Data-driven financial services, a bigger imperative in a post pandemic world 

The pandemic has had a detrimental impact on economic growth, unemployment, inequality and poverty levels in various parts of the world. At the same time, from a financial perspective, it has acted as a catalyst for financial inclusion and rapid digital adoption.

The adoption of digital wallets and online payments has seen exponential growth throughout the pandemic, and this behavioral shift is likely to stick. At the same time, the rapid digitization 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 starting 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 expand 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 begin 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 trade journey).

Customers are leveraging these integrated experiences, and traditional financial services companies are quickly realizing this shift. To meet the growing demand for embedded finance, banks are responding by offering banking as a service (bundled offerings, often white-labeled or co-branded services) that non-banks can use to serve their customers.

For customers, the appeal is simple: ease of use. It gives them immersive experiences that are holistic, easy and embedded.

It is also something of a focal point in the industry from a retail financial service perspective. In a post-pandemic world, with all the macroeconomic uncertainty and rising operating cost pressures, retail banks and financial services companies are increasingly looking at new revenue models focused on fully digital distribution while reducing their vast networks of brick-and-mortar infrastructures. .

In this context, allowing partners to distribute banking products in terms of being a low-margin, high-volume business for banks can be good news.

Read also: Healthtech data: The race for new oil in Southeast Asia

In this rapidly changing paradigm with intense competition between many players (‘every company wants to be a fintech’), knowing your customer and personalizing experiences become critical differentiators. Hyper-personalizing experiences that are contextual and relevant to customers are becoming a key aspect of customer engagement and retention.

To create hyper-personalized experiences for customers and appeal to their moment of truth, companies basically focus on three aspects:

  • To deliver meaningful content: Real-time alerts, tailored web content and personalized advertising and pre-populated applications.
  • Tailor-made products and advice: Real-time product notifications and transaction triggers, dynamic pricing and hyper-customized offers, personal finance alerts.
  • Optimized service: Interaction with customers at the right time through the right channel, contextualized and high quality responses and a seamless phygital (physical + digital) experience.

To facilitate such hyper-personalization and become a data-driven enterprise, it is essential that enterprises democratize their data while elevating their digital and data infrastructure.

A study reports that only 24 percent of companies claim to have succeeded in creating a data-driven organization despite the extensive effort. Why is this the case? Of the many challenges, the main ones involve data quality issues due to lack of data ownership, data silos due to huge legacy, and the overburdened analytical data platform teams.

Over several decades, financial firms have accumulated generations of data warehouses that have been passed from one employee to another.

ONE UK report shows that almost 92 percent of financial companies rely on legacy technology. How is this a challenge? Retrieving data from these legacy data warehouses is not easy, as the warehouses may not have active vendor support. The ability to inquire from them is scarce, as is the ability to move away from them.

In such cases, data discovery and management is a constant hassle. Even more so, in financial services these legacy data warehouses can be the central 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 realizes that most of its data is still in silos that are opaque.

Despite moving away from legacy systems, companies cannot become data-driven. Why is that so? Some challenges include data quality issues, people and processes not taken into account, and overstretched data platform teams. Analytical use cases such as hyper-personalization are seen exclusively as a central data platform effort.

From an infrastructure perspective, it makes sense to centralize, but it has also led to individual business areas or departments not owning their data from a quality, availability, discovery and governance point of view.

Since there is no data ownership, it leaves the value of huge data sets untapped. “We are surrounded by data but starved for insight”, it is rightly said!

Centralize data infrastructure, but decentralize data ownership

So who understands the data assets of enterprises best? The very people who generate the data.

For example, in retail banking we talk about channels, payments, accounts, mortgages, et al. as potential domains. The teams and systems within these domains revolve around the goals of these business areas.

Also read: How to unlock opportunities through technologies to improve data protection

Similarly, the ownership of data for operational and analytical purposes should also rightfully reside 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 they own. The best way is to start by understanding the vision for the domain, the goals that need to be met, and the business use cases that can help achieve those goals.

From there, companies can then create the data journeys that are necessary to satisfy the use cases mentioned. Moving away from legacy data warehouses can be similarly accomplished by approaching moving away to a modern data solution as a measure of success for this use case. Therefore, companies gradually reduce the legacy data landscape with each business use.

To become a data-driven company that enables strong data governance consisting of data quality, ownership, metadata management et al. is the key. With domains, data management will also become a federated concern between the enterprise and each individual domain.

Functions such as compliance, domain identification, discovery and lineage can be centralized, while data ownership, quality and metadata management can be decentralized to the domains.

Enterprises should ensure that they can bake most cross-cutting concerns into the centralized data infrastructure platform and therefore get the most out of the governance functions computationally, while making them auditable.

When done right, the above data mesh paradigm will be a great way to enable the democratization of data within enterprises.

Data is the new oil

However, unlike oil, data has not been exploited in companies to the extent that it generates value. It sits like crude oil in data lakes, unused. It is high time that companies start treating data as a product that provides significant value by making it visible, accessible, trustworthy and secure.

Also read: Conservation technology: The role of data and technology in dealing with the biodiversity crisis

Companies can begin to do so by creating data products that can be used by end users. On top of that, federated governance and empowerment of 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, companies should also keep in mind and respect personal data protection policies such as GDPR, PDPA and several others. Consent management should be an important consideration before using customer data for analytical needs.

Authentication, authorization and appropriate data anonymization should be ensured so that customers are protected from any re-identification risk. This is an extremely important aspect of data management.

Becoming data-driven is imperative for any business in today’s age, and it’s proving to be especially so for financial firms in the post-pandemic world. Apart from investing in data infrastructure modernization, it is extremely important to bring about the organization-wide cultural and mindset change to start treating data as an asset.

With the aforementioned approach, companies will not only transform the data estate technologically, but will also be able to use data literacy to build products that enable hyper-personalization. Only then will data generate as much value as oil instead of sitting untapped in underground wells!

This article is co-authored by Lakshmanan CS, Principal Consultant for Digital Transformation, Financial Services, Thoughtworks

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