What are the current trends and challenges in data analytics and visualization for asset management?

1 Data integration and quality

One of the main challenges in data analytics and visualization for asset management is ensuring data integration and quality across multiple sources, systems, and formats. Asset managers need to access and analyze data from various internal and external sources, such as market data, portfolio data, risk data, performance data, client data, and regulatory data. However, these data may have different standards, formats, definitions, and levels of accuracy and completeness. Technical directors need to implement robust data governance and management practices, such as data quality checks, data cleansing, data mapping, data lineage, and data security, to ensure data integrity and consistency. They also need to use effective data integration tools and techniques, such as data warehousing, data lakes, data pipelines, and APIs, to enable seamless data flow and exchange among different systems and platforms.

2 Data analytics and modeling

Another challenge in data analytics and visualization for asset management is applying advanced data analytics and modeling techniques to generate actionable insights and recommendations. Asset managers need to use data analytics and modeling to support various functions and objectives, such as asset allocation, portfolio optimization, risk management, performance measurement, client reporting, and market research. However, these functions and objectives require different levels of complexity, granularity, and frequency of data analytics and modeling. Technical directors need to select and apply the appropriate data analytics and modeling methods and tools, such as descriptive, diagnostic, predictive, and prescriptive analytics, machine learning, artificial intelligence, and optimization algorithms, to address the specific needs and goals of each function and objective. They also need to ensure the validity, reliability, and scalability of the data analytics and modeling results, and to communicate them clearly and effectively to the relevant stakeholders.

3 Data visualization and storytelling

A third challenge in data analytics and visualization for asset management is creating engaging and informative data visualization and storytelling products and solutions. Asset managers need to use data visualization and storytelling to convey the data analytics and modeling results to their clients and other audiences, such as investors, regulators, and the public. However, these audiences have different levels of data literacy, preferences, and expectations, and may require different formats, channels, and devices to access and consume the data visualization and storytelling products and solutions. Technical directors need to design and develop data visualization and storytelling products and solutions that are tailored to the specific needs and interests of each audience, and that use the best practices and principles of data visualization and storytelling, such as choosing the right type of charts, graphs, dashboards, reports, or narratives, using appropriate colors, fonts, labels, legends, and annotations, applying interactivity, animation, and navigation features, and highlighting the key messages, insights, and recommendations.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?