By Boyke Baboelal, Strategic Solutions Director, Asset Control.
Continuous, and often rapid, change has been a given for organisations across multiple economic sectors since the COVID-19 virus took hold and it looks set to be a key feature of the post-pandemic age, whose key trends are already taking shape. Across the financial services industry, change has already become the new business as usual. There is a new dynamic in play, with firms wanting to understand more about what data they have within the firm but also how they can best use it intelligently to drive operational efficiencies and business advantage.
How quickly firms can adapt to change is key and as they look ahead to the future, one of the biggest priorities of many will be to minimise the cost of change, ensuring business continuity and analysing ever-larger data sets.
In response, data management and managed data services are now more important than ever in providing a solution to cut operational risks and lower the cost and impact of change for financial services organisations.
In a world where data is the fuel that has to power business performance, bringing intelligence and facilitating access to that data is increasingly key. Keeping the cost of change in mind, the emphasis needs to be as much on the adaptability and the extensibility of the different data models and the sources as it is about the aggregation of data. Going forward, the focus for financial services firms should now be on actively achieving intelligence from quality data rather than more passively collecting and tracking reams of data for its own sake.
Managed data services offer the potential to reduce the cost of change but only looking at traditional data sets and integration workflows can be myopic. Firms should be looking into new data sets, new sourcing and distribution models and reports to more quickly adapt to change and to increase the actionable intelligence they get from their data management processes.
Today the number of data sets and the diversity of data is increasing across financial services – and the number of alternative data services continues to grow strongly. That in turn makes the data integration task ever more complex. It needs to be set up properly if firms are to avoid not being able to see the forest for the trees. Data is meaningless if it is not available to be used. Data quality is key here as is the capability, speed and reliability of the integration and overall preparation for use where it matters.
Moving forward, data services will also need to bring a more detailed and more transparent understanding of data lineage – where does data come from, what are the ultimate sources and what quality checks took place on the way. The more one gains an appreciation of data flows and data transformation events on the way to data usage the more one can expand augment and enrich these data sets.
That can be done through traditional business rules and through new types of machine learning algorithms and linking new data attributes to enable faster cleaning and wider integration. The traditional function of data sourcing, mastering and quality management has to be rethought. AI and machine learning models can go wildly off the rails if fed with inaccurate data. So, it is important to track the context, metadata, quality statistics and permissions to get the context to prepare data for advanced analytics.
Businesses expect data to be plug and play and so do the algorithms. So, in conclusion, good data management is enabled by introducing processes that create data intelligence, that create a feedback loop for further improvement, a virtuous cycle if you like. To me, data intelligence relates to processes and procedures that bring meaning and depth to classification, categorisation, to rapid labelling and validation of outcomes. Achieving actionable insight is about managing metadata with skill and about building data models that are aligned to the insights that are needed across the business and across the different stakeholders downstream. These insights are discovered through analytics, visualisation, AI models and through different forms of advanced automation.
How quickly you can source, digest and process information and use the right contextual information to positively impact resilience in models in spreadsheets, in applications, in inboxes or in end users’ desks will separate leaders from laggards. But in achieving that, firms need expertise, they need specialist knowledge and understanding and that’s where drawing on the support and help of a managed services provider can really make a positive difference to enable them to cost-effectively take data management to the next level while still focusing on their core competencies. Change will continue to impact financial services firms in the post-pandemic age, how effective they are at managing data to navigate this change will be key to their future success.
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