With the advent of big data, data warehouse has become an indispensable infrastructure for enterprises to manage and analyze data. For investment institutions, building a high-performance data warehouse is crucial to support data-driven investment strategies and ensure competitive advantages. However, how to maximize return on investment (ROI) on data warehousing remains a major concern. This article will analyze the key factors affecting data warehouse ROI from aspects of technology selection, system design, data governance and talent building.
Choose Cloud-Native Technologies for Data Elasticity
Unlike traditional data warehouses built on expensive hardware, cloud-native data warehouses run on public clouds and utilize virtually unlimited storage and computing resources in a cost-efficient pay-as-you-go manner. For investment scenarios where data volume and analytics workload tend to fluctuate drastically, a cloud-native solution like Snowflake enables elastic adjustment of resources to optimize ROI. However, to avoid vendor lock-in, the data warehouse should support multi-cloud deployment and standardized open APIs.
Separate Storage and Compute for Flexible Scaling
The disaggregation of storage and compute is a defining architectural advantage of cloud-native data warehouses. Instead of scaling them together, independent resource adjustment prevents over-provisioning and wasted expenditure. This is particularly beneficial for investment data analytics where computation-intensive workloads like quantitative modeling and risk analysis can be conducted on temporarily boosted resources.
Implement Effective Data Life Cycle Management
A key to maximizing long-term ROI is to enforce proper data life cycle management. Hot data should be placed on high-performance storage, warm data moved to cheaper object storage, and cold data archived. Data should also be systematically deleted based on business needs. These practices reduce cost by storing different data based on access frequency and priority.
Cultivate Data and Analytics Talent
The success of an investment data warehouse hinges on specialized data management and analytics talent. Data engineers who can optimize system performance and data analysts who can translate insights into investment strategies are crucial.Companies should invest in recruiting talents, as well as provide sufficient learning opportunities and resources for them to stay competitive.
In summary, maximizing ROI of investment data warehousing requires adopting cloud-native technologies, separating storage and compute, enforcing data life cycle management, and cultivating data talent. With the right architecture, operations and expertise, data can truly become a valuable asset that empowers smart investment.