needed.

Besides controlled spending on capacity, next-generation data platforms also have a lower total cost of ownership (TCO) because there is no need for ancillary software and specialised hardware.

Impact of next-generation data platforms on IT decision-making

Speed is the name of the game when it comes to deriving insights from data. Delivering products and services before the competition is a serious advantage. With the next generation of data platforms, organisations can derive near-real time insights through better processing speeds and the usage of structured, unstructured, and semi-structured data from various sources.

Just as important as delivering new products and services faster than the competition, enterprises also have to ensure compliance with data governance and privacy regulations. According to IBM, the average total cost of a data breach in India last yearwas INR 176 million, a 6.6 percent increase from 2021 and a 25 percent increase from 2020.

With serverless architecture, next-generation data platforms help enterprises comply with regulatory requirements such as the European Union's GDPR. Businesses can easily replicate their data across multiple data centres with zero risk.

The agility of next-generation data platforms enables users to quickly scale up with peak demand without having to spend on resources that are unused the rest of the time. Organisations can easily meet demand while being judicious with costs.

Considerations for implementing next-generation data platforms

Organisations should choose a platform that best suits their needs and select a deployment model that aligns with their IT strategy. Before implementing a next-generation data platform, business and IT leaders should identify their organisation's business requirements and clarify their IT strategy. Doing so will allow the enterprise to deploy the platform in a way that maximises its benefits to the company.

Aside from public and private clouds, organisations can also deploy a hybrid cloud model for their data platform. With this model, the services of a private cloud and at least one public cloud are combined.

Critical and sensitive data is located in the private cloud, using the enterprise's own firewall. This enables businesses to comply with data governance and privacy regulations better. Enterprises can also reduce costs and maximise access to files that need to be updated frequently. The public cloud, on the other hand, is used for on-demand scalability, dynamic workload management, and accessibility from anywhere in the world.

Three of the most common hybrid cloud deployments are tiered, edge, and cloud-bursting:

• With a tiered deployment of the hybrid cloud, frontend applications run on the public cloud while backend applications are on the private cloud. Organisations benefit from this model through better front-end performance.
• Edge hybrid deployment, on the other hand, uses private cloud servers to provide cloud computing to local devices. Along with the use of the public cloud to receive transferred data, this results in significant improvement in user latency.
• In a cloud-bursting hybrid model, the public cloud is used during demand spikes while the private cloud is used during normal operations.

Last but not least, organisations can also consider a multi-cloud deployment model. While this strategy uses two or more public cloud services, it is similar to the hybrid cloud in that both approaches provide agility and scalability to their users. Users avoid attempts by a single cloud vendor to lock them in at a high cost.

Conclusion

Next-generation data platforms use cloud-native architecture and serverless technology to provide services with continuous availability, high processing speeds, scalability, and lower TCOs. This flexibility enables enterprises to draw insights faster while complying with regulations such as the GDPR. 

Before deploying a next-gen platform, organisations should identify their business requirements and clarify IT strategies to achieve maximum benefit from the new system. Businesses can choose from public or private clouds for deployment models but have further options such as hybrid deployments that include tiered, edge or cloudbursts; or multi-clouds for greater agility and independence at low cost.

Authored By: Deb Dutta, General Manager, Asia Pacific & Japan, DataStax