The Future of Securities Markets: Data Management, Analytics, and AI at Scale

Table of Contents

1. Introduction
2. Challenges in Transitioning to an Analytic-Driven Environment
3. Overcoming the Challenge of Massive Scale Data
4. Solving the Problem of Fragmented and Siloed Data
5. Addressing Disjointed Development Teams
6. Introducing a Unified Data Management System
7. Standardizing Big Data Analytics with Data Bricks
8. Changing the Dynamics of the Development Process
9. Transitioning from Traditional Analytics to AI Analytics
10. Removing Obstacles and Lowering Costs in Data Science and Machine Learning
11. Conclusion

Introduction

In this article, we will discuss the challenges and solutions to transitioning to an analytic-driven and machine learning-oriented environment. We will explore the specific obstacles faced in the Financial Industry Regulatory Authority (FINRA) and how they overcame these challenges. The focus will be on the two major points that were addressed - the problems of massive scale data and fragmented development teams. By implementing a unified data management system and standardizing big data analytics with Data Bricks, FINRA was able to overcome these obstacles and achieve real business impact.

Challenges in Transitioning to an Analytic-Driven Environment

Transitioning to an analytic-driven environment comes with its own set of challenges. In the case of FINRA, the two significant points that posed challenges were the massive scale data and disjointed development teams. These challenges hindered the ability to leverage data-driven analytics and impeded the adoption of machine learning for business impact. In the following sections, we will Delve into each of these challenges and explore the solutions that FINRA implemented.

Overcoming the Challenge of Massive Scale Data

Dealing with massive scale data is a daunting task. With tens of billions to hundreds of billions of events per day, storing and analyzing such data requires robust infrastructure and efficient processes. FINRA addressed this challenge by implementing a data management system called "Heard." Heard is an open-source data management system developed by FINRA, which serves as a centralized data lake for all enterprise data. By consolidating the data in one place, FINRA eliminates the fragmented and siloed nature of the data. This significantly reduces the time spent searching for data and allows data scientists to immediately access and analyze the required data. Heard also provides access control and security mechanisms to protect sensitive data.

Solving the Problem of Fragmented and Siloed Data

In a data-intensive environment like FINRA, fragmented and siloed data can impede analytics efforts. Traditionally, assembling data for analytics purposes involved disparate processes and separate teams, resulting in siloed environments. FINRA's data management system, Heard, addresses this challenge by providing a unified data repository. With all the necessary data accessible through this repository, data scientists no longer need to spend valuable time figuring out where the data is or who to contact to obtain it. This eliminates the learning curve for new employees and enhances productivity. Additionally, FINRA has implemented various analytic and query tools that interface with Heard, catering to different application needs, from ETL to search and discovery, and facilitating machine learning efforts.

Addressing Disjointed Development Teams

In the realm of analytics, disjointed development teams can Create inefficiencies and hinder progress. Traditionally, the software development lifecycle was not well-suited for data science projects, leading to challenges in transitioning prototypes to scalable production systems. To overcome this challenge, FINRA standardized their big data analytics development process around the notebook environment with Data Bricks. This shift revolutionized the development lifecycle, eliminating the need to HAND off projects between different teams and reducing the cost of iteration. With one combined team working on a unified notebook, the development, testing, and documentation process becomes seamless. This collaborative approach enhances cross-domain expertise and streamlines the deployment process, resulting in faster delivery of analytics solutions.

Introducing a Unified Data Management System

The implementation of Heard, FINRA's unified data management system, has proven to be a game changer for the organization. By consolidating all enterprise data into a single data lake, Heard removes the obstacles of fragmented and siloed data. Data scientists can now focus on their Core expertise in data science, rather than spending time on data wrangling. The centralized catalog ensures easy access to the required data, significantly reducing the learning curve for new employees. As a result, the overall efficiency and productivity of the organization have increased.

Standardizing Big Data Analytics with Data Bricks

Standardizing big data analytics is crucial for streamlined development and efficient collaboration. FINRA has adopted Data Bricks as their unified analytics platform, enabling the development of modular and reusable software using Scala. Data Bricks provides a scalable and flexible framework for machine learning and analytics. With this standardized approach, FINRA has eliminated the drawbacks of traditional analytics, such as complex SQL queries and lack of modularity. The unified platform enhances collaboration and enables data scientists to focus on generating real business impact.

Changing the Dynamics of the Development Process

The transition to a notebook-Based development process using Data Bricks has brought about significant changes in the dynamics of development teams. Previously, different teams with varying expertise had to work together, often leading to miscommunication and delays. With the notebook environment, all aspects of the development process, including test cases, code, and documentation, are contained within a single notebook. This eliminates the need for handoffs and improves collaboration between different team members. The unified approach fosters close collaboration between domain experts and data scientists, resulting in more efficient development cycles and faster time to production.

Transitioning from Traditional Analytics to AI Analytics

FINRA's Journey has involved a transition from traditional analytics methods, primarily based on SQL queries, to AI analytics driven by machine learning. As the organization shifted to a unified data management system and standardized analytics platform, the focus shifted from manually writing complex SQL queries to developing modular software using Scala. This transition enabled the implementation of sophisticated machine learning models and frameworks. The shift to AI analytics has opened up new possibilities for exploring the data, identifying Patterns, and generating real-time insights with significant business impact.

Removing Obstacles and Lowering Costs in Data Science and Machine Learning

The adoption of unified data management and standardized analytics processes has removed numerous obstacles in data science and machine learning. Previously, the time and effort required to locate and wrangle data created friction and hindered exploration and Curiosity. With the centralized data lake and streamlined processes, the cost of curiosity has been significantly lowered. Data scientists can freely explore the data, follow hunches, and discover new relationships. This enhanced exploration, coupled with Simplified deployment processes, enables rapid development and deployment of data science solutions. The lowered costs and increased delivery velocities lead to tangible business impact.

Conclusion

The journey towards an analytic-driven and machine learning-oriented environment is not without its challenges. However, FINRA has successfully addressed the obstacles of massive scale data and disjointed development teams. By implementing a unified data management system and standardizing analytics with Data Bricks, FINRA has revolutionized their analytics capabilities. The centralized data lake, coupled with a notebook-based development process, has streamlined operations, enhanced collaboration, and lowered the cost of iteration. These efforts have resulted in increased productivity, faster delivery of analytics solutions, and real business impact.