Mastering Snowflake for Asset Management: A Step-by-Step Guide

Asset managers struggling with decentralized data and slow calculations is nothing new.

And obstacles and disruptions to quality data and calculations have consequences.

According to a report from SIX, almost half of asset managers view data velocity as the key challenge in enabling analytics to drive growth.

The report also shared a related comment:

“Given that asset managers invest in and distribute products from a broad array of fast-moving asset classes, access to near real-time market and reference data can make the difference between outperforming or underperforming the benchmark.”

And as mentioned above that asset management data struggles are nothing new, it doesn't mean that's how things always have to be.

Instead, turn those pain points into opportunities.

Below, learn how to optimize your investment performance analytics and decision-making using the Snowflake platform.

How to Overcome Data Challenges & Master Snowflake

Step 1: Understand the Problem

The Challenges of Decentralized Data and Slow Calculations

Data scattered across multiple systems can be incredibly time-consuming to consolidate, leading to errors.

Additionally, legacy systems struggle with complex attribution analysis, slowing down the process and delaying insights. Reporting often requires manual manipulation, lacking the flexibility for custom benchmarks. Without addressing these issues, businesses face increased operational costs, inefficiencies, and missed opportunities.

Step 2: Assess Your Current Situation

Evaluating Your Current Data Management and Performance Analytics

Consider these steps:

1. Conduct an audit of your current data management systems.
2. Identify redundancies and inefficiencies in data consolidation and reporting.
3. Evaluate the performance of your current analytical tools.

Use tools like Tableau and Excel for a comprehensive audit.

Step 3: Implement Effective Solutions

Steps to Implement Effective Investment Performance Analytics:

1. Choose Snowflake as your centralized data warehouse to integrate all data sources.‍
2. Design Snowflake-optimized data models to support accurate performance calculations
3. Develop custom attribution models and performance metrics using SQL or other preferred languages.‍
4. Build interactive dashboards for real-time insights using BI tools.

Ensure smooth implementation by conducting regular reviews and adjustments. Offer varied solutions for different business sizes or industries to maximize effectiveness.‍

Step 4: Optimize Performance

Enhance Efficiency in Performance Analytics

1. Implement automation to streamline data consolidation.
2. Regularly review and adjust attribution models to maintain accuracy.
3. Use Snowflake’s MPP architecture for fast query execution.

And consider tools like Alteryx for data automation and Tableau for visualization.

Step 5: Monitor and Maintain Setup

1. Set up automated alerts for performance anomalies.
2. Conduct regular reviews of your data models and performance metrics.
3. Update your BI tools and dashboards to reflect changes in data sources or performance criteria.

Don't forget to adopt industry best practices for data governance and quality assurance. This ongoing journey includes regular checkpoints and milestones to ensure continued success.

Become a Snowflake Pro

Optimizing your investment performance analytics is crucial for informed decision-making and operational efficiency.

And from assessing your current situation to implementing and optimizing solutions, each step is essential.

Following these steps, it will lead to improved data integration, faster calculations, and more flexible reporting.

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Ready to improve your performance analytics? Contact Empaxis to start your journey with Snowflake. Transform your investment performance analytics, transform your future.