The Financial Services landscape is being transformed. Long standing value chains are being disrupted and torn apart through the application of new technology in the form of fintechs, regtechs and neo-banks. Customer and business expectations of their bank are evolving. Banks hold a treasure chest of rich data on customers – much more than the Googles and Amazons of the world, yet they often do not manage or leverage this data effectively to generate value, deliver innovative customer experiences or manage risk.
Banks and Financial Services organisations need to transform from product centric, vertically aligned organisations, to be customer and service centric. However, this is not easy. Most organisations have a large number of disparate legacy applications to deal with, a large and growing regulatory burden and many manual data processes across critical functions, including risk and finance. The last decade has been characterised by evolutionary change with new tech and data bolted on in a tactical fashion to minimise costs and achieve regulatory compliance, but the agenda has now changed.
It’s time for a revolution, not evolution – in order to succeed, the bank of the future will look very different from today. Fully leveraging data will require a paradigm shift – embedding data, intelligence and automation into every process and point of interaction will fundamentally change the operating model and architecture of a bank, and we are only at the start of the journey. Data to a digital bank is like air is to a human – it’s not possible to survive and thrive without it, and it is essential to operate effectively. The Economist comment on data being the new oil now feels old, but there are many parallels to a commodity – data needs to be captured, treated, processed and turned into an asset. There is a growing interest in “infonomics”, which looks to value your data and treat it as a separate line on the balance sheet, as it has such an impact a bank’s success (or failure). Gartner predicts that by 2022, 30% of leading organisations will formally adopt infonomics practices and value their information assets, maintaining a balance sheet for internal purposes, and making data a point of competitive advantage. If all this is true, why are so many banks struggling with their data, analytics and AI?
In this blog mini-series, I’ll explore the increasingly important role that data, and the analytics and AI that it enables, plays in digital banking – whether you’re building a new digital proposition, or a fully greenfield bank, covering topics from strategy and architecture through to AI and governance.
Where do you start? Unfortunately most people rush to the technology, and that is mistake number one. Of course technology has a critical role to play when it comes to data, analytics and AI, and especially in a digital bank.
Everyone gets really excited about playing with some shiny new platforms (who doesn’t like exploring new tech, I’ll never forget my first Amiga back in the day...) but the technology will only deliver value if it’s used correctly and aligned to your vision and strategy. Step one is to actually develop a strategy.
What makes a good data strategy for a digital bank? It needs more than just a nice headline about treating “data as an asset”. It can sometimes be hard to see the wood for the trees given the sheer volume / variety of data and the possibilities on how to use it. In my experience it needs 5 key ingredients to be successful:
1. Define a vision statement. This should be the easy bit. Your data strategy should align to business strategy, so don't sit in a dark room drinking lots of coffee and try to “ideate” – simply understand how data, analytics and AI contributes to the success of your digital bank, and then craft a narrative around this. It's important that your data strategy can be linked to something tangible - not a generic "we want to make better use of our data". Focus on your strategic levers - revenue growth, customer experience, cost reduction and risk management.
2. Set business goals & outcomes. Your data strategy is a business conversation, not a technical one, so define your goals and business outcomes and overall value proposition – why do you need data, analytics and AI, what will it enable - what are the edge experiences that you want to deliver for customers? Once you’ve done this, define the specific metrics and KPIs that you will use to measure success across your digital bank. This can be anything from customer growth targets to brand advocacy to operational process STP rates – whatever your priority is. Stealing Alanis Morrisettes pop classic title from the 90’s, it’s ironic how many data and analytics strategies don't use data to track and measure success. This is a must for a data driven digital bank - if it doesn’t get measured, it doesn’t get done.
3. Identify key capabilities and data sources. Capture the capabilities and identify the data sources needed to deliver your business goals, and link them. Make sure your data, analytics and AI capabilities are written in a way that describe the need (either customer or internal) they are meeting. This is your opportunity to excite everyone through demonstrating what the data, analytics and AI will do for your digital bank – use that opportunity wisely.
4. Define your team and stakeholders.
- For your team, identify the skills needed to deliver your required capabilities – data engineers, architects, scientists, analysts, ops, etc. - you get the gist. Work out the skills you need and find the right people.
- For you stakeholders – identify who the consumers of your data strategy are. This could be product owners, proposition leads, marketing, finance, operations. Educate them on your vision and what you’re building for them, and include them as part of your design and build sprints.
5. Develop your roadmap and delivery governance. Finally, bring all of this together in a roadmap to articulate when you will deliver your data features, and the value they will provide. As part of this, it’s important to formalise your data delivery and governance model. Break your development down into manageable sprints, and remember, not everything in your digital bank will work out as expected, especially if you’re attempting new things and innovating at the edge. This is ok, but don’t take 6 months of detailed design work to realise this - fail fast and aim for progress over perfection.
There will always be times during your development when you’re challenged – why are you doing this? What’s the benefit? If done correctly, your data strategy should be your reference point – your North Star, so invest appropriate time in this initiative.
So, data strategy sorted. What next? Architecture, where I’ll look at some of the key data architecture considerations needed for a digital bank.
This blog is the first in a mini-series exploring the increasingly important role that data, and the analytics and AI that it enables, plays in digital banking.