The Benefits of Data Lakes for Financial Services
What is a data lake?
A data lake is a centralized repository of data that can be structured, unstructured, or semi-structured. Structured data is typically stored in relational databases, while unstructured data includes text documents, images, and videos. Semi-structured data falls somewhere between and includes data that may have a structure but is not necessarily organized in a traditional database.
Data lakes are often used by organizations that want to store all of their data in one central repository. This can be helpful for compliance purposes, performing forecasts, risk assessments, and understanding customer behavior. Additionally, data lakes can help organizations drive innovation by making it easier to test new hypotheses and explore new data sets.
The most common use case for data lakes involves storing large amounts of unstructured data. For example, an organization might collect customer tweets about a product or service. The tweets could then be analyzed to understand how the customers feel about their experience. For example, an organization might analyze social media posts to predict which products will sell well at a specific time.
What are the benefits of a data lake?
There are several benefits of using a data lake, including:
- Improved compliance: A data lake can help financial institutions meet compliance requirements by providing a central repository for all data. This can make tracking data easier and ensure it is being used appropriately.
- Cost efficiencies: A data lake can help financial institutions realize cost efficiencies by reducing the need to purchase and maintain multiple storage solutions. Additionally, a data lake can help organizations avoid the costs associated with data migration.
- Improved foresight: A data lake can help financial institutions improve their forecasting abilities by providing access to historical data sets. This can be helpful for trend analysis and identifying potential risks.
- Better customer understanding: A data lake can help financial institutions better understand customer behavior by providing access to customer data. This can be helpful for marketing and customer service purposes.
- Increased innovation: A data lake can help financial institutions drive innovation by making it easier to test new hypotheses and explore new data sets. Additionally, a data lake can help organizations keep up with the ever-changing needs of the financial services industry.
Why use a data lake for financial services?
There are several reasons why financial institutions choose to use a data lake, including:
- Compliance & Security: With our encryption, you can protect your most sensitive data and still have access to it when you need it. Our controls also allow you to track who is accessing the data and its use.
- Scalability: Amazon S3 data lakes allow any data to be stored at any scale, making it easy to meet varying data requirements.
- Agility: Perform ad-hoc and cost-effective analytics on a per-query basis without moving data from the data lake.
- Innovation: Aggregated and normalized data sets provide advanced analytics and machine learning foundation.
What are some data lake security best practices?
There are several data lake security best practices that financial institutions should keep in mind, including:
- Encrypting data at rest and in transit: Encrypting data can help protect it from unauthorized access. Financial institutions should consider encrypting data both at rest and in transit.
- Controlling access: Financial institutions should control access to their data lake using security groups and IAM roles. Additionally, they should consider implementing least privilege principles to restrict access further.
- Monitoring activity: Financial institutions should monitor activity in their data lake to look for unusual or unauthorized activity. Additionally, they should consider setting up alerts to notify them of any suspicious activity.
What are some data lake governance best practices?
There are several data lake governance best practices that financial institutions should keep in mind, including:
- Defining roles and responsibilities: Financial institutions should define roles and responsibilities for those working with the data lake. This can help ensure that everyone understands their roles and responsibilities and that the data lake is used appropriately.
- Creating a data management plan: Financial institutions should create a data management plan to define how data will be managed in the data lake. This can help ensure that data is properly organized and that it is being used appropriately.
- Implementing security and controls: Financial institutions should implement security and controls to protect the data in the data lake. This can help ensure that only authorized users have access to the data and that the data is being used appropriately.
Data lakes can help financial institutions improve their forecasting abilities, understand customer behavior, and drive innovation. Additionally, data lakes can help with compliance and security, scalability, and agility. Financial institutions should consider several data lake security and governance best practices when implementing a data lake.