6 trends in insight driven data management

Financial institutions need to improve data quality and availability not only to meet operational goals, but to contribute to core competencies in managing investment performance, reducing risk and meeting compliance goals. Our research shows firms are looking to adopt new and additional capabilities to derive higher levels of business value from their data management efforts. …

by | January 14, 2019

Financial institutions need to improve data quality and availability not only to meet operational goals, but to contribute to core competencies in managing investment performance, reducing risk and meeting compliance goals. Our research shows firms are looking to adopt new and additional capabilities to derive higher levels of business value from their data management efforts. They are moving from operational and IT driven data projects, to a more customer and insight-centric approach.

Crucially, the value of any insight-driven data project is decided by evaluating business-relevant use cases, not generic technology benefits. The outlines of this new generation of insight-driven data management are clear. The volume and complexity of data is increasing rapidly due to regulatory pressure and new data-intensive business processes. The demand for change outstrips firms’ internal capacity, at the level of infrastructure and change management. Banks are looking to reduce that operational and financial burden through cloud deployment, and adoption of ‘native’ data analytics – allowing them to bring analytics to the data instead of endlessly moving data between analytical apps.

The devil, as always in data management, is in the detail of our questions: What can we learn from acquired, derived and manufactured data? How can data management and analytics deliver business value? What will make that happen? What is holding firms back? What are the specific problems firms want to solve with these new technology capabilities?

In research conducted through ADOX Research, we questioned 80+ survey participants both on core drivers for change and on use cases (some familiar, some new) which will be crucial in realising the opportunities for insight-driven data management. Our findings are summarised in six key observations:

  1. Data management priorities move to handling volume and variety
  2. Cloud and AI have become core tenets of technology infrastructure
  3. Deployment and development preferences are shifting
  4. Top priorities are: Integration, Integration, Integration
  5. Opportunities outscore obstacles; nonintrusive and use-case driven data management can deliver new capabilities
  6. Use case, not technology, drive value and change

After the over-ambitious promise and elusive goal of enterprise-wide standardisation of financial data, a new generation of data management solutions is moving beyond operational and IT efficiency as a primary goal. While core data management capabilities such as data lineage and data governance deliver trusted foundations for analytics and analysis and provide enterprise benefits (and are indeed required by regulatory initiatives ranging from Mifid II to TRIM and FRTB), increasingly data intensive business processes have led to a demand for easy data access and data exploration in many individual roles.

Combined with rapid developments in the data landscape with wide ranges of new information on offer and the maturing of enabling technologies such as NoSQL databases and cloud, this makes for a much more business-user enablement focused solution landscape. Data management has escaped its historic confines of back office and IT. Demands from user roles ranging from front office, portfolio management, product control, compliance to risk, combined with much nimbler and piecemeal deployment options have made data management primarily about business user enablement and productivity.

Register here for the bobsguide webinar, Mapping the opportunities for insight-driven data management, in association with Asset Control

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