Driving data efficiency: three strategies for modern organisations

Data generation is increasing rapidly. According to Statista, the total amount of data created, captured, copied and consumed globally is forecast to rise to 527.5 zettabytes by 2029, tripling what it was in 2025. For IT leaders, the challenge isn’t just keeping up with this growth, but managing it in a way that is efficient, scalable and sustainable.

The amount of data to be managed differs based on industry, but with software and now artificial intelligence (AI), every company is managing an increasing volume of data. And with data redundancy, both intentional and unintentional, the increased storage requirements and operational burden can impact performance and sustainability.

This is nothing new: it held true as the industry transitioned from processing large volumes of data warehouse workloads with ELT (extract, load, transform) and ETL (extract, transform, load) to scale-out self-managed systems like Apache Hadoop and, more recently, cloud-based solutions. Over the last decade, many of the tools have changed and evolved, but the fundamental problem remains. How can enterprises manage their growing volumes of data efficiently?

Schemas are the foundation of reliable data

A good place to start is with the structure of the data itself. Schemas, which define how information is stored, organised and accessed, form the foundation of efficient data management. Schemas improve data quality and efficiency by preventing inconsistencies and streamlining data processing.

For example, using schemas to optimise data pipelines ensures information is processed accurately and efficiently across different systems. By shifting governance processes as close to the data source as possible, enterprises can ensure that the highest-quality data reaches downstream systems.

In short, schemas and their associated registries and catalogues are the basis of a robust data strategy. Improving data quality and categorisation should therefore be a first step for any business looking to improve data efficiency.

Achieving real-time efficiency with stream processing

But data efficiency isn’t only about structure, it’s also about movement. Data that sits idle in rigid, batch-based systems quickly loses value and drives up cost. For true reliability and resource-efficiency, data needs to flow continuously through the organisation.

Stream processing enables this by analysing and processing data in real time from various sources. Unlike batch processing, which handles data in fixed intervals, stream processing involves the ingestion, transformation and analysis of data in motion. This reduces duplication and unnecessary storage, and ensures insights are always based on the current data available.

Open formats like Apache Iceberg and Delta Lake extend this efficiency to how data is shared, keeping it consistent and reusable across different platforms and teams. Businesses can then build on this idea by uniting operational and analytical data in real time, enabling seamless data access and management.

Three strategies to increase data efficiency

Achieving true data efficiency requires more than upgrading tools or scaling infrastructure. It demands a shift in how data is designed, moved and governed. While every organisation’s data journey is different, three approaches can deliver meaningful results.

• First, adopt no-copy and zero ELT solutions to minimise data duplication. By processing data in motion rather than creating multiple intermediate copies, organisations can cut down on storage and compute costs while enabling faster access to real-time insights.
• Second, shift data governance left. Bringing governance and quality controls closer to the source can prevent downstream data pollution and improve overall data integrity. This proactive approach ensures that high-quality data is propagated throughout the data lifecycle.
• Finally, reduce data waste by processing data at the source. Optimising data flow and removing redundant data by processing data as close to the source as possible can lead to significant cost savings and efficiency improvements. Techniques such as data deduplication and compression can help achieve this.
   

Together, these strategies create a more sustainable approach to managing data growth, one where every byte is purposeful, accessible and ready to deliver value in real time.

Rethinking data architecture for sustainable growth

Many organisations learned the hard way during cloud adoption that simply ‘lifting and shifting’ legacy systems doesn’t guarantee better performance or lower costs. The same lesson applies to data.

Achieving data efficiency is critical for sustaining organisational growth in the face of exponential data expansion. But to achieve this, enterprises need to rethink how they approach their data architecture, adopting robust data management practices such as schemas, leveraging open table formats, and implementing no-copy solutions. As data continues to grow, modern data practices are crucial to maintaining performance and efficiency.

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