Data-driven asset managers are those that maximize the value of data and treat it as a strategic asset—using it for innovation and critical business decisions. These asset managers are characterized by a strong data foundation, cloud-based optimization and an insights-driven culture. And to identify and seize growth opportunities, they take proactive steps to embed AI, machine learning and advanced analytics at their core.
…but there are barriers that can limit the value from data.
Whether they’re transitioning their entire operating model to support investment accounting, or simply trying to keep pace with rapid change in the industry, asset managers know that immense potential value is locked up in their data. But many are struggling to unleash it.
Why? All too often, operational and cultural challenges are hampering their efforts to realize this value.
So, what’s holding your firm back?
Lack of an enterprise-wide data strategy, C-level sponsorship and the right workforce skills.
Poor data quality requiring tremendous time and effort to produce transparent, trusted and integrated data that is accessible at speed.
Fragmented data and a slow data supply chain due to legacy technologies and outdated governance practices.
believe that data management needs to be completely disrupted.
expect AI to deliver the next wave of cost reductions to the industry.
expect to see an increasing need for data science and technology development capabilities over the next five years.
It’s not enough to have the right tools and technologies. Firms should 'connect the dots' between insights and technologies, have a broader vision on how to apply them and ensure end-to-end integration.
Rethink the future of asset management. Today.
For asset management firms, the ability to use emerging technologies like AI, advanced analytics and machine learning at scale—along with harnessing new sources of data—is key to driving innovation, growth and increased efficiency.
It’s time to use these technologies to reimagine asset management
What is the power of AI?
The ability to combine multiple unstructured sources of external data to allow portfolio discovery and alpha generation.
In the age of advanced analytics
Firms can infer customers’ needs by analyzing their offline and digital footprint—while also improving sales and distribution.
Machine learning’s dual purpose
As well as automating and accelerating data-cleaning processes, machine learning can also help with performing modeling and predictive analytics.
Create a roadmap to unlock your data’s full potential
Every asset manager should move quickly to assess its data and technology capabilities and chart a course for transformation to become a data-driven enterprise. It is a crucial part of becoming more responsive to customers and to market opportunities, and to developing greater agility given the rapidly changing nature of technology and the marketplace.