Simon Axon, International Financial Services Industry Director at Teradata
Data is the brand new battleground for banks. Many financial services institutions still do not have a strong business-wide strategy for data. A study found that 83 percent of leading financial services firms see data as their most valuable asset, however only 16 percent consider themselves “excellent” in extracting value from their data. Trying to compete without a robust data strategy is like going into battle with both eyes closed.
For all the talk about the importance of digitalisation leveraging AI and machine learning, the partnerships with digital innovators and the moves into ‘digital first’ models, banks need a coherent data strategy.
Creating an effective blueprint
For banks to succeed in this new data battlefield, they must rapidly develop the same broad but granular understanding of it and how to maximise its value across the financial services institution. It is true that banking is a data-driven business, but the problem is that the data is siloed across different systems, owned by different functions and formats. To overcome this, financial service institutions must rapidly orchestrate all types of data across their entire business and make it all available for enterprise-level analysis and decision-making.
However, before banks even think about changing processes, they must have the buy-in from the organisation, which may mean changing attitudes and culture within the business. In addition, it could also mean that the bank needs to hire a chief data officer or data scientists to derive the most value.
To have a complete data strategy, banks need to start out by defining their vision – looking at what they need, want and the roadmap to achieve their goals. This needs to be strengthened with a strong governance model that transforms data from ‘stuff’ that exists all over the business into a trusted, secure, actionable and reusable asset.
It is important for everyone within the bank to understand, use and trust the data, which means instilling a culture of data and analytics-driven innovation across the organisation. Through this process, the insights which stem from the data can be transformed into beneficial outcomes and strategies, which financial service institutions can operationalise at scale.
Turning a plan into a strong data strategy
The main goal of becoming a data-centric bank is to turn insights into business outcomes. To do this, they must leverage the right technology to capture, analyse and exploit data in ways that quickly lead to measurable progress on reduced or avoided costs.
A business-wide data and technology-first approach is critical to ensure that this can create new value either as direct revenue or through improving customer experiences. By leveraging technology such as artificial intelligence (AI) and machine learning, banks can create a strong and agile model to better analyse data, but this requires high-quality data in the first place to ensure that the outcomes are trusted.
One of the main components of AI is machine learning and this is used to identify patterns in data and make accurate predictions. This model can find previously unnoticed relationships, interactions, and causational effects in the data. As such, financial services must bring these AI-driven models to their data or develop and train their own advanced analytics in-house. However, for this to work effectively, the model must be trained on lots of data for which outcomes are already known.
Without a financial service institution having a strong data strategy, led from the top of the organisation and encompassing the whole bank, technology investments are at risk of becoming expensive projects, driven by fads and siloed thinking. In the same vein, if the data is not completely shared and orchestrated throughout the organisation, it will not deliver returns on investment or be a useful asset. Data has a central role to transform banks into forward thinking and customer-centric entities.
