Financial Services companies must be data-driven in order to provide customers with personalized products and services. This requires companies to access, ingest, and connect existing data in a centralized system or data lake. Companies can then train an artificial intelligent (AI) model to accurately interpret masses of data and produce an outcome that will guide business decisions and deliver superior experiences.
However, collecting, ingesting, and amalgamating data is challenging because the current repositories used by financial services companies to store data are not always easily accessible. In some cases, despite an increase in the number of tools and technologies designed to ease the collection and storage of important information, many financial services companies are still unsure how best to utilize data. A 2019 study by IHS Markit of asset management firms in North America and Europe found over two-thirds of respondents had limited or no visibility of where data came from, who touched it, how it was altered, and how it should be used.
To add complexity to this issue, not all data is the same; there’s big data, and in defining big data, it’s important to understand the mix of unstructured, structured, and multi-structured data that comprises the volume of information. Unstructured data refers to information that is not easily interpreted by databases or data models, while structured data refers to pre-defined data models that are easy to search. Key examples of unstructured data include email messages, text files, and audio files, and structured data incudes addresses and phone numbers, for example. Multi-structured data refers to data formats and types derived from interactions between people and machines, such as web applications or social networks—for instance, search engine history log data, including a combination of text and visual images along with structured data.
Some financial applications and systems can only accept certain data sources and types. It is one of the reasons why nearly 90 percent of financial services companies struggle being data-driven, despite nearly 92 percent of them expressing an urgency to invest in big data and artificial intelligence (AI).
To tie data sources together, financial services executives must expand data management infrastructures and invest in a data warehouse or a data lake, which forms part of a data management platform. In relation to operating as a data lake or a data warehouse, the data management platform also acts as a data processing mechanism designed to consolidate, process, and analyze data. To make the data processing mechanism more time and cost efficient, AI and advanced analytics can help automate the collection of crucial data, such as electronic banking records, portfolio analysis reports, and more.
The future of financial services relies on applying data capturing, data analysis, and data ingestion to a centralized system. This can be achieved by the amalgamation of data-driven insights and leveraging them for more personalized customer services and decision making.
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