Published by Jessica Weisman-Pitts
Posted on April 7, 2022
4 min readLast updated: February 8, 2026
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By Matthew Hodgson, CEO of Mosaic Smart Data
Harnessing the data explosion has become one of the biggest challenges facing banks today. With such massive amounts of data – all sourced by different methods and stored in different locations across the bank – the challenge can sometimes feel insurmountable.
The benefits that come from getting a grip on your transaction and market data are exponential, driving decision intelligence and improving profitability across the organisation. For many banks, the most meaningful fintech investment they can make is one that provides a solution to this very problem – but even with the best technology, the path by which to tackle it is often unclear.
Many banks start data analytics projects but end up abandoning them when the expected results don’t arrive – despite having invested significantly in data infrastructure or beginning to experiment with advanced analytics techniques and technology. Efforts are often disparate across different departments and locations in the bank, with little hope of being unified into a comprehensive global analytics programme.
Ultimately, avoiding these pitfalls rests on two variables: a robust, global strategy and a best-in-class data analytics specialist to assist and empower the bank on its journey.
Strategy begins with IA
All successful analytics strategies must first focus on putting fundamental data foundations in place. Without the right preparatory work, valuable insights may remain inaccessible. This is where implementation analysis (IA) comes in.
IA can be defined as a specification of all work that must be done to successfully implement a new technology service. In the world of data analytics, IA enables banks to quickly identify gaps in their data and deploy effective – and rapid – remediation strategies. The ROI of the service is huge, providing valuable learnings that massively outweigh the small IA fee.
When delivered by an external agency with an objective point of view, IA can massively accelerate the process of turning a bank’s data into actionable intelligence, as it is able to cut across internal politics and provide a realistic assessment of what the institution needs to do to begin its data journey.
Once the necessary data health challenges have been fixed, the bank can then begin on its analytics journey. This begins with normalisation. Data sets must be harmonised and standardised into one consistent format and cover as wide a set of relevant transaction and market data as possible. Furthermore, each data entry should be as comprehensive as possible, with all relevant fields captured for every entry.
Again, the initial IA can provide a valuable roadmap for this stage of the journey – after all, achieving such a unified, cleansed and enriched data set is often far from straightforward. Within market-facing firms, trades are being executed across myriad electronic trading venues including bilateral liquidity streams and by traditional over-the-counter protocols (i.e. voice).
Within the FICC markets, each trading network adheres to its own messaging language for passing and recording trades and there will often be wide variation in the fields captured for a given trade. To add to the complexity, data which firms bring in from external sources will have been processed in a way which is unique to that data provider and cannot simply be added to this new unified data set.
From IA to AI
Only once a bank’s data is normalised can it begin to analyse it by turning it into smart data, then optimise the data by harnessing real-time and personalised insights from it.
One cutting edge technology that can facilitate this is artificial intelligence (AI). Institutions utilising AI have been reported to have a 58% chance of improving profitability.[1] However, the ability to use AI techniques effectively depends on having access to complete and high-quality data – a challenge that financial institutions rank as in the top three key hurdles to AI implementation,[2] but one that can be remedied with an accurate IA at the outset.
In order to act efficiently banks have to be able, at the drop of a hat, to answer questions like, who are their best clients? Which asset class is seeing the most business? and many more. The answers to these questions lie in the decision intelligence that can be unlocked by technologies such as AI, however the vast majority of banks do not have their data in a place where this is even possible.
Critically, what this tells us is: banks must address the fundamentals of their data business before expanding onwards into further technological development – and it begins with IA.
Once these steps are complete, banks will be able to capitalise on their data by having real-time and actionable intelligence at their fingertips. Throughout each step of the journey, the IA learnings provide an invaluable roadmap for success.
Implementation analysis (IA) is a process that specifies the work needed to successfully implement a new technology service, particularly in data analytics, helping organizations identify data gaps and remediation strategies.
Artificial intelligence (AI) in banking refers to the use of advanced algorithms and machine learning techniques to analyze data, automate processes, and enhance decision-making, ultimately improving profitability and customer service.
Decision intelligence is a framework that uses data analytics and AI to improve decision-making processes within organizations, enabling banks to make informed choices based on comprehensive data insights.
Return on investment (ROI) is a financial metric used to evaluate the profitability of an investment, calculated by dividing the net profit from the investment by its initial cost.
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