In the United Kingdom, the banking and finance sector is an important driver of the economy, and data analytics has become a vital tool for this sector to stay competitive. The big data analytics in the banking market is estimated to grow at 23.11% CAGR by 2029.
Financial organisations utilise big data management solutions to deal with large and increasing data pools that generally contain hundreds of terabytes or even petabytes of data saved in multiple formats. Using this data helps them improve their services, take advantage of business opportunities, and streamline operations.
Big Data Management Common Challenges
Let’s delve into the challenges of managing big data within financial organisations. These are crucial to understand for ensuring efficient data handling and informed decision-making:
Data Silos
In financial institutions, data silos occur when information is isolated within different departments. Each department operates independently, leading to duplicate data and inefficient storage utilisation.
The challenge lies in breaking down these silos to enable seamless data sharing across the organisation. Integrated data flow enhances decision-making and reduces redundancy.
Growing Data Storage
The sheer volume of data generated in financial services can overwhelm traditional storage systems. Managing and storing massive datasets efficiently becomes a critical challenge.
As data accumulates, it can slow down systems, impacting performance and responsiveness. Organisations must adopt scalable storage solutions to accommodate this growth.
Data Complexity
Financial data arrives in various forms: structured, unstructured, and semi-structured. It includes transaction records, market data, customer interactions, and more.
Sorting and analysing this complex data, especially when it arrives in huge volumes, poses a significant challenge. Advanced analytics tools and algorithms are essential for making sense of it all.
Maintaining Data Quality
High data volume can compromise data quality. Inaccuracies, inconsistencies, and incomplete records may arise.
Data silos exacerbate this issue as synchronisation becomes difficult. Ensuring data accuracy and accessibility, data quality and resiliency is crucial for informed decision-making.
Shifting to a Data-Friendly Culture
Transitioning from manual decision-making to data-driven processes is a significant cultural shift.
Encouraging employees to embrace data analytics, adopt new technologies, and make decisions based on data insights is a long-term challenge. It requires training, leadership support, and persistence.
Benefits of Big Data Management in Banking
While there are challenges to integrating big data management, there are numerous advantages. Let's look at a few of them.
Improved Decision-Making
Big data analytics provides valuable insights into market trends and customer behaviour, allowing financial institutions to make informed decisions. By analysing vast amounts of financial data, they can predict future outcomes and adjust strategies accordingly.
Cost Reduction
Efficient big data management streamlines processes, leading to cost savings. By automating data collection, analysis, and reporting, financial organisations can reduce the risk of non-compliance and penalties. It also helps create comprehensive records of transactions, facilitating audits.
Enhanced Customer Experience
Big data enables personalised customer interactions. By analysing customer data, financial institutions can tailor services, offer relevant products, and improve overall customer satisfaction.
Operational Efficiency
Properly managed big data optimises operational processes. It allows for better resource allocation, risk assessment, and fraud detection, leading to smoother operations.
Regulatory Compliance
Big data assists in meeting regulatory requirements. It automates compliance-related tasks, ensuring accurate reporting and reducing compliance risks.
Competitive Advantage
Financial organisations that leverage big data gain an edge over competitors. Analytics derived from big data provide insights that help them stay ahead in the market.
Case Study: The Transformation and Integration of Financial Instruments with a large European financial institution
Discover how Finworks approach to big data is designed to handle the challenges of European financial institutions. Learn how our platform seamlessly manages the daily updates of 2 million dynamic data sets, and intricate calculations that meet the rigorous standards set by central banking authorities to elevate decision-making, risk management, and operational efficiency. Read here.
Finworks Big Data Analytics for Banks and Financial Institutions
Banking and financial institutions have always been data-driven entities, constantly processing vast amounts of data. The traditional methods of handling data are no longer sufficient, and banks are struggling to keep up with the pace of innovation.
Fortunately, there is a solution to these challenges. The Finworks Data Management Platform facilitates big data self-service preparation by including a range of features.
- Enables businesses to monitor progress, maintain control, and facilitate onboarding with ease.
- The ability to manage data silos, structured and unstructured data, and ingest from pre-existing Data Lakes or other repositories.
- Data discovery capabilities to identify the data formats, minimum or maximum max lengths, values, and date time ranges of data sets to be ingested, providing valuable insights for analysis.
- Workflow management that facilitates data validation, cleansing, and duplicate identification, ensuring high-quality data for use by the business. Bi-temporal capabilities are also supported, providing a complete picture of data changes over time.
- Scheduling capabilities that allow for data ingestion and analytics to be undertaken increase efficiency and reduce wait times.
- The ability to support the five governance pillars of Data Lake construction (security & accessibility, data quality, data resiliency, organisational agility, cost management & optimisation), ensuring compliance with regulations and maintaining data governance.
- Supporting full traceability and proxying of upstream systems, enabling Data Warehouse capabilities and improving data management.
- Built-in modelling capabilities facilitate data linkage, lineage and semantic understanding, making it easier to extract valuable insights.
- Supports complex and unpredictable queries, as users are not constrained by the design of any underlying database schema, allowing for greater flexibility.
- Provides enhanced query/response times on large datasets, enabling faster analysis and decision-making.
Its features make it easy to get started with big data analytics and pave the way for a data-driven future. Get in touch with us today to learn how to incorporate big data technologies into your business, and we'll discuss how to solve your current challenges.
