UTS picks Atturra to lead data strategy

The university is seeking to modernise and accelerate its digital agenda and is leaning into data transformation.

The University of Technology Sydney has chosen Atturra to lead its data strategy, harnessing the Denodo Platform to connect data sources from across the university.

UTS plans to consolidate capabilities within a single, unified platform aiming to improve efficiency, governance and security across its data operations. It will also eliminate the need for the university to procure and maintain multiple platforms.

For UTS, the aim is to create an integrated ecosystem that provides easier, faster access to its data that can be stored in numerous sites across the university’s footprint.

“Our goal is to make data more accessible and useful not just for IT, but for our educators, researchers, and professional staff,” said Jennifer Leech, senior platform manager, integration, UTS.

“Denodo gives us a more flexible and sustainable way to connect our data without unnecessary complexity.”

Petar Bielovich, director, data and analytics, Atturra said, “The university’s decision to adopt a logical data management approach reflects a growing trend among institutions looking to increase agility, reduce complexity, and maximise the value of their data.”

Data transformation is fundamental to digital transformation. Yet, many organisations focus on upgrading technology but may overlook the value in enabling new business models — not just refreshing infrastructure, according to Bielovich.

AI adoption is integral to enabling new business models, but data access and data quality are often limiting factors in these initiatives.

“How we frame the data challenge determines how we solve it,” Bielovich said.

Without addressing poor data integration and data quality during digital transformation efforts, the importance of data transformation can be lost, leading to failures.

“These typically are caused through poor data integration and data quality,” said Bielovich.

A rethink is needed to avoid the garbage-in, garbage-out problem. To do so, organisations must take deliberate steps to prepare data to suit AI and enterprise-wide decision-making, according to Bielovich.

“We’ve typically relied on data consolidation approaches, such as data warehouse, data lake, lake house, to bring data together to analyse it. Data virtualisation provides a complementary capability to data consolidation,” he said.

Data virtualisation enables organisations to have faster access to data, better cost control and improves user access.

“Data virtualisation is a a critical component to any unified data platform that underpins a digital transformation,” he ended.