Council Post: Why FSI enterprises should invest in Data Reliability Engineering
Financial Services and Insurance companies or FSIs are in the business of customer trust, which means there is a constant perception that needs to be conveyed that financial data/PII/ transactional data are safe and available at the time and manner the customer chooses. FSIs typically handle a lot of data, and there is a need to maintain a strong focus on compliance and risk management. Further, the sector is highly regulated. Therefore, FSI companies must also comply with regulations that require sufficient controls to isolate functions. Data accuracy and reliability are critical in the financial services sector. FSI companies need to run complex data pipelines with minimum rates of error. This is where Data Reliability Engineering (DRE) comes in. Data can break for millions of reasons, from operational issues to unforeseen code changes, since multiple stakeholders interact with the data in its lifecycle. Moreover, according to Gartner, poor data quality costs organisations USD 13 million annually. Hence, it is critical for FSI companies to look at things from a purview of DRE. Reliable data = Right Foundation + Right Processes Data Reliability Engineering encompasses a set of tools and processes that modern data teams use to solve data challenges. Data reliability engineers take care of the whole data infrastructure of the company, thus allowing the data team to focus on their jobs. Data reliability is critical for FSI companies because they need to inspire trust. These companies need to minimize risk and provide secure, reliable, responsive, resilient, and always available services. Data reliability for FSI companies gets an added layer of complexity. They are held to additional regulatory compliance requirements which mandate the segregation of responsibility—minimising and eliminating as much operational and financial risk as possible. FSI companies see millions of transactions on their platforms annually, resulting in a lot of data. Having foundations which enable DRE seamlessly, FSIs can utilize the data in a far more efficient manner, which will give them a competitive advantage and help them make important business decisions. However, in most cases, maintaining data processes is very manual. To address it, the companies should leverage ML-enabled platforms to handle data reliability, leading to better decisions, trust, and outcomes. By automating operations like failovers, backups, and resource provisions, FSI companies could ensure there are no catastrophic data losses. DRE guidelines The need to run complex data pipelines with minimum error rates has led to the rise of a new role: the Data Reliability Engineer. There are seven principles that data reliability engineers follow to ensure high data availability and quality through the entire data life cycle. First of all, data pipelines are bound to break for different reasons. Acknowledging it is the first step, and it helps the teams tackle the issue much more effectively. Secondly, data engineering teams use different tools to set standards for the different data pipelines. It helps provide the organisation with a better understanding of the data and how it should be implemented. A DRE-focused approach also removes repetitive manual processes, resulting in reduced overhead and fewer human errors. Unlike data scientists, data reliability engineers monitor the data at a much deeper level, thus leading to higher reliability. Further, DRE’s investment also brings in automation, which reduces manual mistakes, manpower, and time for tackling higher-order problems. Lastly, there’s a need to ensure processes for reviewing and releasing data pipeline code. A DRE-focused approach helps remove complexity to a great extent. Minimizing complexity leads to increased reliability. Benefits for FSI companies From the point-of-view of a FSI company, DRE would ensure high-quality data and minimum data downtime. This would go a long way in increasing the efficiency and improving the decision making of the organisation. How is this achieved? Based on the principles mentioned above, a DRE-focused approach would enable monitoring of performance and reliability of the firm’s data storage and analytics systems. FSI companies need to ensure that no single function has end-to-end responsibility for a single process which could compromise financial transactions or cause data loss. Looking at Data Reliability from this purview is critical. Furthermore, DRE focuses primarily on the upkeep of databases, data pipelines, deployments, and the availability of these systems. Having an enterprise-wide unified data platform would help remove these data silos and ensure accessing and processing of the data is seamless throughout the organisation. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
FSIs typically handle a lot of data, and there is a need to maintain a strong focus on compliance and risk management. Therefore, FSI companies must also comply with regulations that require sufficient controls to isolate functions as well as run complex data pipelines with minimum rates of error. This is where Data Reliability Engineering (DRE) comes in.
FSIs typically handle a lot of data, and there is a need to maintain a strong focus on compliance and risk management. Therefore, FSI companies must also comply with regulations that require sufficient controls to isolate functions as well as run complex data pipelines with minimum rates of error. This is where Data Reliability Engineering (DRE) comes in. They are held to additional regulatory compliance requirements which mandate the segregation of responsibility—minimising and eliminating as much operational and financial risk as possible.
["Financial Services"]
["data science", "Go", "AI", "ML", "RAG", "Financial Services", "Aim", "data engineering", "analytics", "Rust", "R"]
["AI", "ML", "data science", "analytics", "Aim", "RAG", "R", "Go", "Rust", "data engineering"]
Shoaib Mohammad is a founder and CEO at Lumiq, a data and analytics company that powers data-driven decision-making with AI and machine learning solutions for financial enterprises.