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Why Data Processing Costs Are Spiralling – and What Australian Businesses
Can Do About It

 

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Adopt cloud-based data management and cloud-based data storage.

Add a data fabric architecture for seamless integration. Organisations are looking for flexible data management solutions as data continues to sprawl across disparate destinations—on-premises data centres, multiple clouds, and edge devices.

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Data has become the lifeblood of modern businesses, but for many Australian organisations, the cost of processing data is rising at an alarming rate. Storage expenses, compute costs, and inefficiencies within data pipelines are all contributing to an increasingly expensive problem.

Axle, a data specialist at Novon, explains, “The sheer volume of data being collected continues to grow exponentially. Organisations are generating vast amounts of transactional data, customer insights, and IoT streams. As the data increases, so do the costs associated with storing and processing it.” 

Beyond volume, inefficient data architectures are exacerbating the problem. “Many companies are running redundant or overly complex data pipelines, unnecessarily moving the same datasets across multiple cloud environments,” Axle says. “This racks up costs, especially on platforms like AWS, Azure, and Google Cloud, where every query, transformation, and data movement comes with a price tag.”

Another significant issue is data sprawl. “A lot of organisations don’t actually know what data they have or where it’s stored. This results in duplication and unnecessary expenses,” Axle notes. “On top of that, businesses often design data solutions without considering their actual use cases, leading to inflated and inefficient systems.”

What’s driving a modern Data Architecture

 

Strategies for Cost Control

To combat rising data processing costs, businesses must first gain visibility into their data expenses. “Many organisations don’t track their data-related spending effectively because the costs are hidden in cloud bills or dispersed across departments,” Axle explains. “A thorough data cost audit is a crucial first step.”

Optimising data pipelines is another key measure. “Businesses should eliminate redundant processes, reduce unnecessary data movement, and refine their data queries,” Axle advises. “Eliminating duplication of frequently run or inflated-cost queries can significantly reduce expenses with minimal impact on performance.”

Lean data management is also essential. “Many organisations are storing and managing enormous data warehouses when only a fraction of the data is actually needed for analysis or operations,” Axle says. “Assessing and reducing these complex datasets into smaller, more focused sets can greatly reduce waste and cost.”

Another overlooked factor is software and licensing. “Licensing reviews are crucial, particularly for tools that charge per seat,” Axle explains. “Many businesses overpay for software that isn’t being fully utilised. Regularly reviewing and optimising software licences is a straightforward way to cut costs.”

Furthermore, companies should reconsider their approach to real-time analytics. “Many businesses process data in real-time when batch processing would be more cost-effective. If immediate insights aren’t essential, transitioning to batch processing can significantly reduce costs without compromising analytical value,” Axle suggests

What’s driving a modern Data Architecture?

In today's digital economy, data is a critical asset that drives business innovation, efficiency, and a competitive advantage. A modern data architecture is at the heart of leveraging this business asset. It is designed to address the complexities and scale of managing data in a rapidly evolving technological landscape. So, what are the key tenants of a modern data architecture that allow for this agility, innovation, and security of your organisations data.

  1. Adopt cloud-based data management and cloud-based data storage.

  2. Add a data fabric architecture for seamless integration. Organisations are looking for flexible data management solutions as data continues to sprawl across disparate destinations—on-premises data centres, multiple clouds, and edge devices.

  3. Add a data mesh architecture to simplify and focus your response to the changing data landscape. It will enable your organisation to respond quickly and cost effectively to the data changes that abound in 2024

  4. Adopt automation using generative AI and ML in data management.

  5. Adopt low-code / no-code for data integration. Find the vendors or data specialists that have completed work similar to your challenge. Successful experienced operators are key.

  6. Provide data governance, security, and privacy in an automated fashion. There is no time in 2024 to manually rediscover and develop data changes that other organisations have already found and adopted. These changes must be automated through generative AI and ML capabilities.

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Anxious Australian Man Novon Branding BW

Data management guiding principles still apply in today’s data landscape, however, today change is very rapid and there is no room for a bottom-up approach to managing your data, rather you should build pervasive AI / generative AI / ML models that do the heavy lifting for you. 

In 2023 and still today we talk about data gravity as a key challenging principle of today’s modern enterprise data platform, but increasingly anti-data gravity is seen as the key issue in 2024 and beyond. 

Data gravity is well documented, essentially this is where the accumulation of data (operations and analytical) attracts more data and services into its business data mix, thereby increasing data complexity and potentially causing serious challenges for an organisation to maximise the value of their data, especially if you have adopted a cloud first approach for your infrastructure. To further complicate this scenario, consider the challenges if you are a global organisation as you manage across different time zones, regulatory and business structures. 

Anti-gravity advocates that the data and expertise should stay local and the two modern data architectures when used correctly can help moderate these challenges are data fabric and data mesh architectures. 

How Novon Can Help

Novon is helping Australian companies take control of their data, implementing smarter architectures, lean data management strategies, and optimised processing to reduce costs while maximising the value of their data. “In an era where data is both a vital resource and a significant expense, mastering data management is key to business success,” Axle concludes.

 

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Axle Pellegrino is a Senior Business Intelligence Consultant at Novon, specializing in data strategy, analytics, and BI solutions. With 13 years of experience across industries like banking, energy, and logistics, she has helped businesses optimize their data ecosystems, improve reporting efficiency, and reduce unnecessary processing costs.

Axle has a track record of rescuing critical projects by quickly assessing data challenges and delivering scalable, cost-effective solutions. Currently, her primary focus is on enhancing our customers BI ecosystem, developing advanced reporting solutions for both the Sales and Finance teams to drive strategic decision-making and operational efficiency.

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