Data strategy: The key to success in financial services
Serisha Beosumbar, Group Chief Financial Officer/ Executive Director at TechSoft International
Even though concepts such as DevOps and MLOps have gained support in recent years, FinOps is a relatively new discipline that has yet to become generally practised. And yet, its potential to enhance financial services through a reinvented data strategy is significant.
FinOps can be defined as an evolving cloud financial management discipline and cultural practice that enables organisations to get maximum business value by helping engineering, finance, technology, and business teams to collaborate on data-driven spending decisions.
The key to this is the importance of the cloud and data in enhancing the decision-making process around finance. McKinsey writes that companies in the financial services sector can grow their bottom line by up to 25% using data-driven business initiatives. It says that while analytics was once used only to inform decisioning, it is now embedded in products, processes, services, and front-line activities.
With FinOps and refreshing a company’s data strategy promising such significant improvements, organisations are searching for specific use cases where they can test the approach before rolling it out more extensively across operations. These can include identifying sprawling data in the cloud to data provision lakes and serving advanced analytics use cases; complementing IT asset management, governance, and compliance efforts; and supporting ESG and other sustainability initiatives by injecting a layer of transparency to the process.
Governance improved
Given how vital data governance has become in ongoing efforts to implement digital transformation initiatives for financial services providers, the promise of FinOps and a ‘new’ data strategy is simply too good to ignore.
Since many organisations still rely on disconnected information silos, it is challenging to ensure uniform data governance. A data management strategy for a FinOps-driven environment must therefore focus on how best to create a single source of the truth. If this does not happen, companies will be unable to analyse the data at their disposal accurately and miss out on potential growth opportunities.
Think about it, connecting the core business strategies with data and analytics is the main driver behind any digital transformation initiative. Many companies struggle to effectively manage their reporting and operational requirements due to poor quality of data and a limited view of the data on-premises or in the cloud. Solutions that can help automate tasks, reduce resource effort, and increase operational efficiency are enablers to help facilitate a changing data strategy. It all boils down to producing clean, coherent, and non-redundant data that can be confidently used to discover actionable insights.
Transforming for growth
A more modern, agile data strategy enables the financial services provider to put a framework in place that lets it mobilise and direct data towards the actions that the company needs to deliver. In doing so, the business can ensure that information is optimised and integrates with the goals and expectations of the company.
There are five considerations around developing such an advanced data strategy. It starts by considering the evolving technology and architecture that impacts business development and data analysis. But it is not a case of ripping and replacing the old with the new. Decision-makers must consider how to deal with legacy solutions and any third-party data management services already in place.
Secondly, understanding how best to deal with exponentially growing data volumes will significantly influence how to scope the data strategy. Not only is the amount of data increasing, but its variety and velocity must also be accounted for. Data management skills and technologies need to be continuously updated to reflect this, with the strategy becoming more of an organic structure than something cast in stone.
The third consideration examines data quality by unpacking the reliability of the data points that are already in place. Of course, all the data in the world means little if there is no access to quality data. Re-evaluating the data strategy empowers decision-makers with the insights necessary to identify where the weak points in the data lie and how to improve them within a restricted data governance framework.
Furthermore, the security of the data cannot fall by the wayside. Companies must employ risk analytics techniques that identify and manage these defences more proactively than at any time in the past. When a data strategy is redeveloped, security initiatives must be prioritised, keeping in mind evolving technology and the growing sophistication of cyberattacks.
Ultimately, re-evaluating a data strategy will assist financial organisations in identifying the technology and business gaps inside the organisation and how best to fill them as they prepare for FinOps. Crucially, such an assessment gives the business a deeper understanding of the specific rulesets needed to support an optimised data environment that best positions it for growth.