Delivering a data strategy

Data strategies must be actionable and robust to be truly effective drivers of change

As investment firms review their strategic priorities having navigated the challenges of the last 18 months, one of the themes we have observed is the increasing importance of a data strategy. By this we mean a unified (top-down) vision for how to collect, process, store, manage, share and use data. It articulates how data will enable the business strategy and sets the foundation for everything the organisation does with data.

A data strategy is not a new concept, but when measuring the maturity of a strategy, it is crucial it enables ongoing business development efforts, such as optimising new technology and enhancing client reporting. Such strategies must be actionable and robust to be truly effective drivers of change. Real-life data-related challenges include partial coverage of the in-house data universe, lack of access to desired datasets, multiple instances of the same data, long reporting lead times / lack of depth due to slow, inaccurate or missing data inputs.

As a firm who has assisted clients with their data strategies over a number of years and one that has developed multiple investment operating models, we are well-placed to observe common challenges and have successfully deployed specific solutions. Managing and improving data quality has often been identified as an area of opportunity for investment organisations – from addressing cost/process efficiency needs to better managing operational risk.

Moreover, Willis Tower Watson’s Thinking Ahead Institute has identified through a study on asset owners that although data sophistication is increasing in support of sustainability, its precision requires greater focus – an effective data strategy can shape governance and culture to ensure precision becomes a priority that ultimately enables the organisation to realise greater benefits. Lastly, data is widely perceived as critical in supporting ESG investing – quality coverage is especially reliant upon quality datasets, governance and reporting.

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