If you were to ask asset managers if their company is truly data-driven, enabling them to turn their data into an asset to gain a competitive advantage, only a few will say yes.
According to an Accenture study, 66% of investment managers say that data management within their company needs to be improved.
Turning data into a long-term competitive advantage requires focusing on two aspects concerning companies and their use of technology.
Establishing a data management program is key to turning data into an asset, achieving business goals, and successfully completing complex projects. This data management program should include:
An effective data governance program establishes reliable and certified data for all business users, regardless of their department. It also sets standards for data transparency, data protection, and audit trail integrity.
Data governance is defined by an organization–process–technology triptych. The organization requires the establishment of a data committee and the assignment of roles and responsibilities, for example identifying data owners who manage a defined perimeter of data. Processes define the complete end-to-end data lifecycle, from initial data capture to the delivery of reporting and/or analytical views where the data has been normalized to meet the needs of the business users and data consistency across the company. Technology underpins the organization and processes.
Centralized and shared data in a Master Data Repository increases efficiency in terms of data acquisition, validation, and enrichment.
Ultimately, all sources are standardized and consolidated into a single standard model that meets all data users' requirements.
Across all industries, data scientists spend more than 80% of their time preparing and cleaning data to make it usable for analysis purposes.
Implementing a data quality validation approach is necessary across the data ecosystem. A comprehensive set of validations can provide end-to-end guarantees.
In data quality, the goal is data veracity. "Veracity" refers to the state of data quality in terms of what is fit for purpose and ready for the data consumer.
To stay competitive and reduce costs, companies must establish a modern technology platform with scalable data integration technologies, including the use of the cloud.
Cloud-based data drives business and technology transformation for an investment manager in several ways:
In this case, "modernization" means accelerating the ability to capture, store and publish data for operational and analytical reporting purposes.
Cloud-based CRM solutions can increase efficiency and effectiveness, help firms to better manage customer relationships, and create a central repository of customer relationships and contacts.
Cloud-based shared services accessed on a pay-per-use basis can help reduce costs and facilitate more effective enterprise data transformation.
The scalability of on-demand cloud-based solutions makes them ideally suited for complex risk calculations.
In asset management, we know that a supply chain requires a coordinated effort to search and run data through key enterprise systems.
Establishing a supply chain-focused database helps companies move forward with confidence and quickly ingest external data sources to proactively propose new use cases.
An investment manager's ability to make large-scale use of emerging technologies is critical to increasing efficiency and driving growth.
Few firms are truly leveraging analytics to the full. According to a Forrester cross-industry study, between 60 and 73 percent of all data in an organization is not used for Reporting and Analytics.
And yet, analytics technologies are revolutionizing the way asset managers conduct research and evaluate opportunities.
Investment management software allows investment managers to access previously inaccessible or unreadable datasets, helping them inform their analytics and research platform to experiment and validate investment ideas.
Machine Learning can help create highly contextualized customer experiences. Machine Learning combined with new customer analytics records can result in predictive models for precision targeting, including not only acquisition, but also cross-selling, retention, and repurchase risk.
AI can improve decision-making processes for portfolio management, by automating the identification of buy/sell opportunities as well as the comprehensiveness of order information in the order management solution (OMS), based on criteria that comply with the asset investment strategy.
Our investment management software will enable you to automate data quality control (presence, uniqueness, consistency, and compliance) upstream of reporting using customized workflows.