Mastering Data in Commodities: In Conversation with Umid Akhmedov

As data becomes more democratised, new players are entering the commodities space, ramping up the competition. Today, success hinges less on having access to data and more on how you use it. That’s where a strong technical team makes all the difference. In this series, we’re speaking with tech leaders in the commodities industry to gain their insights on how to thrive in this data-driven landscape.

We recently had the pleasure of speaking with Umid Akhmedov, Chief Data Officer at Danske Commodities. Umid is responsible for all data in the company, whether it’s generated internally or acquired externally. 

What are the biggest challenges you face in your role? 

The biggest challenge I face is addressing the technical debt that accumulates over time. Organisations, whether in commodities or other industries, often overlook that their revenue and operations rely heavily on technology and data, leading to significant technical debt. The key is for these organisations to focus on cleaning up and improving their tech and data structures to stay competitive.

What does it take for organisations to become more data-centric?

People generally understand that data is important, but not everyone treats it as the valuable asset it is. The key for companies is to undergo a paradigm shift, changing their mindset from viewing data investments as costs to recognising them as opportunities for revenue. Once this shift occurs, data will become a powerful tool for driving growth.

With data becoming more democratised within the commodities sector, how do firms get their competitive edge?

Your competitive edge lies in your ability to make decisions quickly. The real competitive advantage goes to companies that can make their decision-making process as quick and flexible as possible. Whether it’s turning a new idea into a product or launching a fresh revenue stream, the key is to adapt and adjust swiftly. Organisations that can react the fastest to market changes will be the ones that come out on top.

How do you go about increasing the efficiency of your teams?

When I began my journey in advanced analytics back in the day, my team in Denmark was among the first to combine data science and data engineering responsibilities. Back then, our work was roughly a 70:30 split between data engineering and data science, with engineers responsible for preparing the data, while data scientists generated insights and value.

Over time, I realised that this division wasn’t quite accurate. Data engineers alone couldn’t create that 70% of value without the support of data managers or master data specialists. These roles, though less technical, are crucial for establishing the structure and organisation needed for data engineers and data scientists to thrive. They ensure that there’s clear governance, data is well-managed and accessible, allowing the entire organisation to benefit from scalable self-service capabilities.

In my current organisation, I’ve organised the team to include a dedicated unit focused on data management, master data management, and data governance – what we now call the Data Office. The main role of the Data Office is to make life easier for the rest of the organisation to get the benefits of using data.

How do you measure the success of your teams?

One of the key ways we measure our success is by looking at “time to data.” This is the time it takes from when data needs to be ingested to when it’s ready for the user to actually use. Our long-term goal is to cut this down to just minutes, instead of days. We track other metrics too, but this one is probably the most relevant to this discussion. 

What are your biggest hiring challenges?

I find that the biggest challenge when finding skilled data scientists or data engineers is identifying individuals with strong soft skills in data management. It’s difficult to find people who have hands-on experience with implementation and who can effectively communicate the importance of data governance across the organisation. 

Ten years ago, roles like Master Data Manager and Data Manager were common. But suddenly, the “sexier” roles emerged, and everyone believed that hiring a few data scientists would solve all their problems. Why would they need a data manager when they could just hire someone with a degree in mathematics, who could write code but had no understanding of their business? As a result, many data managers were let go, and, realising there wasn’t much demand in their area, they shifted to other fields. So today, this breed of professional has almost died out. There isn’t a large talent pool of candidates with competencies in master data management, data governance, and similar areas.

What skills or qualities do you look for in data professionals?

The ability to be curious – that’s what I’m looking for when I’m hiring. Talented individuals who are open-minded enough to look at their own work and say, “actually, this might be done better or differently.” They are not afraid to experiment with new technologies and constantly widen their knowledge horizon. It’s this kind of curiosity that drives them to constantly improve, and so that’s the quality I value the most.

How do you see the role of data evolving in commodities? 

Data will remain incredibly important. However, it will evolve into something more commercial where CDOs will have to face more questions like: how can we generate revenue from the data we produce? 

Traditionally, data organisations have been seen as cost centres, but now the market conditions are pushing the conversation towards the data economy within the organisation. The aim is to get people to think beyond the costs and to recognise the revenue potential that data can bring.

Looking for more insights?

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