How can financial institutions unlock the full potential of their data

What is data monetization? What does it entail?

Data Monetization refers to the methods of using data with monetizable characteristics to obtain quantifiable economic benefits. This includes creating new revenue streams, achieving efficiencies at scale, and attaining competitive differentiation via unique market positioning. Data can be monetized directly via trading, selling, and trading data, or indirectly via improving processes, building and solidifying partnerships, developing new products/services or publishing branded indices, for example.

What are the sources of data that financial institutions typically use for data monetization?

Financial Institutions can monetize business data such as payments and transactions data from operational sources, anonymised customer and accounts data from commercial sources, internal reports and communications from enterprise sources, and economic, geospatial, and social data from public sources. 

What are the key challenges that financial institutions face when implementing data monetization programs?

The challenges that financial institutions face in generating value from data can be categorised into three broad areas.

The first is data readiness. As data is locked in organisational silos, closed data environments and is often duplicated, this causes data accessibility and availability challenges. There are also data privacy and regulatory restrictions on storing and sharing client data which necessitates appropriate handling and use of data. Additionally, data quality issues can arise, driven by a lack of completeness, correctness and consistency which prevent organisations from achieving the best outcomes from business use cases.

The second challenge is business relevance. With a lack of awareness and understanding of the value of data across business users, this makes identification of relevant business use cases difficult. The lack of data democratisation in the organisation due to limited self-service tools restricts the spread of value generated from data across the organisation. 

And finally, technology maturity is another significant challenge. There are difficulties in engineering diversified and voluminous data in real-time, which prevents organisations from scaling up their monetization efforts. Additionally, insufficient capabilities to integrate data and analytics in customer offerings restrict organisations from showing the value of monetization efforts to their target customers. 

How can financial institutions create new revenue streams through data monetization? What steps can retail and commercial banks, asset managers, and financial services institutions take to unlock the full potential of their data?

There are several dimensions that banks and financial institutions can explore to leverage direct data monetization. 

This is about finding new markets for data, or expanding the customer base to some extent, and leveraging the existing cloud service provider (CSP) tooling/processes for improved efficiency, whilst creating or exposing new data delivery channels. This does not necessarily require CSP involvement, especially for firms with a valuable dataset. 

Another key approach to monetization is to use data to drive analytic insights, and then to monetize either the process itself or the insight that is generated. Portfolio analytics tooling could be a part of this process, which is where clients will pay to access the tooling across their data/trades with the firm. In this case, solutions that allow clients to bring their own data to the firm’s analytic solution in a secure and private way also become interesting.

There are several ways in which financial institutions can look to fully leverage their data. Not only can data monetization generate new revenue streams, but leveraging data can also help to gain a better understanding of customer needs to identify new business opportunities, and can also lead to achieving efficiency at scale, whilst helping to create competitive differentiation. 

In terms of the steps that financial institutions can take to unlock the full potential of their data, financial Institutions should look at their Data Strategy, Governance and Architecture together, to prepare themselves to successfully monetize business data. In order to achieve this, financial institutions should:

• Use a Data Strategy to set a clear and common goal for how they intend to monetize the data. For example, if a business model is to sell data externally, then financial institutions will need to articulate the client market, the distribution channels, the commercial levers and the support model.
• Use Data Governance to ensure they understand what data is available, what condition it is in, who the owner is, what rules must be applied to it, and what it can/cannot be used for. In financial services, you cannot safely monetize data which you do not understand.  Data governance maturity is needed to deliver the strategy, irrespective of the type of strategy that has been selected.
• Use Data Architecture to unlock the data in the organisation. Even with supposedly common approaches (e.g. Data Mesh) we see very different architectures being deployed in order to realise different strategic goals. It is through the architecture and engineering that the Strategy and Governance are united to provide a service that will realise the business’s ambition.

What are some valuable data opportunities that financial institutions can take advantage of?

There are some standard approaches and solutions, and there are some well-recognized vendors who offer components of a data platform for monetization. There are some elements that could be expected for the vast majority of solutions, such as a data marketplace, a data catalogue, differential privacy solutions, access management solutions, a search capability, data storage and analytic environments, for example.

Imagine being able to derive critical insights using the latest technological advances, such as knowledge graphs, and being able to offer better, customised products/services to their customers, which could be a game-changer. Imagine being able to apply Artificial Intelligence algorithms layered on top of a modern data infrastructure and obtain a competitive edge.

How can financial institutions ensure that they are monetizing their data ethically and protecting the privacy of their customers?

Direct monetization can require reasonably sophisticated data masking or obfuscation techniques. This will, for example, replace an age field with an age range or a postal code with a region identifier. Even so, care must be taken that the obfuscation is adequate considering the other data being included in the payload - researchers at Imperial College London found that 99.98% of Americans could be uniquely re-identified in any dataset using 15 demographic attributes.

Another approach for ML solutions is federated learning. This allows firms to train analytic models using data that is siloed within different lines of business without copying or centralising the data. This data can be collectively leveraged while keeping individuals, clients and institutions private, and results can be used without exposing client information.

About Tim Jennings

Tim Jennings is a Senior Director of Global Data Practice. Tim works in Synechron’s FinTech and Digital Enterprise practices, focusing on setting and executing strategic business and technology change for Financial Services firms. He has 20+ years’ experience in Financial Services, with practical experience of business transformation developing strategic IT and data architecture, and leading adoption for front Office, Operations and Control functions.

About Synechron

Synechron is a global consulting firm that combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimisation strategies span end-to-end Consulting, Design, Cloud, Data and Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in its FinLabs, Synechron develops solutions for modernization, from Blockchain and Artificial Intelligence to Data Science models, Digital Underwriting, mobile-first applications and more.