The Reliance on Data Lakes in the Wealthtech sector continues to witness an impressive surge

Data warehouses have been used for a long time now by organizations across the globe for the fulfilment of their storage needs. However, the reliance on data lakes for the same purpose is increasing at an impressive pace. This piece intends to make its users familiarize with data lakes, their capabilities, and their widespread adoption by a massive number of WealthTech firms. So, read on! 

Data Lake: An Overview 

Data lake is a storage repository capable of storing, processing, and analyzing huge volumes of unstructured, semi-structured, and structured data in its native format. Unlike data warehouses that require data to be processed and structured before ingestion, data lakes do not require any data transformation or upfront schema design prior to the ingestion phase. Data lakes are also highly cost-effective when compared with their warehouse counterparts. The ability of data lakes to store historical data helps firms in analyzing defects, trends, and patterns over time. 

Future trends and outcomes can easily be anticipated with the help of a data lake owing to its ability to support predictive modeling and forecasting. Data management processes are streamlined by data lakes to reduce the time and effort needed to manually ingest, process, and analyze data. Users can easily and effortlessly create their own reports, dashboards, and visualizations with the help of a data lake without relying on IT or data engineering teams at all. 

The accuracy and consistency of data is ensured by the data lakes by supporting data profiling, cleansing, and validation. The value of the data is significantly enriched by the data lakes through additional attributes and metadata. The insights offered by the data lakes pertaining to the inventory levels, demand forecasts, and supplier performance help in optimizing the entire supply chain. Customer sentiment can easily be identified through the data lakes by analyzing the data from reviews, surveys, and social media platforms. 

Data pertaining to workforce productivity, asset performance, and operational efficiency is analyzed by the data lakes. This analysis helps organizations in optimizing resource allocation and utilization. Data lakes help WealthTech players in generating personalized products and services recommendations based on customer preferences, purchase history, and browsing behavior. Data lakes help in improving the customer experience and identifying opportunities by facilitating the mapping and analysis of the customer journey across several touchpoints and channels. 

Data Lake Architecture 

A data lake features the following layers: 

Storage Layer: This layer is responsible for storing raw data. Hadoop Distributed File System (HDFS), Amazon Simple Storage Service (S3), and Azure Data Lake Storage (ADLS) are the most scalable and cost-effective data storage solutions that are currently available in the market. 

Ingestion Layer: This layer is responsible for ingesting data from multiple sources into the data lake. Apache Kafka and Apache NiFi are the most widely used technologies for data ingestion.  

Processing Layer: This layer is responsible for data transformation, cleansing, and enrichment. Batch and stream processing of data is usually carried out through Apache Spark, Apache Flink, or Apache Beam. 

Query and Analytics Layer: This layer is responsible for facilitating the interaction between the users and the data lake. The same layer also involves data analysis and running ad-hoc queries. Apache Hive, Apache Impala, are Presto are the key technologies associated with this layer. 

Benefits of Data Lakes for the WealthTech Sector 

The key benefits offered by data lakes to WealthTech players are listed below: 

Scalability: WealthTech firms need to deal with huge volumes of financial data acquired from several sources including but not limited to market data feeds, financial interactions, and regulatory filings. Therefore, owing to the ability of data lake to easily accommodate such volume of data, such firms use data lakes without thinking twice. 

Flexibility: WealthTech firms use data lakes to store financial data like trading records and social media feeds in diverse formats. This flexibility is not offered by the conventional data warehouses. 

Data Integration: WealthTech organizations need to integrate data from several sources like internal systems, external data providers, and regulatory agencies. Therefore, a centralized repository like data lake is used for ingestion, storage, and integration. Consequently, data lake offers a holistic view of the clients, investments, portfolios, and market trends. 

Regulatory Compliance: WealthTech firms always operate in heavily regulated markets and therefore, staying compliant with all the regulatory requirements is always essential. Data lake features data governance, metadata management, and audit trails to track the source of data, ensure data quality, and stay compliant. 

Innovation: Data lake encourages innovation by enabling the WealthTech firms to experiment with new data sources and investment strategies. This experimentation offers a competitive edge to the WealthTech players. The product development cycle witnesses a significant acceleration owing to the same innovation opportunities offered by the data lakes.  

WealthTech players use advanced analytics and machine learning algorithms on the data stored in data lakes to optimize investment portfolios, identify opportunities, and reduce risks. Therefore, maximizing returns on investments for the clients becomes easy. The analysis of transaction histories, social media activities, and the demographic data from the data stored in a data lake helps WealthTech firms to acquire rich insights pertaining to the clients’ behavior and preferences. 

The emerging trends and the shifts in sentiment are easily identified by monitoring data lake in real-time.  

Hexaview Technologies is a digital transformation organization engaged in offering data lake solutions to WealthTech clients across the globe for over a decade now. Hexaview is equipped with a team of experienced developers who have been an integral part of an impressive number of data lake creation projects. 

Hexaview recently helped a US-based fintech giant by integrating multiple data sources using AWS services to create a data lake. A standardized structure helped the client in effortlessly managing and organizing data and extracting the most relevant insights from the same. Data architecture employed in the project is showcased below 

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