THOUGHT LEADERSHIP

Composable DataOps - Financial Services

How Composable DataOps Helps Financial Services Firms​

The financial service sector is the most data-intensive business sector. Because of that, it’s always been a frontrunner in adopting data science applications. As early as the 1990s, banks, advisories, wealth management funds and other financial services firms have been exploring ways to better extract business value from their data and make better predictions.

But for all the decades of effort, many firms still encounter serious problems when trying to turn their data into competitive advantage. Here are some recent statistics that speak to how difficult it is to operationalize data.

  • Research from the Financial Technologies Forum shows that nearly one third of financial services organization report mistakes from manual processes is their biggest data reconciliation pain point.
  • Data integrity is costing the average businesses between 15% – 25% of its revenue, according to the MIT Sloan school of business.
  • Nearly two thirds of financial services organizations say that investing in solutions that automate manual processes to be one of their top three goals.

Composable DataOps is a model for managing and governing data that provides financial services a complete set of tools and processes to consistently optimize the efficiency and ROI of their data science projects, while also helping them strengthen their regulatory compliance and data privacy protections.

Streamline Data Management for Advanced Analytics

Data hungry AI and machine learning models can consume an almost limitless amount of new information. To ensure those analytics provide actionable and accurate insights, you must provide those applications with a steady stream of up-to-date input data.

Automate the Data Integration Process The traditional models of extraction, transform, and load (ETL) is labor intensive and time-consuming, especially when working with data from a third-party system. Tedious scripts, manual ingestion, and data quality processes all drain data engineer hours, harming the efficiency of your analytics workflows.

In contrast, the Composable DataOps methodology enables your data engineers to develop low-code, fully automated data pipelines that weaves both basic and advanced ETL functions like concatenation, fuzzy matching, and parsing into reliable and repeatable workflows. Automation not only streamlines data profiling and cleansing processes, but it empowers your team to scale up data-intensive projects without hiring expensive, hard-to-find talent.

Stronger Cross-Team Collaboration and Self-Service Composable DataOps platforms centralize data workflows behind a visual, intuitive interface built on secure, composable microservices. This provides several benefits over legacy model.

On one hand, it democratizes data workflows, allowing data consumers without advanced programming skills to load analytics ready data from applications, Tableau, PowerBI, and proprietary applications with limited input from senior data engineers.

Additionally, the Composable DataOps platforms provide a single repository and platform for staff to collaborate and exchange ideas. This eliminates many of the historical barriers that have separated data stewards, data consumers, and business departments, and allowing stronger cross-departmental visibility and collaboration.

For example, 56% of banks say they’ve already implemented AI into their risk management procedures, while 52% are using machine learning for generating revenue from new products or services.

Master Data Management Success

According to PwC, financial firms create about 44 zettabytes of new data created every year. Those data volumes are already overwhelming, but as the amount of internal, third-party, and even fourth-party data continues to grow at an exponential rate, the challenges of locating authoritative datasets to power data science applications will become an even greater challenge.

Given this complexity, the importance of master data management (MDM) has become critical for financial firms that wish to undertake data science initiatives with confidence. A properly implemented MDM strategy allows your business to collect data from across heterogeneous on-premise, cloud, and IoT systems and build a unified, authoritative dataset that provides a true single source of truth (SSOT) for your entire firm.

Composable DataOps supports your MDM strategies by providing the ETL, querying, processing, and integration capabilities your team needs for MDM success across client, product, and other datasets.

  • Enterprise AI capabilities in Composable DataOps solutions allow you to process paper documents and extract the information you need with a high degree of accuracy and efficiency.
  • Data mining, text analytics, and natural language process create stronger automation and offload complex, adaptive data integration tasks that typically required human involvement.

According to the Harvard Business Review, bad data costs U.S. businesses 3 trillion dollars per year.

Achieve Regulatory Compliance Confidence

Regulatory compliance is an enormous hurdle for financial services firms that are trying to extract business value from data. Companies large and small are often managing not just one, but several regulations like at a time, each of which have unique requirements for how data your collect and use data.

Good data governance establishes norms for the ownership and access privileges, which are critical steps to achieving reliable regulatory compliance. But reliable data governance can be difficult to achieve, with unstructured data, heterogeneous technologies, and a lack of tools frustrating even the most well-intentioned efforts.

In addition to the automated data pipelines mentioned above, which themselves have important applications in achieving regulatory compliance, Composable DataOps provides other resources and tools that firms can use to develop a stronger, more proactive data governance program, including automated risk assessments and compliance checks for structured and unstructured data, and tools for eliminating data governance ambiguity and orchestrating the work of data stewards in each department.

Consistent and Scalable Metadata Cataloging The larger and more complex the data assets your firm holds, the more important it is that you are properly managing your metadata. By automating metadata management, and applying the appropriate machine learning algorithms to classification, and tagging tasks, Composable DataOps platforms allow even a small team to locate and manage sensitive data assets at scale.

Learn More about Composable DataOps

Composable Analytics is a pioneer of the Composable DataOps methodology, providing firms in the financial services industry, including banks, brokerages, insurance firms, and others with the industry’s leading platform for next-generation data management, analytics, and enterprise AI.