the catalyst for rapid success in a bank’s analytics journey – Natural Self Esteem

Through Matthew Hodgson, CEO of Mosaic Smart Data

Harnessing the data explosion has become one of the biggest challenges for banks today. With such massive amounts of data – all sourced using different methods and stored in different locations within the bank – the challenge can sometimes seem insurmountable.

The benefits of getting a handle on your transactional and market data are exponential, driving decision intelligence and improving profitability across the enterprise. For many banks, the most sensible fintech investment they can make is one that solves this exact problem – but even with the best technology, the path to the solution is often unclear.

Many banks start data analytics projects, but abandon them when expected results don’t arrive — despite having invested significantly in data infrastructure or started experimenting with advanced analytics techniques and technologies. Efforts are often spread across different departments and locations within the bank, with little hope of being unified into a comprehensive global analysis program.

Ultimately, avoiding these pitfalls depends on two variables: a robust, global strategy and a world-class data analytics specialist to support and empower the bank along the way.

Strategy starts with IA

All successful analysis strategies must first focus on creating fundamental data foundations. Without the right groundwork, valuable insights may remain inaccessible. This is where implementation analysis (IA) comes in.

IA can be defined as a specification of all the work that needs to be done to successfully implement a new technology service. In the world of data analysis, IA enables banks to quickly identify gaps in their data and implement effective – and fast – remediation strategies. The ROI of the service is tremendous and offers valuable insights that far outweigh the low IA fee.

When provided by an outside agency with an objective point of view, IA can massively speed up the process of turning a bank’s data into actionable information because of its ability to break through internal guidelines and provide a realistic assessment of what the institution must start its data journey.

Once the necessary data health challenges are resolved, the bank can begin its analytics journey. This starts with normalization. Data sets must be harmonized and standardized in a uniform format and cover as much relevant transaction and market data as possible. Additionally, each data entry should be as comprehensive as possible, capturing all relevant fields for each entry.

Again, initial IA can provide a valuable roadmap for this phase of the journey—after all, achieving such a unified, cleaned, and enriched data set is often far from easy. Within market-oriented firms, trades are executed through a myriad of electronic trading venues, including bilateral liquidity flows and through traditional over-the-counter protocols (e.g., voice).

Within the FICC markets, each trading network adheres to its own message language for relaying and recording trades, and there are often major differences in the fields recorded for a particular trade. To add complexity, data companies bring in from external sources has been processed in a way unique to that data provider and cannot easily be added to this new unified data set.

From IA to AI

Only when a bank’s data is normalized can it begin to analyze it, turning it into smart data, and then optimize the data by leveraging personalized and personalized insights from it in real-time.

One cutting-edge technology that can facilitate this is artificial intelligence (AI). It has been reported that institutions using AI have a 58% chance of improving their profitability.[1] However, the ability to use AI techniques effectively depends on access to complete and high-quality data – a challenge that financial institutions rank as one of the top three hurdles to AI implementation.[2] but one that can be fixed with an accurate IA from the start.

In order to be able to act efficiently, banks must be able to answer questions straight away, such as: Who are their best customers? Which asset class is seeing the most business? and many more. The answers to these questions lie in the decision intelligence that technologies like AI can unleash, but the vast majority of banks don’t have their data in a place where this is even possible.

Crucially, what this tells us is that banks need to address the fundamentals of their data business before expanding into further technological advances – and that starts with IA.

Once these steps are complete, banks will be able to capitalize on their data by having real-time and actionable information at their fingertips. At every step of the journey, IA insights provide an invaluable roadmap to success.

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