Modernising data management in banking
Many banks have high ambitions in the field of AI but are held back by fragmented platforms, silos between teams and inconsistent data quality. With Data Bootcamp, Orange Business brings together key personnel from both business and IT to establish a shared understanding of the current situation and a clear plan for modernising data operations.
“The aim is to rapidly transition the bank from an inefficient current state to a future-ready, more effective way of working,” says Joakim Valderhaug, Cloud & AI Sales Lead at Orange Business.
What is Data Bootcamp – and why now?
Valderhaug describes the bootcamp as a condensed overview of everything that needs to be in place to ensure the modern and effective data management”.
It’s all about technology, platforms, tools, processes, governance and people. In Data Bootcamp, we have brought together the key areas that we believe must be covered to be ready for the data and AI needs of the future.
The duration and content are tailored to the bank’s business needs, he emphasises.
Data Bootcamp is tailor-made for each client. If you have recently completed a platform migration, we focus on data governance. If you have a more comprehensive transformation ahead of you, we cover the entire spectrum.
The format: short, focused and relevant
The process normally takes two to three days but is determined by the bank’s actual needs. Some need to get more value out of a modern platform they already have. Others need to take a bigger leap from an older platform to a scalable cloud architecture. What they have in common is that the work focuses on real bottlenecks – not general lectures.
A recurring theme is that platforms, processes and people are often fragmented. Analytics environments and infrastructure management operate in different ways, sometimes using different technologies, which creates silos. This makes it difficult to achieve consistent data quality.
Target Data Platform Architecture is therefore a major part of the work, both for those transitioning from old to new systems and for those who are already using modern technology but are fragmented, says Valderhaug.
He is clear that governance is a prerequisite for quality and value creation.
The level of maturity in data governance within the Belgium market is quite low. The risk of incorrect decisions based on poor data quality is high. The business case for strengthening governance to improve data quality is strong, particularly in banking and finance.
There is a high level of understanding of this issue at the top of the organisation. According to Valderhaug, the CEO, CFO and IT management all recognise the risks associated with poor data quality, but the dialogue must be tailored: to the business in terms of value and risk, and to IT in terms of implementation and operations.
Who needs to be involved – and who is responsible for the follow-up?
The bootcamp brings business and IT together.
We’re aiming for a roughly 50/50 split between IT and business operations. Everyone must work with data if we are to become a data-centric organisation. IT, business operations and system owners – ideally at management level – should be involved, says Valderhaug.
Ownership varies depending on the level of ambition. It may lie with the CEO, IT director, CTO, CDO or the security function.
Who owns the process depends on our approach and the outcome the client wishes to achieve. We’re also seeing IT and business converging more now than in the past, he says.
Tangible results – not just ambitions
Once the bootcamp has finished, the bank will have two concrete documents and a recommended starting point.
“One is a technical summary of the current situation regarding data governance, architecture, processes and tools, as well as any gaps identified. The other is a project proposal for moving towards a modern data platform with high security, strong governance and high maturity,” says Valderhaug.
It also includes a recommendation on where the bank should start, as well as a budget proposal showing the use of Orange resources and internal resources, broken down by role. The duration of a modernisation project can vary from weeks to years, depending on its scope and ambition
From ambition to production
Which needs are most often prioritised when drawing up the roadmap? Valderhaug points to reporting requirements and regulatory compliance. The bank usually knows where the ‘pain points’ are, and measures are targeted at these areas – whilst at the same time laying the foundations for good data quality and a common architecture.
AI is driving the need for better data practices and platforms. Valderhaug points out that many AI initiatives fail due to poor data quality – and that a flexible, accessible platform that supports relevant tools makes AI work effective in practice.
– AI is very much the talk of the town now. At the same time, it has become clear that AI technology can deliver business value. An initiative to improve the state of the data platform therefore has significant upside potential, as it paves the way for AI. At the same time, the data platform is something that, regardless of the context, benefits from being modern, which means the risk is relatively low.
Obstacles – and how to get past them
Typical obstacles are rarely solely technical in nature.
Fragmented teams, departments that are unwilling to participate or a lack of mandate can slow down the work. In such cases, it is necessary to secure buy-in higher up in the organisation and establish a common direction. The key is a data-centred approach where everyone has a connection to the data, he says.
Preparations ahead of the launch are minimal.
We need a preparatory meeting lasting two to four hours to tailor the content to the bank in question. We do the rest together on site.
Industry standards and choice of platform
Many banks have come a long way, but there are significant differences. Valderhaug points out that Snowflake, as a ‘data cloud’, has become an industry standard for many, and that combinations such as Azure and Snowflake are common architectural patterns. At the same time, he emphasises that the aim of the bootcamp is to gain control over data, working methods and governance – regardless of whether the platform is cloud-based, on-premises or hybrid.
Data Bootcamp makes it easy to get started on the right path, with a shared understanding and a well-founded plan.
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