Introduction & observations
In an ever-changing market cost/income ratio of financial institutions is under heavy scrutiny. Linked with the ever-increasing push to support end-customers in the right manner, at the right moment and through the right channel. The key is data; which is too often seen as a cost driver. Proper attention to data is often only paid after regulatory pressure or fines. Let’s see data as a value driver, not a cost driver.
Data needs to be valued as an enterprise asset. The challenge is that there are not many generally accepted ways to put value on data, which leads to some questions:
- What would it cost to rebuild your full data collection?
- What would it cost to buy your data on the market (if available at all)?
- How much more revenues could you generate if you have additional (quality) data?
- How much cost could you save if you would have perfect information at the right time?
The incremental revenues is the most interesting question to ask. It will help you think much more in the lines of value rather than cost.
Key examples of data as a value driver for the Financial Services industry are:
At all financial institutions data challenges are imminent: KYC, CDD, GDPR, Basel and other key topics on the executive board agenda are driven or led by data. To address these challenges, and to comply with these regulations data needs to get in order and underpin value by data based, trustworthy evidence.
In this process financial institutions are already creating a lot of value, showcasing the right information, no double work, single sources of truth finally start to exist, and a single view of the customer will slowly be created. Resulting in value from data.
Poor data quality can lead to additional cost, more waste, rework in business processes and undetected risks. Poor data quality in many cases is also an inhibitor for unleashing the potential of Artificial Intelligence.
In many situations some of the consequences of risk/cost-based approach lead to additional collateral damage. For example, the cost of resolving technical debt on data ecosystems is becoming increasingly high as risk avoidance keeps the system unaltered, resulting in a deteriorating competitive position. Moving to a data value centric approach can help turn this around.
Moving away from the risk driven, fine based approaches to programs like GDPR into a holistic overview and the opportunity to bring a better personalized and improved offer to individual customers. Making it a completely different business case; seeing where early movers in digital transformation are and will be focussing on the hyper personalization the GDPR programs could be a value driver with a positive focus.
This also applies to the current programs in KYC/CDD a defensive mode looking in solving the issues, showcasing that you are in control regarding who is in your books, who is your customer and caring that your customers are doing legal and compliant financial transactions. Whilst this also is a next step towards a pristine path to serve your customer even better.
And market agility
Market developments are increasing in speed and impact, disruptors are around the corner. Not in the last place from companies which are most data savvy: techfins. Although they might not have the in-depth financial services knowledge, they do have an abundance of data and the passion to drive value from it.
With a more value-based approach on data, financial services companies can increase the speed and quality of operations, supporting reduced time to market of new offerings and be at the forefront of (re)inventing financial services.
With innovations thriving on data
Outlining a number of topics that can only be done if you let go of the risk/cost-based approach of data and approach it from a (business) value perspective, foundational for the survival in the future.
- Hyper personalization: shifting from (generic) customer journey thinking to hyper personalized thinking, be at the heart/mind of your client.
- Embedded Banking/Insurance: financial services as a seamless part of other services, API based business models
- Open X: expanding business models, shifting from single products to full services/experiences
- License to Operate: regular demand for data (level of detail, frequency, coverage) will increase, want to do it ad-hoc every time or choose a flexible data ecosystem approach that is able to deal with any future demand swiftly and without friction?
Change your mind set about data: it harbours huge business value, but harvesting this value is many times blocked by working in a cost/risk-based approach. Data is often referred to as the new oil or water, look at it differently:
- If data is the new oil, why not do additional refinement, creating much more valuable products for your clients from the raw material?
- If data is the new water, why not make sure that it is treated as the primary source of your organization’s life?
Choose a data valuation approach that fits your organizational culture, make the value of data tangible and widely acknowledged. Define initiatives to improve the value of data and use the incremental business value to reinvest in the next steps of the journey.
Additionally, ensuring your enterprise data liquifies, achieving a situation where it flows freely through the enterprise, without friction to the point where it is needed for decision making and help achieve ultimate stakeholder value.
Finally, expanding horizons by considering data as a value driver. The platform economy continues to be a big opportunity for Financial Services companies that embrace it, sharing data with platform peers, aggregating client value offering a tailored family of adjacent services.
No matter the angle data is a value driver, and it is time to acknowledge and manage it accordingly and harvest its yields!
Vice President – Insights & Data
Chief Technology Officer (CTO)