Authored By
Isaac Alexander
Isaac supports Arcesium's flagship clients in his capacity as a Forward Deployed Solution Architect. Isaac’s focus is on delivering for clients and maximizing their positive experience on the Arcesium platform. He loves talking about sports, movies and food.
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In today's digital age, data has become the world's most valuable currency. Financial institutions of all sizes are grappling with a crucial question: How can we effectively leverage our vast data resources? For organizations seeking to harness the power of their data without building an in-house technology team, the answer lies in a feature-complete data platform, built upon a modern data lakehouse and equipped with tools including data ingestion, analytics, and reporting.
A data lakehouse is a cutting-edge, open data management architecture that combines the flexibility, cost-efficiency, and scalability of data lakes with the robust data management and transactional capabilities of data warehouses.1 This innovative approach enables business intelligence (BI) across all data types, empowering both technical and non-technical users to access and analyze data for informed investment decisions.
With the insights gleaned from this new architecture, firms can make strategic decisions on everything from market segment entry to equity investments. The first crucial step in unlocking the potential of this data is implementing a comprehensive permissions structure across the organization.
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Any data lakehouse requires a robust permission and governance structure to control end-user data access. Below are examples of why it is paramount to establish strong data governance to provide the most accurate data to your end users in the most efficient way possible:
A common approach to creating this governance infrastructure is implementing data policy controls, where user or group permissions are defined by a set of data policy filters. These filters govern the range of accessible data resources and can be managed from a centralized permissions application.
Key Components of Data Policy Filters:
As global firms increasingly focus on controlling data access methods, API functionality support has become crucial. API keys are typically issued on a per-user basis, with users added to specific authorization groups controlling API access and read/write capabilities. This structure limits dataset access and enhances security by preventing total data layer access for non-admin users.
Once you have established proper data access controls, the next step is to empower your users with tools to analyze and report on the data they now have access to.
Data lakehouses offer exceptional flexibility in reporting, supporting various BI tools due to their structured data format. Popular visualization tools like Google's Looker, Tableau, and Power BI can directly connect to datasets, enabling users to create customized reports and dashboards. Modern data platforms with embedded visualization tools may have out-of-the-box reports or flexible low-code custom reporting.
Looker, for instance, allows users to create views using its native LookML language, powering dashboards with multiple widgets drawing from various datasets. This flexibility creates powerful visualizations to meet diverse client needs.
While the learning curve for these BI tools can be steep, their popularity means abundant online resources are available for skills development, making them scalable solutions for growing teams.
For use cases where data is required outside the platform, data egress methods must be evaluated.
To support bespoke use cases and downstream solutions, data platforms natively support API integration. This approach requires a staging area for data transformation and the ability to load it into downstream applications. Leveraging best-in-class data warehouses like Snowflake or Databricks, where many firms have existing connectivity, streamlines this process. Further efficiencies can be found in advanced data platforms with native integration capabilities and pre-existing connectors to industry utilities.
APIs enable data transfer not only to data warehouses but also directly to databases, supporting the use of the platform as a single source of truth accessible by any application. The main challenge with this approach is the need for additional tools or warehouses to transform data into the required format before final loading.
For maximum efficiency, leveraging data systematically for downstream processes is ideal. This approach allows for data transformation and direct loading into downstream systems with a single hop, solving timing issues and eliminating the need for additional data tools.
For example, populating a risk system with near real-time, quality-controlled data provides immense value, allowing analysts and quants to derive investment insights using machine learning, Python, or other analytical methods. The primary challenge here lies in building the transformation layer to format data appropriately for downstream consumption.
As the finance industry realizes the value and importance of data and data platforms become standard, it’s imperative to enhance the data platform’s effectiveness in utilizing and distributing data organization-wide. Whether through reporting, APIs, or systematic data transfer, leveraging clean, quality-controlled data is essential for scaling operations and uncovering new opportunities.
By identifying optimal data utilization methods and implementing robust access controls, financial institutions can position themselves to capitalize on the data revolution, driving innovation and growth in the years to come.
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