For most financial firms, integrating the vast amount of data created every day remains a costly and burdensome challenge.
Financial institutions and regulators have long realized that data quality is central to achieving complete transparency into markets and banking operations.
If analytics efforts are to provide the expected return on investment, corporate leadership needs to invest in the people, processes and technologies that empower decision support and automation.
Big data governance is agile, collaborative, and efficient. It engages, not separates, analysts in capturing their learnings to accelerate production readiness.
Going forward, the need to acquire, manage and use data more efficiently to drive profitability and mitigate risk will become a larger priority for financial institutions in Asia.
In order to realize the full potential of machine learning in finance, it’s critical in these early days to understand what the system is doing, how it’s learning, and what data it’s using.
An ECB survey found that banks spent 42 percent of their information technology budgets on outsourcing services last year.
For some firms, having a role committed to oversight can add real value. For others, a decentralized data management strategy might make more sense.
A growing number of organizations have introduced the role of Chief Data Officer, however the priorities for this new corporate officer aren’t as clear.
It is now widely recognized that to have effective controls over enterprise wide data, financial institutions must first gain control of data governance.