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Smart data powers business sustainability

Smart data powers business sustainability

By Agnes Heftberger

Credit: Getty Images

In recent years, data has been at the front and centre for enterprises seeking to stay relevant, sustainable and emerge as industry leaders. The democratisation of data has further led to the availability, demand and use of data at an unprecedented pace. This phenomenon, however, has surfaced challenges and opportunities for enterprises. The rise of generative AI such as ChatGPT has accelerated this further. Forward thinking enterprises recognise the opportunities and are looking at ways to turn generative AI into productive data for their industries.

Australia’s Productivity Commission had warned that living standards could slip if enterprises fail to use data and AI to revolutionise growth. The current landscape underscores the belief that data is the lifeblood for enterprises to grow their business in a sustainable way. Becoming more sustainable is an opportunity to innovate, make a difference and scale.  

While top performing enterprises are data driven, research found that data veracity is still a challenge with up to 68% of data not getting analysed and data silos persists at 82% of these organisations. A joint study by IBM and Morning Consult found that businesses are drawing from more than 20 different data sources - such as databases, data warehouses and data lakes – with some up to a whopping 500 data sources.

These issues are only intensified by the complexities of the platforms that enterprises have parked their data on. Enterprises aren’t just dealing with data spread all over their company, it’s also being stored and managed across a variety of places - public clouds, private clouds, on-premises and data centres. The average enterprise has five different environments, cloud-wise, turning the data challenge into a hybrid cloud problem as cloud spend continues to rise in Australia and expected to reach US$14.1 billion by 2025.

Why data fabric

Clearly a robust strategy is needed to manage the complexity of data to harvest useful insights within heterogeneous environments. How can enterprises simplify access and governance of data quality, regardless of where it resides? There are industry leaders who have adopted a data fabric architecture to improve “findability” of data.

ING and Citigroup are great examples enterprises that have maximised the data fabric architecture for business sustainability. ING put in place a set governing and data quality rules, defined business taxonomy, access rights, privacy, and protection across its data stores regardless of the platform where it resides help its team make informed decisions. Citigroup introduced machine learning and AI into the full audit lifecycle to find anomalies within business monitoring, then planned and scoped audits more effectively using all the fresh findings. IBM collaborated with Citi to build a new audit platform injected with advanced analytics and AI.

Closer to home the University of Queensland and Dubber are tapping data fabric to speed up collaboration and draw insights for growth. For instance, Dubber is using Watson AI to merge call recordings with unified communications solution at the price point of a utility while the University of Queensland sought to simplify data capture, storage, analysis and management for its high-performance computing environment with a unified data fabric.

Their foray – and optimisation gained – present opportunities for smaller enterprises to explore data for trends and other practical purposes.  It also makes the case for a data fabric architecture stronger especially with the demand by regulators to ensure data quality, fairness, governance and equitable access to boost secure sharing without compromising personally identifiable information.

Productive data with AI

The first step is usually the hardest as enterprises struggle to make sense of vast data volume and tapping into AI tools had helped reduce manual labour needed to sift and analyse data, remove data duplication and develop a recommendation engine while meeting regulatory compliance.

Removing bottlenecks to data with a data fabric architecture also allowed enterprises to foster more productivity, enable users to make informed decisions and free up valuable time for teams to focus on higher value work. Done right, a data fabric will connect the right people with the right data at the right time. It will eliminate the complexities involved in data movement, data transformation and data integration. And a well-designed data fabric architecture to manage the influx of data without compromising the integrity is surely the way forward to a resilient and sustainable business enterprise.

Credit: Agnes Heftberger

This complements Australia data strategy that is focused on maximising the value of data, trust and protection to deliver better services to citizens and be well on the path of a modern, data-driven society by 2030.

(Agnes Heftberger is the General Manager & Technology Leader for IBM Australia, Southeast Asia, New Zealand & Korea (ASEANZK). A practitioner of sustainable growth at work and home, she is more likely to flip back questions to uncover insights instead of trying to explain what the acronym ASEANZK stands for).

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Tags artificial intelligence (AI). ChatGPT

More about AustraliaCitigroupDubberIBMIBM AustraliaLeaderProductiveProductivity CommissionTechnologyUniversity of Queensland

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