Data flows through every trading business. Forecasts. Prices. Trades. P&L. And myriads more. The breadth of sources that data is collected from, and ways it is stored and analysed, has grown exponentially in the past decade.
Data in the trading environment presents a great opportunity, but also a major challenge. Across our clients we commonly see the following critical capabilities gaps:
- lack of understanding how to access data
- lack of trust in data that is available
- insufficient skills and tooling to access and use data
As set out in our last blog, How Trading Organisations Can Get Their Digital Strategies Right, the value generated from this data can offer differentiated insight, giving trading professionals an edge.
Liberating trusted data is key to provide such insight, unlocking additional revenue streams, making better, more profitable trading decisions and lowering running costs of your trading organisation.
This blog sets out how successful trading organisations deliver value from their data assets.
Know where to go. Liberate.
Historically, the majority of data was generated post-trade execution and often during the End of Day [EOD] process. A reactive approach was the dominant theme:
- Post EOD profit and loss / market risk evaluations and reconciliations
- Limit breaches only evaluated as part of EOD process
- Retrospective market abuse monitoring
- Credit limits monitored on a daily rather than hourly or real-time basis
Times have definitively changed. Trading companies have been forced to become proactive, and successful organisations are now striving for a faster and a more targeted approach, shifting to pre-trade analytics.
The goal: Real-time monitoring with the ability to proactively respond, to realise value or to reduce risk. Wherever possible the control, detection and action, coming into effect prior to order causing limit breach being placed on the market.
But why stop here? Predicting the future has always been at the heart of profitable trading activity, commonly through forecasting supply/demand imbalance and out-turn prices. Today’s proprietary models that employ machine-learning algorithms are more sophisticated and achieve higher accuracy than before. They respond to changing conditions more rapidly.
This is all reliant on making data available at the right time with the right quality.
As set out in our Twelve Shifts of Digital series, data-driven businesses start their transformation journey with a clear vision of how data can be managed and nurtured so it delivers maximum value.
We see organisations investing in the following areas:
- Data being searchable and accessible to the right people when they need it
- Data that is owned, defined and well-understood
- Data quality that is measured and maintained
- A data culture, empowering and upskilling their users
- Skilled analysts and data scientists within the business, not only quants in trading
- Industry standard tooling to analyse and visualise their work
- Underpinning data architecture and governance
Companies that want to succeed in digital transformation make their valuable data highly accessible. They don’t prescribe what data is good for, they empower their users.
Successful trading businesses get data to where it is needed: in the hands of analysts assessing fundamentals; in front of their traders in the form of insight; to highly skilled risk teams as intelligence on the company’s trading profile; and in the hands of managers, given a view on their market position.
Self-knowledge & incremental growth
It’s important to know where you’re going, but you don’t need to get there in one leap. The key to successful transformation is through continual, incremental benefit release.
It’s about earning the right to continually invest through a journey of small changes aligned to a common data strategy and roadmap. Their combined result reflected in people, process and technology changes increasing the value extracted from your data.
Take the journey to improve imbalance price forecasting:
- Start small – treat the data associated with your renewable production as the first asset.
- Build capability to better forecast your own generation – make sure the data is clean and trusted.
- Build access layers and basic governance
Result: you will release value by understanding the asset outturn better. The foundations of trusted data and associated tooling and governance can then be extended to wider market data and the skills learnt can be turned to market imbalance price forecasting.
Each business will have their own confidential business strategy, objectives and applications.
Business strategy informs both the data strategy and also the focus points of the data transformation journey. It helps to identify the foundational elements that must be prioritised. Each incremental improvement in data optimisation delivered throughout the journey needs to unlock further value for business. Successful trading organisations periodically measure value and costs of their data investments. They recognize it is critically important to link digital strategy to commercial goals. They integrate use of insight into their everyday processes, culture and ways of working to unlock value and turn it into a business capability they continually strive to grow and improve.
How is your trading business liberating data to deliver value? Does your business link its digital strategy to commercial goals? We would welcome your comments. If you would like to discuss any of the topics raised – or have different perspectives to share – please reach out to a member of our team.
In the next part of our blog series on Digital Trading we will explore how trading businesses are leveraging digital first propositions to generate a competitive advantage.
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