How data migration now drives innovation in the era of GenAI

Blog Post • 6 min read

The cloud provides strategic advantages ranging from flexibility and efficiency to strategic value — and it has done so since its inception. 

In fact, it’s no exaggeration to say that cloud infrastructure has enabled a paradigm shift in the efficacy and affordability of IT platforms. After all, Infrastructure as Code (IaC) has enabled best practices in design, continuous improvement in security, and speed of deployment, which have all driven game-changing efficiencies in time and cost and enabled previously unimaginable opportunities.

But this is no news to anyone, and these benefits are now realised and leveraged the world over - because that was just stage one in the evolution of the cloud, and how it has evolved. 

In this blog, we look at the cloud’s incredible advancements and how its use of generative AI (GenAI) now provides a compelling reason to move both data and data tools to the public cloud.

The evolution of the cloud

We are currently in the third phase of cloud innovation. These phases are as follows:

  • 2000s: Infrastructure 
  • 2010s: Platforms 
  • 2020s: Data (AI) 

2000s

In the 2000s, the world began moving its infrastructure to the cloud on a grand scale. The motivation behind this was (for the most part) infrastructure advantages. These included:

  • Higher compute — storage on demand
  • A more efficient use of compute — storage from tailored infrastructure
  • Infinitely better security
  • The ability to abstract away non-core business activities

2010s

Aggregating data sources was a long-held dream in the 2010s, either by centralising (i.e. amalgamating multiple data sources into a single place so they can be more effectively managed and accessed) or virtualising (i.e. providing seamless access to data through a data fabric without data analysts needing to know where exactly it is stored). 

The idea was to drive value by reducing costs, promoting efficiencies, and streamlining operations. The cloud provided all this seamlessly. It gave organisations the flexibility to ideate, try, and (if necessary) fail fast, thanks to complex analysis and AI/ML-based analytics enterprises.

2020s

The cloud has now evolved to a level that it can tap the previously unrealised potential of an organisation’s data assets. In particular, the increased availability of AI and GenAI has unlocked a raft of innovation opportunities because the cloud is the optimal setting for enabling AI and GenAI. 

The cloud provides AI and GenAI advantages that include (but are by no means limited to):

  • Access to all data tools in a single place
  • Immediate access to the latest hyperscaler tools and the latest iterations of current tooling
  • The consumption and joining of multiple internal and external data sources in an extremely secure way
  • The ability to access a community in terms of both solutions and expertise

Thanks to GenAI, businesses can now locate, aggregate and leverage previously hidden data from structured relational databases such as Oracle & MySQL, images, videos, or even unstructured file data containing text.

This brings about competitive advantage, cost savings and the generation of new value streams, all thanks to the way GenAI can be used in the cloud. 

Mitigating the challenges of GenAI

For all its glorious advantages, GenAI also brings new challenges, including data governance. But the cloud can alleviate that. 

It enables organisations to bring more data assets than ever into a controlled and structured framework. This addresses the escalating data control risks, as cloud services address observability and control flexibly and quickly by providing access, security, and granular supervision, all in real-time, all while enabling the tools and processes required for effective searching, analysis, and analytics.

The complexity of control

The cloud exponentially increases the complexity of control, thanks to the greater volume of different data that can be viewed by the organisation.

It is true that the larger the data store, the larger the problem—but conversely, the larger the data store, the greater the potential benefit. 

A case study

As a case in point, we recently migrated a large consultancy organisation from an entirely on-premise infrastructure and platform to a public cloud. The existing infrastructure was (for the most part) made up of entirely unstructured file data in excess of a Petabyte in volume. 

However, because AI and GenAI tools can classify the data into varying degrees of value and risk, the organisation reduced their data storage cost by a massive 40% in that single initial migration. 

In that same organisation, we used cloud-native GenAI to enrich and interrogate the company’s own data, which has given rise to over ten areas of increased revenue. Thanks to this same process, the organisation has significantly improved its operational efficiency through self-service and automation in customer services and internal collaboration.

How we did it

The goal for this client was simple: create new value. We achieved this alongside considerable cost efficiencies by introducing a new data platform that included a chatbot. 

By using an accelerated delivery approach and GenAI-enabled cloud tools for this migration, we dramatically minimised the cost of storing unneeded data and, in parallel, drove new revenue through the enrichment of high-value data. 

The advantage of using the public cloud for this was that it provides observability over the data and can classify it into categories based on value, governance, risk and compliance.

Conclusion

In conclusion, the evolution of the cloud and the advancement of AI and GenAI have transformed the landscape of data migration and innovation. The cloud has provided organisations with flexibility, efficiency, and strategic value and opened up new possibilities for leveraging data assets. With the power of AI and GenAI, businesses can now uncover hidden insights, drive competitive advantage, and generate new value streams.

As we move forward, organisations need to focus on migrating their data to the cloud and leveraging AI and GenAI to drive value outcomes. Integrating a cost management framework into the data migration process can help organisations optimise resource allocation, maximise returns on their investment and increase data consumption.

The future of data migration lies in the seamless integration of technology, data strategy, and business objectives. Organisations that embrace this shift and harness the power of the cloud and AI will be well-positioned to innovate, thrive, and stay ahead in this data-driven era.