Article by Charles Evans, CFA Managing Principal, CAPCO

Before exploring how best to make data work for the enterprise, it is important to understand the different external dynamics that are driving the need for change. A Porter’s Five Forces framework shows that the enterprise strategy of traditional industry players needs to evolve, with data at the heart of the change. This is driven by heightened client expectations, complex new interactions with third parties across the value chain, an increasingly competitive environment, and expanding regulatory requirements.

An increasingly competitive environment, where data and technology are differentiators for the business model. Data is not only a driver for reducing costs and regulatory compliance. The improving capabilities of Artificial Intelligence (AI) and analytical technologies, increased computational power from the cloud, growing volumes of data and new data sources are collectively enhancing the art of the possible in the organisation, front-to- back. Key examples include improved client analytics and real- time risk analytics.

Regulators are requiring banks to put data on their agenda. Regulation (MiFID II, SFTR, EMIR, FRTB, PRIPS etc.) is shining a light – and raising the bar – on how data is governed, managed and used within Capital Markets enterprises. In response, enterprises are forming new data risk frameworks to formally define & manage the risk to the enterprise associated with data.

Data is an asset that can serve as a differentiator for Capital Markets enterprises. Whether a bank is providing bespoke client services, eliminating manual work, integrating external services, enabling decision making, or reducing time spent on low-value high-frequency tasks, the future of value creation across the enterprise is rooted in data.

CHARACTERISTICS OF DATA WORKING FOR THE ENTERPRISE: 

Bespoke client services – Every client is different, but they all value a level of service that is tailored to their specific needs, while also sharing some common characteristics. Data from across various lines of business can be brought together to ensure a seamless client interaction at the point of service. Beyond the ease of interaction, there is additional value to be captured by leveraging data sources to derive insights into clients’ needs and behaviours, allowing the development of real-time targeted recommendations and personalised service offerings. These insights can not only help your client feel connected to your organization, but your service team can also recommend highly targeted cross-sell opportunities, unlocking the secret to diversifying your client’s wallet. A data-driven strategy that provides a low cost of bespoke servicing increases client satisfaction, retention and revenues.

Become less manual – Manual processes have higher operational risk and are likely to be costly in the long run. Instrumentation of these processes can provide insights around the critical points of integration, thereby driving your automation strategy on empirical evidence and reducing the cost of insight. Automation can be accelerated by digitizing and structuring data, reducing the time spent on data sourcing and interpretation. There is also increasing value in leveraging unstructured data. For example, intelligent automation strategies – such as the real-time capture, interpretation and digitisation of emails and chat messages – can automate the pricing and booking of transactions or responses to client queries. 

Integration of external sources – External data sources are a significant cost to all Capital Markets organizations, but the rise of new alternative datasets can provide a competitive edge by unlocking additional insights for a differentiated perspective. For example, the need for understanding and transparency around Environmental, Social and Governance (ESG) factors has created a market for such alternative data sets. These new data requirements will drive change in your infrastructure as well as in the external data market. Even the operating processes and systems at data origination are likely to be forced to evolve to deliver the required granularity of data models at transaction, product and client level.

Right data available at the right time for decision making

 Currently, financial institutions battle to provide ‘as much data as possible’ to support decision makers. The reasons for this current focus are twofold. First, organizations aim to provide the highest data quality possible – i.e. the data is accurate, complete, and timely. Second, financial institutions are working to increase access to data for decision makers through the use of business intelligence and dashboards, or by integrating the data on one screen for improved visibility. A data-enabled organisation will go one step further, providing the ‘right data’ at the right time to enhance decision making, using patterns, Machine Learning, AI and other predictive capabilities to provide recommendations on which data sets should be presented for decision making.

Less time on low-value, high frequency tasks – In a high-functioning organization, skilled employees are focused on the important tasks, utilising their talent and experience to provide maximum value to the enterprise. However, in many organizations, employees of all types often spend significant time on low-value, high frequency tasks. Others suffer through endless context switching that can distract them from important tasks and is also a cause of employee dissatisfaction. Data can be used to define and manage rules that better route these tasks, reducing distractions and automating where possible within a defined and more streamlined workflow.

A CHALLENGING DATA LANDSCAPE 

Firstly, Data Management is a fundamental capability, and one in which many organizations have already invested. Data Management requires an understanding of the data, implementation of data accountabilities, and addressing underlying data quality issues by implementing a pragmatic control framework. Recent regulatory requirements, such as BCBS 239 & CCAR, have prompted many banks to create sophisticated data management assets that can be leveraged and curated to make data work for the enterprise.

Next, Data Architecture requires an intentional design approach to how data is stored and flows around the organisation, decoupling data from legacy architecture, breaking down data siloes across businesses, and optimising data-in-motion to reframe business agility. Whilst this might seem like an impossible challenge, now is a perfect opportunity to implement these changes as financial institutions look to address their legacy technology landscape as part of their move to Cloud infrastructure. There is a clear opportunity to undertake architecture modernisation efforts, and embed enhanced data standards, governance and lineage within the fabric of the organisation. Capco is co-chairing the Cloud Data Management Capability Workgroup in partnership with the EDM Council to help move the industry forward in this regard.

 

Finally, Data Literacy enables employees to make the most out of the organisation’s data, trust the data and use the data in a responsible and ethical manner. An organization with high Data Literacy empowers people and teams to understand, use, experiment, and make decisions with data that positively impact the overall data assets of the organization. There should be a major effort to upskill your workforce to improve levels of Data Literacy, and banks should also be championing Data Literacy externally to clients and other third parties in the value chain.

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