Without an effective cloud
data platform analytics
are doomed to under-perform

As good as organisations have become at capturing and keeping data, our ability to find the right information and get it to the people who need it – when they need it – is stalling.

A study by IDC[1] says the world’s store of digital info will grow by more than 60 per cent over the next four years — reaching 175 zettabytes. That’s an unfathomable amount of data, at least half of which will live in the cloud.

And the other half? it’s too soon to say. Enterprises simply have too much data in too many places in too many formats to know for sure where it all will be stored or how effectively they’ll be able to leverage it.

Hybrid cloud environments complicate the picture, making it even harder to access info when its needed or apply the controls and policies that regulators and customers demand.

Organisations need to be able to pull value from the exploding array of information held across their systems. For many, that will mean changing how they manage data in the cloud.

It needs an approach that's nimble enough to handle the workloads created by expanding data volume and complexity, reflect the requirements of analytics, and ensure everything captured is delivering value back to the business.

Integrating data, knowing where it sits, and understanding its provenance requires numerous checks and data quality rules. Using the capabilities of world-leading cloud data platforms to automate and execute those requirements at scale is more critical than ever.

Enter the cloud data lakehouse


Legacy IT infrastructure is no longer powerful or nimble enough to sort through data and turn around insights quickly. To overcome that, many organisations have invested in cloud data warehouses or created data lakes.

But those models have also started to show signs of strain. Cloud data warehouses are optimised for structured data but struggle to handle unstructured and semi-structured data.

Data lakes were designed to overcome those limitations and accommodate data in various formats. Still, lack of support for transactions and an inability to enforce data quality means data lakes can't keep up with modern analytics requirements.

So a new model has emerged that combines the best aspects of both: the cloud data lakehouse.

The lakehouse combines the best elements of data lakes and data warehouses, blending data structures and data management features similar to what you’d get in a data warehouse, and applies them in the same low-cost storage used for data lakes.

The result is enterprise-scale data integration, data quality, and metadata management suited to today’s analytics.  It also enables today’s governance requirements by managing discovery, cleansing, integration, protection, and reporting across cloud environments.

Successful implementation of a cloud data lakehouse can deliver the kind of insight-led, data-driven decision making modern digital businesses need to compete and survive — all the more reason to trust data migration and lakehouse cloud infrastructure to the right vendor.

Key considerations for a lakehouse implementation


What is the current architecture? First, you'll need to look at your existing architecture and understand its current pain points. If your analytics team is spending too much time processing and prepping data and not enough conducting value-add analytics, that’s one sure sign that you need a new approach to data management.

What are the limitations of the current system? Some legacy environments make it difficult to extract data and understand it, which complicates the creation of effective data models for analytics.

What are the use cases you need to enable? It's essential to define problem statements and be clear on the tangible business benefits you want to achieve. It’s important to know which of the use cases are worth investing in and prioritise them according to expected short- medium- and long-term ROI.

A phased approach to cloud data migration


Agile’s cloud data migration methodology enables customers to break free from the inertia that technical complexity can bring and accelerate the move to a data lakehouse. It's based on learnings obtained from hundreds of implementations but flexible enough to be tailored to each organisation's individual needs and objectives.

There are four key phases:

01 Assess

This is the foundational phase where we consider current readiness for operating in the cloud, looking at existing applications, data, infrastructure, and licensing.

02 Mobilise

Once we've clarified where an organisation is starting from and where it wants to get to, we move to the second phase and decide how we're going to achieve our goals.

03 Migrate and modernise

We work with our customers in the migration phase to execute the migration and implementation plan developed during the earlier phases.

04 Manage and monetise

Once the lakehouse has been established, and data quality is where it should be, there may be an opportunity to turn your business information into revenue.

One data platform to rule them all


To take advantage of data and turn it into actionable insights, today’s organisations need a unified repository that can manage data across environments and then act as a mechanism for delivering insights and information to users and applications.

A cloud data lakehouse delivers fast and contextualised access to the data businesses need. That way, they can generate analytics that point to changes in customer behaviour,  investment opportunities, activities with a higher propensity for risk, and emerging market trends.

Want to learn more? Download our latest white paper: Why the time is right to implement a cloud data lakehouse.

[1] IDC: Data Age 2025