The investment industry is faced with a relentless growth of the quantity and variety of data, presenting huge challenges to effectively source, manage and distribute data and analysis. Issues managing data are often due to legacy systems, storage infrastructures that cannot handle unstructured data efficiently or data silos within businesses hindering salability. Investment organizations are increasingly focused on pursuing digital alpha to provide their professionals with opportunity and risk discovery insights.
Trusted by more than 70 of the world’s top financial institutions, Bitvore has a wealth of experience in providing artificial intelligence (AI) and machine learning (ML) powered precision intelligence capabilities top firms need to counter risks and drive efficiencies. Below we have listed six key data and analytics trends we are witnessing our investment focused clients are working through, adopting or planning for the future.
- Cloud-Based Data Management will continue to be adopted for scalability, cost-effectiveness, and accessibility offered by the cloud. Movement to the cloud will be balanced with on-premises virtual clouds that may remain necessary due regulatory requirements.
- Investment management firms will increase adoption of data lakes; their scalability and efficiency will provide the basis for advanced data analytics, machine learning and artificial intelligence.
- Adoption of a broader range of data non-traditional sources of real-time data will increase as will investment management’s firms’ ability to process the data. Responding effectively to market changes and making informed investment decisions in real time will require evolving investment decision-making processes to meet the challenges of analyzing and storing the data.
- Investment management firms will increasingly adopt AI and ML to improve data analysis and drive investment decisions. Tools and processes are being adopted and designed to harness, processes and convert vast amounts of data into useful and readily accessible information to generate insights and power strategies. This will lead to more accurate and practical risk assessment and portfolio optimization. It is likely firms will adopt a hybrid approach to implementations by licensing AI offerings and developing in-house capabilities. Talent acquisition will remain a key challenge.
- Data Science will continue to advance rapidly due to the proliferation of open-source Python and SQL packages and the convergence of the two languages and other coding packages available to data scientists. More ML and AI capabilities will be required to keep up with ever increasing data volumes and the demand for shorter turnarounds.
- As the data within investment firms expands, they will need to enhance their data governance programs to ensure that data is adequately managed, governed and protected. Increased regulation is inevitable.