Breakthrough Data Architecture: 6 Reasons to Love Snowflake’s Unique Approach to Data Warehousing
10 May 2024
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Published by
BluePi
Data-Driven Business Transformation
Unleashing the Power of Unstructured Data: BluePi's Data Modernization Solutions for Personalized Banking
Introductory paragraph: Storage and analytics
Today, in the age of data-driven banking, the financial industry is dormant—it has unmined, structured, and semi-structured data.
Although structured data, which includes records of transactions and information regarding deposit accounts, has long been the source of power for banks, the key to leverage lies in combining the two sets of data to improve the bank’s customer operations.
From this data, banks can reap the benefits of catering to different customers, enriching customer engagement, and making data-driven decisions. Nevertheless, the institutions of finance can find themselves facing the intricacies of unstructured data, and this act can be as formidable as either a heroine or an antagonist.
BluePi’s data modernization platform is a source of solutions that can be utilized by banks to unveil the hidden value within data and maintain their positions in the rapidly changing business space.
Unstructured and semi-structured knowledge are important in banking.
Unstructured data is information that has no predefined data model or is not organized in the row-column fashion simply expected of regular databases. The banking industry may deal with customer emails, call transcripts, social media interactions, multimedia files and others as unstructured data. In contrast to the other cases, semi-structured data does not obviously follow a very strict and tabular way of organization, but on the other hand, it does somehow contain some organization parameters, like XML files, JSON documents, and some sort of log files.
In the same manner, structured data has been the essence of banking operations, unstructured and semi-structured data are viewed by experts as the current and future reservoir of crucial information and knowledge. Utilizing data analytics and integrating advanced technologies of AI/ML and big data will allow banks to receive meaningful insights from the data and then deliver individualized experiences, improve risk management, and drive operational excellence
A Admiration on Unstructured Data for Personalize Banking
Better Customer Experience The raw data sources of unorganized data, like the emails of the customers, social media communications and call center transcriptions, contain the information of customers” thoughts and feedback. Banks provide a multitude of channels for communication with customers, such as web pages, social media platforms, customer service chatbots, and mobile applications. By analyzing this data using natural language processing (NLP) and text analytics techniques, banks can gain a deeper understanding of their customers’ needs and tailor their products and services accordingly. This feature can bring about a real breakthrough in the way customers may think about supportive services, thus increasing their satisfaction with offered services, fostering loyalty, and retention.
Fraud Detection and Risk Management Unstructured data sources, such as transaction descriptions, news articles, and social media posts, are beneficial in identifying the often ingenious methods devised by unscrupulous persons to commit fraud, launder money, and perpetrate other financial crimes. By integrating the incoming data from different sources with structured data, banks can create powerful risk managing strategies and anticipate dangers before they appear.
Sentiment Analysis and Market Intelligence: The vast amount of unorganized information available on web pages, news articles, and discussion forums can substantially contribute to a better understanding of the views of the public towards the brands, products, and services of a particular banking institution. Using this data, banks can track customer contentment levels, figure out what is the current trend in their industry and therefore, adjust their marketing methods accordingly.
BluePi's Data Modernization Solutions
BluePi will offer data modernization solutions, especially focused on helping banks process the unstructured and semi-structured movement, which will support them in the provision of personalized experiences, enhancement of risk management, and driving of operational excellence. Here’s how BluePi can help:Here’s how BluePi can help:
Data integration and harmonization of their diverse branches with the silos of data sources is perhaps the biggest challenge banks are facing at the moment. Banks can consolidate data sources from different entities from various sources that are structured, unstructured, or semi-structured, and BluePi acts as a data integrator and harmonizer. Under this, a comprehensive customer information view is provided. This is important because it helps create a uniform view of the interactions and experiences between the customer and the business.
AI/ML/NLP with know-How as a crucial component of its technology stack, banks receive the ability to recognize and process valuable information from unstructured data sources. One of their solutions is a checking account fraud prevention engine that uses sentiment analysis, auditing, peer analysis, predictive modeling, and personalized recommendation engines. The engine uses financial intuition to deliver personalized experiences and detect possible risks.
Real-time personalization from BluePi The typical process of data modernization in banks now uses real-time data processing and analytics to build customer personal experiences in mobile apps, online portals and other channels. Such strategies involve user’s personalized e-commerce recommendations, advertising campaigns designed for each customer, and intuitive financial advice taking into account each user’s unique needs and tastes.
Data Analytics Advantages for Leaders and Decision-makers The data modernization solutions, inclusive of BluePi, help bring the best out of unstructured data and into structured form that leaders and decision-makers can use to make informed choices and provide proper leadership. That entails dashboards in real-time, predictive modeling, and “what-if” scenario analysis, which allows them to make data-driven decisions with strategic initiatives.
Deployment Options: On Site, In-House, Private, Cloud, and Hybrid Implementations
Banking tends to rely on sensitive data and privacy, therefore, BluePi is aware that its security and compliance are the most important things. To address these concerns, BluePi offers flexible deployment options for their data modernization solutions:To address these concerns, BluePi offers flexible deployment options for their data modernization solutions:
On-Premises Deployment While the on-cloud deployment option is popular, BluePi also provides on-premises infrastructure deployment for banks which require rigorous regulation or stringent security parameters. This gives banks complete control over their data, while at the same time enjoying the current competence in data technology that comes with BluePi.
Private cloud deployment BluePi’s private clouds render secure and scalable environments suitable for banks thinking of data storage and application hosting. In this model, organizations can enjoy the same benefits that cloud computing offers, such as elasticity and cost-effectiveness, but also strict data privacy and security controls.
Hybrid Solutions: Through hybrid solutions, BluePi ensures that banks looking to take advantage of the merits of both on-premise and cloud deployments do not get left behind. Through mopping up the data, banks are able to keep valuable, sensitive information on-premises while being able to utilize the scalability and flexibility of the cloud for non-sensitive data and tasks.
None of the deployment modes will in any way lower BluePi’s standards for data security and compliance. However, all the data management features and tools used by this company, ensure that banks have the leverage to effectively and prudently analyze their unstructured data while adhering to industry regulations and best practices.
Conclusion
In the landscape of large scale data, unstructured and partially structured data represent a great but, at the same time, untapped footstool for banking institutions to use. Rather than data just being collected, banks can use it by knowing the client more personally, handling risk management more correctly, and running operational activities more effectively. Nevertheless, managing the inappropriate on account of raw data is the task that shall be acquired by well-qualified professionals and data management solutions for the process.
BluePi data modernization solutions are based on the idea that banks can get the benefits of keeping structured data but with more obvious advantages because they can combine data from multiple sources and work with advanced artificial intelligence and/or machine learning technologies that enable them to create real-time personalized products. Regarding the BluePi solution, banks have the flexibility of deploying on-premises, private cloud, or hybrid solutions to maintain rule compliance and adhere to stringent bank security protocols.
The data-driven transformation of the financial institution can be successfully done by coupling with BluePi. It enables the ability to stay ahead in an expanding field of competition and the provision of a wonderful experience for customers.
About the Author
Published by
BluePi
Data-Driven Business Transformation
Published by
Divya Dass
A data-driven solutions architect, leverages his expertise in data science, data lake management, data warehousing, and cloud CDPs to lead impactful data projects across diverse domains. A skilled communicator and collaborator, Divya translates data insights into actionable business strategies, continuously evolving and optimizing data-driven operations within the company.
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