The pandemic has had a detrimental impact on economic growth, unemployment, inequality and poverty levels in different parts of the world. At the same time, from a financial services perspective, it has acted as a catalyst for financial inclusion and fast digital adoption.
The adoption of digital wallets and online payments has seen exponential growth through the pandemic, and this behavioural shift is likely to stick. At the same time, the rapid digitalisation of consumer businesses and commerce has created unique customer experiences through the creation of ecosystems on social media and other platforms (e-commerce, food delivery, ride-hailing etc.).
These consumer businesses are beginning to offer financial services as part of their customer engagement journeys by offering products/services like payments, wallets, Buy Now Pay Later (BNPL), insurance, investments and others to extend the customer value chain, improve customer engagement and stickiness, enhance the customer value proposition and create new avenues for revenue growth.
As more non-banking companies are beginning to offer financial services products and services, a new theme is beginning to emerge in the form of Embedded Finance (embedding a financial services product as part of a commerce journey).
Customers leverage these integrated experiences, and traditional financial services firms quickly realise this shift. To meet the rising demand for embedded finance, banks are responding by offering banking as a service (bundled offerings, often white-labelled or co-branded services) that non-banks can use to serve their customers.
For customers, the appeal is simple: ease of use. It provides them with immersive experiences which are holistic, easy and embedded.
It is also quite an inflection point in the industry from a retail financial services perspective. In a post-pandemic world, along with all the macroeconomic uncertainties and increasing operational cost pressures, retail banks and financial services firms are increasingly looking at new revenue models focused on fully digital distribution while reducing their vast network of brick-and-mortar physical infrastructure.
In this context, enabling partners to distribute banking products can be good news in the form of being a low margin, high volume business for banks.
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In this fast-changing paradigm with intense competition between many players (‘every company wants to be a fintech’), knowing your customer and personalising experiences become critical differentiators. Hyper-personalising experiences that are contextual and relevant to customers is becoming a key aspect of customer engagement and retention.
In order to create hyper-personalised experiences for customers and appeal to their moment of truth, enterprises are essentially focusing on three aspects:
- Providing meaningful content: Real-time alerts, tailored web content and personalised advertising and pre-populated applications.
- Tailored products and advice: Real-time product notification and transaction triggers, dynamic pricing and hyper-customised offers, personal finance management alerts.
- Optimised service: Interaction with customers at the right time, through the right channel, contextualised and high-quality responses, and a seamless phygital (physical + digital) experience.
To facilitate such hyper-personalisation and become a data-driven enterprise, it’s important that businesses democratise their data while elevating their digital and data infrastructure.
A study reports that only 24 per cent of businesses claim to have succeeded in creating a data-driven organisation despite the widespread effort. Why is this the case? Of the many challenges, key ones involve data quality issues due to lack of data ownership, data silos because of enormous legacy estate and the overburdened analytical data platform teams.
Over several decades, financial businesses have accrued generations of data warehouses transferred from one employee to another.
A UK report shows that nearly 92 per cent of financial firms rely on legacy tech. How is this a challenge? Fetching data from these legacy data warehouses isn’t easy as the warehouses might not have active vendor support. The capability to query from them is scarce, and so is the capability to move away from them.
In such cases, data discovery and governance are a constant hassle. All the more, in financial services, these legacy data warehouses might be the core banking data warehouses and file systems, so the risk of moving away is high. This debt in terms of effort and cost will continue to accrue until the company finds that most of its data is still in silos that are opaque.
Despite moving away from legacy systems, businesses cannot become data-driven. Why is that so? Some challenges include data quality issues, people and processes not being factored in, and overworked data platform teams. Analytical use cases such as hyper-personalisation are seen purely as a central data platform effort.
From an infrastructure perspective, it makes sense to centralise, but this also has led to the individual business lines or departments not owning their data from a quality, accessibility, discoverability and governance standpoint.
Given that there’s no data ownership, it leaves the value of enormous datasets untapped. “We’re surrounded by data but starved for insights”, it is rightly said!
Centralise data infrastructure, but decentralise data ownership
So, who understands the data assets in businesses the best? The very people who generate the data.
For instance, in retail banking, we are talking about channels, payments, accounts, mortgages, et al. as potential domains. The teams and systems within these domains revolve around the objectives of these business lines.
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Similarly, the ownership of data for operational and analytical purposes also should rightfully sit within these domains, with the right roles and responsibilities.
These domains are best placed to identify and deliver use cases that can generate value from the data that they own. The best way is to start with understanding the vision of the domain, the goals that need to be met, and the business use cases that can help achieve these goals.
From there, businesses can then create the data journeys needed to satisfy said use cases. Moving away from legacy data warehouses can be achieved similarly by approaching moving away to a modern data solution as a measure of success of this use case. Therefore, with every business use case, businesses are incrementally reducing the legacy data landscape.
In order to become a data-driven business, enabling strong data governance consisting of data quality, ownership, metadata management et al. is key. With domains, data governance will also become a federated concern between the enterprise and each individual domain.
Functions such as compliance, domain identification, discovery, and lineage could be centralised, while data ownership, quality, and metadata management can be decentralised to the domains.
Businesses should ensure that they can bake in most of the cross-cutting concerns into the centralised data infrastructure platform and therefore make the most out of the governance functions computational while making it auditable in nature.
When done right, the above-said data mesh paradigm will be a great way to enable the democratisation of data within businesses.
Data is the new oil
But unlike oil, data hasn’t been tapped in the enterprises to the extent of generating value. It sits as crude oil in data lakes, unused. It’s high time businesses start treating data as a product that provides significant value by making it discoverable, accessible, trustworthy and secure.
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Businesses can start doing so by creating data products that can be used by end users. On top of that, federated governance and empowering individual domains will also help bring the product ownership needed to innovate, experiment and iterate faster to build data products and drive innovation.
While doing so, businesses should also keep in mind and respect personal data protection policies such as GDPR, PDPA and several others. Consent management should be a key consideration before using customer data for any analytical needs.
Authentication, authorisation and appropriate data anonymisation should be ensured so that customers are protected from any re-identification risk. This is an extremely important aspect of data governance.
Becoming data-driven is imperative for any business in today’s age and proves to be especially so for financial firms in the post-pandemic world. Apart from investing in modernising the data infrastructure, it is highly important to bring out the organisation-wide cultural and mindset change to start treating data as an asset.
With the aforementioned approach, businesses will not only be transforming the data estate technologically but will also be able to use data literacy to build products that enable hyper-personalisation. Only then, will data generate as much value as oil instead of being untapped in underground wells!
This article is co-authored by Lakshmanan CS, Principal Consultant for Digital Transformation, Financial Services, Thoughtworks
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