MasteringSAP in Sydney was an exciting community space where participants from across Australia and New Zealand exchanged stories about their digital transformation journeys, touching on hot topics such as leveraging AI and Machine Learning. It was especially inspiring to hear how firms and innovators are using AI in fascinating ways whether finding sustainable advantages in the hyper-competitive e-gaming space or enabling people with physical disabilities to achieve greater agency, equity, and dignity. For the PiLog team, it was equally energizing to meet representatives from a wide range of industries, including Mining & Resources, Energy & Utilities, Manufacturing, Construction Materials, Logistics & Transport, and Government & Public Sector.
Of course, our interactions weren’t limited to serious business alone we had some fun too! We invited attendees to enter a draw to win Ray-Ban Meta glasses and snuck in a very short survey, asking participants to share their biggest “current or anticipated data challenges.” And what a response we got! We sincerely thank everyone who contributed. Your candid inputs highlighting everything from low-quality data and duplicates to governance gaps and S/4HANA migration pains have shaped the insights below. We’re turning your voice into actionable ideas to help you navigate these hurdles and unlock the full potential of AI for your organization. Let’s dive in.
We heard from professionals across asset-intensive industries. Your top concern? Data quality issues – accounting for 34% of all responses clearly dominate the pain points. Customers are feeling the effects of bad data every day.
Close behind was data governance (27%) , highlighting the growing realization that even after cleansing data, organizations still struggle with ownership, stewardship, and ongoing policy enforcement. In short: building a trusted golden record + protecting it = strong master data management.
But achieving clean, well-governed data is only part of the journey. In asset-intensive industries, unstandardized data and migration issues can derail AI initiatives even when the data is cleaned and governed after the fact. Respondents cited data migration (18%) and data standardization (9%) as the next biggest challenges.
Beyond these four pillars of master data management , respondents also mentioned challenges including infrastructure limitations, security concerns, and BI/Analytics issues all of which impact day-to-day operations across the enterprise.
As more enterprises rely on AI for asset management, predictive maintenance, asset optimization, and more, one truth stands out: AI trained on low-quality data doesn’t just underperform it amplifies risk. Bad data can trigger inflated forecasts, incorrect asset flags, or faulty reliability predictions. A large portion of enterprises report underperforming AI initiatives due to poor data quality, often turning promising projects into expensive paperweights. In the mining industry alone, over 60% of AI projects have failed because of bad data, contributing to issues like commodity price exposure and margin erosion of 20–30%.
It’s not all gloom and doom, though. PiLog’s Data Quality and Governance (DQG) Suite offers an end-to-end solution for data migration, data standardization, data cleansing and enrichment, and data governance powered by an AI co-pilot trained on industry-relevant content and context to support master data management.
PiLog’s DQG Suite ensures clean, structured, and harmonized master data for SAP EAM, APM, and S/4HANA. You gain access to a comprehensive library of ISO 81346 and ISO 14224-compliant asset hierarchies, supporting reliability data standards across asset-intensive industries. You also benefit from seamless integration via SAP API Hub, connecting PiLog to SAP APM, FSM, IBP (MRO), BNAC, BN4P, and IPD. This accelerates deployment, strengthens reliability, and enables digital twins, predictive maintenance, and data-driven insights to reduce downtime and optimize asset performance.
PiLog’s DQG Suite ensures clean, structured, and harmonized master data for SAP EAM, APM, and S/4HANA. You gain access to a comprehensive library of ISO 81346 and ISO 14224-compliant asset hierarchies, supporting reliability data standards across asset-intensive industries. You also benefit from seamless integration via SAP API Hub, connecting PiLog to SAP APM, FSM, IBP (MRO), BNAC, BN4P, and IPD. This accelerates deployment, strengthens reliability, and enables digital twins, predictive maintenance, and data-driven insights to reduce downtime and optimize asset performance.
Our clients in asset-heavy sectors have reduced their inventory cost by 28-30%, maintenance costs by 20-25%, and procurement cost by 20-25% by implementing DQG Suite. They also improved equipment availability by 34% and equipment effectiveness by 24-28% among several other tangible benefits. Your survey echoes the same needs let’s make those outcomes possible for you too.
Scan to request a free demo of PiLog’s DQG Suite tailored to your data challenges.
Together, we’ll build AI-ready data foundations.
