Transform data into your super power

1. Data quality, standardization and interoperability

The idea of gaining control over data might be seen as misleading – or even impossible. The reality is that commodity data is volatile, idiosyncratic and inherently difficult to manage. Unlike traditional financial market data, which operates within a relatively standardized structure, commodity data is impacted by geopolitical events, weather disruptions and regulatory shifts. In addition, there is an ever-growing range of sources to get to grips with – from satellite imagery to sensor data.

Along with the sheer volume of data, organizations are struggling with data quality, standardization, and interoperability. Some of the most sophisticated trading firms still face gaps in data lineage, reconciliation issues and integration challenges across systems. The question is no longer about who owns the data, but how to ensure it flows efficiently across the organization to enhance decision-making rather than creating bottlenecks.

2. Artificial Intelligence (AI) and the evolution of data analytics

It’s impossible to discuss data management today without addressing the role of AI. Many of us are still trying to understand the potential of AI, and how, where and when it should be applied.

While there are companies that are applying AI and machine learning to improve data quality, structure data, and automate data ingestion, many haven’t yet started down the AI path or even have a plan for doing so.

At Zema Global, we see first-hand how AI is transforming data workflows – but AI is only as good as the data it learns from. Without clean, well-structured data, AI initiatives can end up amplifying biases and errors rather than solving them.

3. Breaking down data silos

One of the most common pain points raised as we speak to our customers is interoperability – or rather, the lack of it. Many firms have invested heavily in data lakes, cloud-based architectures and APIs, yet data still moves too slowly across the organization.

The reasons are often structural, such as a lack of standardization between different business units or a hesitancy to trust external data sources leading to inefficiencies and duplication.

For firms that rely on real-time decision-making, these silos create a significant competitive disadvantage. But the industry is stepping up to address these issues, and data architectures are evolving to tackle this problem – particularly through data meshes and hybrid cloud models.

4. Regulation, data governance and trust

A theme that comes up repeatedly is the growing scrutiny around data governance and regulatory compliance. With regulators increasingly asking firms to prove the lineage of their data and demonstrate auditability, organizations are investing more heavily in stronger governance frameworks to track flows and automated reporting tools to reduce risk.

Firms need to embed compliance into their data strategies, rather than treating it as a separate function. When done right, regulatory rigor can enhance trust in data rather than acting as a constraint.

5. Delivering true data-driven decision-making

The shift from data hoarding to data intelligence is clear. Many organizations are realizing that simply collecting more data is not enough– it must be operationalized, analyzed and acted upon in real time. Furthermore, it’s clear that the increasing reliance on AI-powered analytics and collaboration with data providers and technology vendors is essential for staying ahead of the competition.

Final Thoughts

Discovering the true value within your data is an ongoing journey, not a fixed destination. The companies that succeed will be those that move beyond simply managing data and start using it strategically to drive decision-making.

We’d love to know which of these issues matters most to you. Have your say here.

Find out how Zema Global can help you transform data into your super power.