This article was first published in Traders Magazine.
Reading past the headlines
Innovations in technology, particularly AI, have made the headlines in pretty much every media outlet from the most specialist trade journal to the widest-read tabloid. So it is easy to see why trading firms are hoping these shiny new technologies can offer a global panacea to their woes. According to Gartner, AI will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally in 2021.
Taking up far fewer column inches is the area of data ingestion and transformation – and yet this is the crucial first step towards any transformational IT project. The lack of attention is unsurprising as data has long been a back-office companion, but without due attention, trading firms risk losing a the chance for a competitive edge, and any new AI or ML initiatives will ultimately fail.
Tried and tested ways aren’t always best
Data lineage remains important, as trading firms need a solid grasp of it to not only to ensure efficient and exception-free trading but to also track how an individual trade, a position or a price impacts other parts of the business. But when data is stuck in silos and loitering in hard-to-consume PDFs, emails and spreadsheets, firms are forced to take a partial view of the full data picture.
A recent webinar with Adox Research, showed that 30% of senior technology and data management executives recognised that ‘complex, derived, and textual data sets which need oversight and context’ are increasing in importance – leading to increased business risk unless those functions can be automated. Indeed, the same Adox webinar revealed that most of those decision makers are not pursuing big-vision transformation projects when it comes to data quality. Instead, many are still applying the same data quality metrics of old and are using existing approaches and methods to prepare for change.
So how do we move forward? For all digital transformation projects but especially trading, the answer lies in employing a data-first strategy – putting data at the heart of the transformation, so the right data can be intelligently connected in the right format at the right time. Only being able to digitise simple tasks or parts of processes that avoid this prioritisation of data, is unlikely to generate the transformation or the return being sought. Understanding and embracing the challenge from the outset will enable higher value and more complex processes to be digitised thereby generating real change and real returns.
How the theory translates in practice
Single automation platforms that have both breadth and depth of functionality, including AI, can deliver key transformation and data-centric benefits, working both with what is currently in place, replacing some as well as deploying new capabilities such as machine learning and NLP.
A case in point, a global firm we work with had an issue that many reading this will identify with – its finance operations were being largely conducted through a series of complex and distributed spreadsheets. The business was using over 30 separate systems for over 300 processes, pulling all sorts of data from disparate sources. We replaced all these with a single integrated platform and consolidated the reporting across the organisation. This gives clear oversight, reduces risk and helps build a data-first mind set.
In the first instance we helped recruit and inspire a set of pioneering change leaders across financial operations. Using our platform, the change leaders built transformational blueprints mapping out how business processes and functions would evolve. Now, over 350 employees globally are driving the project, creating a consistent approach for the management of financial operations.
We’ve enabled the firm to boost the consistency, accuracy, and transparency of vital data-intensive processes and in so doing it gained more flexibility and consistency than previous spreadsheet-based practices. The transformation blueprint method used by this firm also means the project extended beyond the simple remit to eradicate the reliance on spreadsheets.
Today’s data is different, as are the players. As data underpins customer UX and better automation, the importance of data and data strategy no longer sits just with operations and IT, the front office has also become a key stakeholder. So rather than measuring data projects on cost-efficiency outcomes, reducing the time and effort needed, etc. firms need to think about how they can support better new investment and trading strategies, in turn delivering better customer outcomes.
Trusted data is essential to making better decisions – and those decisions are about the true core competencies of today’s front office: how can customers get the right products and the right price/value point. Data-driven and customer-centric decision support should be the ultimate goal for any transformation projects.