By the end of 2024, 75% of enterprises will drive a 5X increase in streaming data and analytics infrastructures, shifting from piloting to operationalizing Artificial Intelligence (AI), says Gartner. In today’s data-driven economy, data cleansing is essential for informed decision-making. Operational data management gets challenging day by day as volumes of data continue to enter the enterprise systems from multiple sources. This data related to products, clients, and accounts gets consumed by various business units. Enterprise operations data is also critical for regulatory reporting. In this blog, let’s look at the operational data management challenges faced by financial institutions and how they can leverage AI/ML capabilities to optimize costs and gain maximum business value.
Challenges in Operational Data Management
Traditionally, data analysts used statistical analysis methods for sorting and cleansing of enterprise data. The data entry process to semi-automated data management systems was manual and time-consuming. For more data, the enterprises subscribed to data feeds. Common business rules were applied to sort data and fix errors manually. And unavailability of data directed the data analysts to source data from multiple internal and external data feeds. For data validation, analysts looked into company reports, SLAs, etc. to extract information from historical records. The manual reviews also include four-eyes checks to ensure data is usable and ensure the data quality is high. With connected systems, massive data have been piling up. Today, managing and cleansing the data using traditional methods is turning out to be expensive, labor-intensive and time-consuming for enterprises. Following are some of the critical data cleansing challenges enterprises face during Operational Data Management:
  • Inconsistent Data: Data enters an enterprise system in various formats via multiple sources. For instance, there can be incomplete attributes received from the sources which need to be completed to fulfill the data-related tasks at another end. This incomplete data may be available somewhere in the system under a different attribute name. The lack of data consistency affects the successful utilization of the source data.
  • Manual Efforts: Cleansing volumes of data is labor-intensive, even with semi-automated processes. Enterprises need to bring in more data experts to understand the criticality of data and perform appropriate data quality assessments. A thorough data cleansing process manually is likely to become a never-ending process with millions of data entries entering the system with business continuity. Manual data management activities would become uneconomical for enterprises eventually.
  • Data Quality Errors: Human errors may exist in extracting and managing enormous data that can lead to data quality errors. Poor data quality may result in costly business decisions.
  • Impact on business: As analysts handle a high volume of data, there can be delays in the data maintenance efforts, which will significantly impact the business. For example, an important message or invoice can get delivered at the wrong customer address if the address field in the records is incorrect. Resending the message or invoice will be a cost to the business and a delayed communication can affect customer experience.

Increase operational efficiencies in data management using AI modeling

A typical data management environment consists of a platform, people, and process. Data related to these components are pulled from multiple data sources and passed through a funnel. The output at the end of the funnel is clean and up-to-date data to fulfill business objectives and regulatory compliance. By leveraging emerging technologies such as AI/ML, the level of operational efficiency is improved. Besides, to build operational efficiencies for a business, it is essential to identify the specific data management challenges of the business that AI/ML is established to solve.   Some of the AI-based data models that can be used for enhancing operational data management and governance are:
  • Python
  • Naïve Bayes algorithm
  • Logistic regression
  • Deep learning
  • Word embeddings
  • Pre-trained models
  • Labeled data

Key elements of AI/ML architecture for developing operational data use cases


Enterprises can design AI/ML architectures by investing in identifying use cases and working on prototypes. Below are certain key elements for developing operational data management use cases for enterprises:
  • Collect Data: Accumulate authoritative data from multiple sources. Validate it with internal entries. Natural language processing can be used for the extraction of semi-structured information. Private information or limited information are challenges that can be resolved by expanding the data sets by subscribing to data feeds such as S&P, Bloomberg, etc. and other reliable external sources. Financial institutions can cross-reference entity data to get a single view of the customer.
  • Manage Massive data sets: In operational data scenarios, AI/ML will be a continuous journey that depends on the problem the organization is trying to address. There is no such thing as minimum or maximum data when creating AI-based models. There could be millions of exceptions the experts may resolve over a period of time; however, the data will grow exponentially. The business value depends on the situations and the models that AI experts use to solve the problems.
  • Build Models / Frameworks: Every business use case is unique when it comes to solving data management challenges with AI/ML. Various statistical models are available to dissect and segment data. The choice of techniques depends on the scope of the problem. Data scientists will need some business context to develop solutions that can translate into excellent ROI over a period of time. Besides, enterprises can build and enrich hierarchies between clients, accounts, and products to identify the ultimate parent source for predicting AI-based data outcomes.
  • Define the need for AI in data cleansing: Enterprises need AI capabilities to make data understandable and compatible while ensuring data accuracy. Defining the problem will help AI experts to propose models for accurately fulfilling various data requirements across business units in an enterprise.
  • Simplify Data Cleansing: The AI-based data management prototype should simplify the data cleansing process with easy workflows, dashboards, and controls.

Benefits of AI/ML for Operational Data Management

  • Improve data quality: Automation of operational data processes minimizes human errors. Enterprises can harness AI/ML features to ensure flexibility to the operations team and make the automation very configurable. Besides, the features of AI/ML, such as reference sources, Fuzzy Logic, Co-occurrence Matrix, etc. can improve data accuracy. Data validation further helps in training AI/ML models.
  • Increase productivity: With AI-based automation, business users can focus more on strategic decision-making. Enterprises can increase the speed of extracting data from multiple sources for the operations team by assigning and validating attributes across entities. With AI/ML capabilities, analysts can validate data, review data, correct data, and submit it to populate the different fields in data collection formats automatically. Here, an enormous amount of time can be saved as no copy-pasting of information is required by users.
  • Reduce costs: The time and human effort to perform data-related tasks are minimized with automated data management. Maximize cost savings by minimizing penalties resulting from incorrect regulatory reporting.
  • Lower capital reserves: Reduce exposure to potentially lowering the capital reserve requirements.
  • Accelerate business growth: Accelerate business growth by leveraging and consuming clean data within the organization.

Xoriant adapts AI/ML for Operational Data Governance

At Xoriant CDi, experts have been developing AI/ML capabilities to solve operational data challenges by collaborating with product, technology, and data science teams. We have developed use cases by investigating the challenges faced by our operations teams. Our AI/ML experts have created prototypes that not only solved our internal challenges but also gave us an edge by pushing our capabilities to address our client requirements better. With well-developed processes, workflows, data ingestion, and verification guidelines, the Xoriant CDi team continues to work on new developing PoCs to enhance data cleansing models using AI and ML.  
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