Our banking clients recognise the power of data, though working out how to manage and leverage it to its maximum potential is not easy. As banks, their customers and enabling technologies change at pace, so does the breadth, volume and diversity of data, creating an ever-evolving challenge. We believe that pursuing a product-led approach to data management and enablement is the path forward, not only for an enhanced data-driven ways of work today, but also for setting the foundation for enhanced value tomorrow. 

The banking data challenge 

Financial institutions are constantly striving to outperform competitors, deliver innovative value to customers, manage risk effectively, and adapt to the ever-changing dynamics of the market. Today more than ever, data sits at the heart of these efforts, serving as the key to unlocking insights from historical, transactional, operational and market data. However, building an enterprise view of data is not easy, particularly as the pace of technological change creates new and varied data sources that need rationalising, categorising and analysing to derive meaningful value.  

Traditionally, two approaches have been adopted to manage data. The bottom-up approach involves individual functional teams creating, storing and utilising data tailored to their needs, leading to fragmented data efficacy, duplicated organisational efforts, and a complex technological infrastructure. Conversely, the top-down approach, where a central team defines and manages data aggregation and extraction for the entire enterprise, often results in bottlenecks in data management and governance, as well as a misalignment with the specific needs of business units for effective execution of use cases. 

A new approach to data management 

To tackle these challenges, banks are increasingly pursuing a productised approach to data management as a means to consolidate and structure data for enterprise use. A productised approach means managing data in the same way you would a consumer product – analysing what consumers want and need, prioritising product features, and so on.  

Put simply, a data product is a logical grouping of data designed with commonality, consistency, and, most importantly, user consumption in mind. In banking, we see these typically grouped around customers, accounts and specific product areas such as deposits, loans and investments. The goal is to create easily accessible, ready-to-use datasets that can be utilised across the organisation to meet diverse needs. This not only establishes a single source of truth but also eliminates the tangled web of disparate data storage and access methods. Moreover, data products promote enhanced data literacy by presenting data in a clear and logical manner, supported by robust data governance frameworks that are centrally defined but locally implemented, instilling confidence in users regarding data access and utilisation. 

Understanding the productised banking data ecosystem 

Let’s unpack a productised banking data ecosystem in more detail – what does it look like and why does it matter? 

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Banks run innumerate business processes on a daily basis both to keep the lights on and support the realisation of specific strategic ambitions, such as growing deposits or expanding the customer base. Take, for example, a retail bank moving through the process of customer enrolment: a prospect enters through an active channel (digital, online, branch, etc.), an application is created and reviewed before a customer and account are created with supporting personal information. So, before a transaction even takes place, data has been produced at every touchpoint requiring storage for use. 

While this data is useful in its raw format and business lines can access it to address specific needs and use cases, where the enterprise value really kicks in is in how it’s then consolidated, structured and managed – in this instance, as common data products. A productised approach means that raw data is created and ingested from core banking systems, digital applications and sources across the enterprise, before being productised and published for wider consumption.  

By taking the data product approach we can deliver high-quality, ready-to-use data through a virtual marketplace to individuals across the organisation. This marketplace provides data, governed, stored and enriched by clear standards for access and use, where users can consume using a diverse setup of methods, including reporting, AI, advanced Analytics, self service and digital applications. This marketplace can be internal-facing, external-facing, or both, depending on the maturity of the model and organisational appetite. In an internal context, business areas subscribe to data products or raw data and, assuming they meet the access requirements and agree to operating standards, are able to consume data and critically, produce valuable data that can be published back to the marketplace. For example, a corporate intelligence unit consuming the loan and customer data products to produce a regulatory risk code that is valuable for other business areas and ad hoc use cases. A similar concept applies in the external model though there are more stringent considerations required around which data can be published without compromising sensitive information before businesses can even think about commoditising what they publish and establishing the operational requirements for a functioning marketplace. Nevertheless, the opportunities exist which is what makes the productised approach to data so compelling.  

What next? 

Having worked with clients to design and implement a productised banking data ecosystem, we understand the prerequisites for success as well as the common pitfalls. This is what we’ve learnt: 

  1. Start small and scale – Like with any initiative, proving value early is key. Starting with a manageable product area with dedicated development support and business SMEs to establish the first data product before tackling more complex data domains (e.g. customer). This will help you prove out the approach and importantly, build understanding, excitement and learnings to bring to the next iteration. 
  1. Bring the business on the journey early – Without buy-in and adoption, the new data ecosystem won’t reach its full potential. Ensure business stakeholders and established data users understand the strategic thinking behind the new approach and that there is a clear articulation of business value to ensure you have champions not detractors amongst the business.  
  1. Manage data products like any other product – To meet the target standards of accessibility and utility, data products should be owned and managed like any other product area. Establishing a product manager early will establish accountability and ensure the product is built, managed and evolved effectively, whilst also offering an expert point of contact for business areas.  
  1. Don’t leave data governance as an afterthought – Secure and effective business use of developed data products requires right-sized data governance standards, policies and procedures. It’s important to build the required governance framework in tandem with product development to ensure a continuous feedback loop and the development of a new environment that meets all required risk and operating standards. 
  1. Establish the right operating model – A new data ecosystem mandates an adjusted operating model to meet the ownership, management and governance needs of the new environment. Considering this early, particularly the roles, skillsets and capabilities needed to deliver, is critical to operationalisation and adoption. 

Whether you are looking to commoditise a data marketplace or just defining your data transformation strategy, we have the expertise and learnings to support you on your journey. Connect with us to discover more. 

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