Introduction

Alongside competitive compensation, career growth opportunities, and flexible work arrangements¹, a collaborative and innovative environment, continuous learning, and recognition also play a crucial role in attracting and retaining talent.

For technical and data hires, an investment firm's technology stack and infrastructure are undoubtedly at the forefront as enablers for successful talent attainment and retention. Tech and data hires with a remit to build, support, and develop data products want to work with modern architecture and applications available. However, many financial organizations cannot swiftly adopt or swap out their legacy, on-premises, server-based tools and technologies for the latest and greatest, compared with other industries. Several factors come into play, ranging from a lengthy evaluation process, senior buy-in, legal contracts, the sheer size of a potential implementation (for larger firms and systems), and budget.

How can investment organizations ensure they offer the best-in-class technology sought by top tech talent? By embracing modern technology, they can revolutionize their current architecture, define infrastructure and data framework goals, and identify which systems should be replaced or modernized. Newer, cloud-native technologies and features modernize a firm's infrastructure and empower technical and data talent, sparking optimism and excitement about the potential improvements in their work environment.

So, what constitutes a modern system that flexibly fits into an existing ecosystem of separate software and disconnected data, and how do they help attract technical and data professionals?

Here is a two-part checklist that technology leaders in the investment industry and beyond can reference to ensure that:

A. Their technology stack and infrastructure comprise comprehensive, modern, intuitive, and self-service components.

B. They promote a culture of experimentation, collaboration, and innovation to attract and retain technical and data talent.

Part A: Technology Stack and Infrastructure

Advanced, cloud-native data platforms integrate with numerous applications, vendors, counterparties, and databases. Centralizing data from disconnected systems, standardized to a consumable format, facilitates organization-wide access to cleansed and harmonized information.

Modern data systems offer several customizable features, from ingesting data to transforming it and building visualizations. This level of customization and control empowers investment firms and employees, making them feel confident and in control of their data processes.

As they research new technologies for modernizing existing infrastructure, technology leaders can look for the following features to evaluate whether the solutions they are considering can meet their current and future data needs:

1. Data ingestion, lineage, and catalog

An array of tools is now available for integrating data, curating a data catalog, and preserving lineage for each data point throughout its lifecycle.

Data ingestion: Robust data ingestion tools drastically reduce the time it takes to integrate data from new or existing sources.
Data lineage: Lineage tracks the journey of all changes administered on a data point from initial capture throughout its lifespan.
Data catalog: A data catalog serves as an index or dictionary of all-encompassed data, with a descriptor, to help data consumers quickly discover and locate the information they need and explore related data points.

Data ingestion, catalog, and lineage capabilities are core elements of a modern data framework and strategy. Low-code and no-code capabilities empower users to craft data pipelines to swiftly integrate new investment and market data sources from any system, source, or vendor. These fundamental functionalities, cataloging, and lineage deliver a comprehensive, self-service, and intuitive data platform with a metadata dictionary to support organization-wide functions and needs.

2. Data storage and management

As data volumes expand, so do an investment firm's data storage capacity needs. Data management platforms are purpose-built to handle exponential increases and variances in data sources, types, and formats.

Data storage: Flexible and scalable data storage options provide viability suitable for varying data needs and budgets. Data warehouses, lakes, and lake houses cater to different data objectives and sizes.
Data management: Capabilities for managing vast and disparate data formats are foundational for successfully collating, combining, and organizing all incoming data in the future.

Data storage and management underpin an organization's data strategy and are fundamental in meeting data goals and objectives. Open-capacity storage enables investment firms to scale as they connect with new data sources without compromising database performance or worrying about future data storage constraints. Comprehensive tools in a cohesive data platform allow data teams to manage and monitor data volumes, types, and access.

3. Data modeling and transformation

Data modeling tools enable firms to define data models and formats for storing data so that the consolidated data is standardized and conforms to required guidelines.

Data modeling: Some industry data platforms offer pre-built data models right out of the box. These quicken time to market by helping firms ingest and model vast amounts of data faster and make it immediately consumable.
Data transformation: Customizable transformation tools allow investment firms to realize a faster return on investment (ROI) with ready-to-use transformation rules, which can be further customized to format data for specific use cases.

Modern data platforms offer built-in tools for modeling and transforming data, empowering professionals to build models and create data transformation rules. This efficiency in data modeling and transformation makes the audience feel productive and efficient in their data-related tasks.

4. Data analysis and visualization

Business and technical users can develop independence through self-service analysis and visualization capabilities.

Data analysis: Anyone accessing the data to generate comprehensive and data-driven insights will benefit from analytics tools. Data exploration through filtering, relationships, and referencing the data catalog all enrich the data interaction experience for data teams.
Data visualization: Intuitive graphical user interfaces promote creativity in designing dynamic reporting dashboards by including visual elements such as tables, charts, and graphs in various charting formats.

Democratizing access to comingled, centralized, and standardized data instills confidence and trust in the data consumers. Users can extract value from data while benefiting from an interactive experience to produce visually rich reporting for business use cases. With the right tools, investment firms can empower talent to explore datasets, observe patterns, and derive meaningful insights to deliver data-driven decisions.

5. Data security and governance

The data integrity capabilities to look for include those supporting data governance mandates, such as data quality, data lineage, and, most importantly, data security.

Data security: Applying multi-level authentication and granular entitlements based on data sets, sources, and systems through advanced data security tools. Encryption methods, and authentication layers significantly enhances security and protects data.
Data governance: Robust data quality controls and data monitoring maintain data accuracy and integrity, which are crucial for data protection and compliance. data governance policies and practices enhance data trust and security organizationally.

Data security and governance controls and frameworks allow organizations to entrust access to users across multiple business areas with centralized data without compromising security, authorization, and client trust.

6. Collaboration and sharing

Data democracy fosters collaboration and teamwork. Sharing data and initiatives motivates individuals to work as a team towards common goals while developing positive working relationships and building each other up.

Data access: Democratizing access to data increases the willingness of talent to explore and spurs creativity. Making data accessible increases efficiency and productivity across business and technical teams and reduces bottlenecks by eliminating dependencies on key personnel.
Data documentation: Shared documentation encourages knowledge sharing. Comprehensive, up-to-date, and user-friendly documentation allows users to search and locate answers to data questions. Furthermore, collaborative tools like Atlassian's Confluence enable employees to create and edit documentation, making it shareable across teams and firmwide.

Part B: Experimentation and Improvement Culture

Now that we've covered the technology aspects for attracting top technical talent, we can discuss how to empower talent to explore, experiment, innovate, and create data products for the organization while flourishing as they further develop expertise in managing data.

To nurture a culture of experimentation and continuous improvement within data teams, tech leaders can implement the following strategies:

1. Encourage a growth mindset

Promote a culture that values learning, curiosity, and a willingness to try new approaches. Provide opportunities for employees to share what they have learned.
Publicly recognize and reward teams or individuals who successfully implement innovative solutions or drive continuous improvement.

2. Dedicate time for innovation

Allocate a portion of the data team's time (e.g., 10-20%) for experimentation, research, and exploration of new tools, techniques, or architectures.
Provide secure, isolated testing or "sandbox" environments where data teams and individuals can safely test new ideas without impacting production environments.

3. Foster collaborative problem-solving

Cultivate a communicative environment and knowledge-sharing. Encourage teams to collaborate on solving complex problems, drawing on their diverse perspectives and experiences.
Organize regular "hack days," "lunch and learn" events, or brainstorming sessions to encourage the cross-pollination of ideas.

4. Provide metrics and feedback loops

Define clear, data-driven success metrics for experimental projects and ongoing improvement efforts.
Review these initiatives' results regularly and solicit stakeholder feedback to guide future iterations. Establish a process for incorporating feedback and lessons learned into the team's workflows and decision-making.

5. Empower data talent autonomy

Give data and technical talent a high degree of autonomy in defining and executing their projects and improvement initiatives.
Provide the necessary resources, support, and sponsorship from leadership to enable these efforts.

Example data projects for tech talent

Encouraging data teams and talent to take on projects to build products, optimize processes, or achieve technical business objectives provides opportunities to develop and empower employees. Initiatives to enhance investment operations and data practices can help organizations keep up with technological changes and maintain competitiveness.

Automated data quality and monitoring

Implementing comprehensive monitoring for real-time visibility. Data and technical talent can experiment with building custom data quality rules either within or alongside cloud-based data platforms. Often, data platforms with built-in data quality frameworks offer a set of pre-canned data validation rules that users can customize further to fit specific needs. Alternatively, tech teams can create new rules.

Such initiatives facilitate automated detection and alerting on data anomalies. Proactively identifying and diagnosing data issues allows for resolution before potential impact on downstream processes. Organizations can reduce data-related incidents and improve decision-making based on higher-quality data.

Low-code, no-code data pipelines

Building data pipelines with low- and no-code platforms that require no developer resources and no coding experience allows for a faster time to market to integrate new datasets. Non-developer data users can use intuitive drag-and-drop interfaces to create and automate data integration workflows. Pre-built connectors to market data vendors, databases, and applications enable users to craft pipelines to facilitate swift ingestion of various data systems and file formats.

Organizations can lower costs by empowering internal teams to create integration pipelines without adding resources, enabling investment firms to streamline connectivity to new data sources. Automating data integration with modern, cloud-based data pipeline tools reduces data process latency inherent in traditional, on-premises data integration methods.

Dynamic performance and look-through reporting

Dispersed data across disconnected systems and clunky tools can hinder investment data analyses. When data across portfolios and investment strategies is harmonized and available to business users through a unified system like a data platform, generating comprehensive reporting for use cases, including performance, risk management, and look-through, is attainable.

By leveraging embedded self-service business intelligence (BI) tools, data teams and analysts can filter and utilize the underlying data to develop dynamic reporting dashboards. Investors require more transparency in their portfolios and underlying investments. Comparing look-through earnings reporting across different investment strategies can be challenging, but BI tools enable transparency into the underlying companies' performance within a fund. BI tools offer visual charting and graphical elements that business and data users can add to create and enrich look-through reporting.

Different investment strategies have different objectives, which can impact look-through analysis. Data platforms with advanced reporting capabilities can simplify complex performance reporting across multiple funds and investment strategies by empowering analysts with intuitive tools and access to data to generate dynamic, visually rich dashboards swiftly producing reports for various stakeholder requirements.

Conclusion

Technology leaders who prioritize modernizing their tech stack and adopting adaptable and resilient cloud-native infrastructure enable their organizations to keep pace with changing business requirements and emerging business needs.

Providing a comprehensive, flexible, and scalable ecosystem empowers data and technical professionals to be effective and productive. Implementing strategies to encourage learning and cultivate growth and development enables tech leaders to foster a culture of experimentation, risk-taking, and continuous improvement within their data teams, ultimately driving greater agility, innovation, and business value.

Ready to change the way you see your data? Learn how Arcesium's Data Platform, Aquata^TM, can modernize your tech stack, empower data and technical talent, and help your firm make critical decisions from a synchronized data source.

Sources:

Mutual Funds 2030 Rising Expectations, PWC, November 2023