Organizations across the financial sector are opting for agility in business operations to induce resilience and accelerate innovation. Implementing agile technology is a key to the development of innovative solutions with an efficient time to market.
A lot of data in the financial sector is fragmented across silos. As a result, there is an underlying impedance mismatch for data like customer information, accounts, product details, etc. Products and their data are scattered, impacting the product and service lifecycle.
Despite the significant investments made in the data infrastructure, the ability of financial organizations to introduce agility into their daily operations is limited.
Therefore, an AI-centric approach is required, focusing on machine learning and advanced analytics to build the next-generation data-powered deliverables.
Here’re the strategic technologies supporting the transition from traditional data management to an agile approach in the financial sector.
- Agile data flows
Agile data flows are critical for letting the data flow smoothly through an organization. This also means a shift from batched and static data pipelines. Agile data flows rely on the quick adoption of tech innovation for ensuring increased data visibility. It also helps in the creation of value-based processes for meeting customer demands.
- Converged Analytics and Machine Learning
An analytical environment enables the finance sector to capitalize on existing market and industry trends.
The combined approach helps generate valuable business insights and achieve operational agility. Converged Analytics also supports qualitative research activities to create customer-centric financial services.
Converged analytics and ML assist with different processes involved in the stages of the big data lifecycle like retrieval, storage, and aggregation of data. The environment is conducive to driving data agility at all levels, including operations activities, quantitative research, and business analyst functions.
- High-Speed Data Movement
High-speed data movement supports the transition into hybrid data architecture from on-premises data lakes and data warehouses. High-speed data movement leverages the power of ML and analytic functions to process unstructured information.
- Cloud Agility with Hyperconverged Infrastructure (HCI)
This approach allows you to abstract the complexity across converged and hyperconverged infrastructure and focus on reducing the software usage.
This approach results in reduced complexities and higher productivity. Cloud agility is also important to create innovative systems while supporting core applications.
Leveraging Foundational Technologies in Financial Sector
Foundational technologies like Artificial Intelligence, ML, and analytical functions are giving businesses a competitive edge. These technologies are helping financial companies reduce the processing cycles and enabling production-ready data flows.
- Accuracy in credit decisions by the incorporation of advanced algorithms in real-time across large datasets
- Automated investment planning, portfolio construction, and wealth management activities
- Creating pipelines for ML training for ensuring auditable data models
- HPC (high-performance computing) data platform for calculating risks and creating profitable trading models
- Analysing customer interactions and optimizing customer journeys based on interaction data.
The focus should be aligning your financial data infrastructure with your organizational structure. A data-driven and AI-centric approach must be adopted to gain a real competitive advantage and generate higher revenue. As the industry moves away from hard-coded data management, the process-centric model won’t work anymore.
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