Guide to Data Lakes

A data lake is a central repository of large volumes of data that are stored in its original form. Most of that information is raw and unprocessed. Examples include:

• Social media posts and reactions
• Images
• Sensor data
• Log files
• Financial data
• Physician’s notes
• IoT data and all kinds of text data in documents, emails, and product reviews

Data lakes can store structured, unstructured, and semi-structured data. The data can then be processed (i.e., cleaned, organized, and transformed) with AI and machine learning for data analytics and insights that your entire company can use for a competitive advantage. In fact, data-leading companies experience a whopping 89% improvement in customer acquisition and retention.

Data lakes can make data management easier. Experts estimate that unstructured data makes up 80 to 90% of all data , meaning organizations that cannot process and analyze it aren’t getting the full picture of their business. Data lakes provide an affordable, agile environment to store all this information without having to process and structure it first – saving time and money.

Offering convenient storage, scalability, and cost-efficiency, data lakes help businesses realize the full potential of datasets in numerous ways.

Data lakes store a range of raw, unprocessed data in one central place. Having all that data in one place saves organizations the time and challenge of performing complex data transformations or organizing data into predefined schemas. Essentially, data storage is convenient and accessible for analytics.

Data lakes bring together data from multiple sources, both internal – such as your CRM or ERP systems – and external, such as websites and social media. Unifying all this information in one location—breaks down data silos that prevent companies from getting holistic views of their business health and a full understanding of customers.

Data lakes let you build AI initiatives on a vast and diverse foundation. That foundation is ideal for training AI and machine learning models to personalize customer experiences, make predictions, inform decision-making, and offer real-time recommendations.

Data lakes can store structured, semi-structured, and unstructured data without extensive data transformation or schema changes. This flexibility eliminates the need for costly pre-processing. Data lakes can even provide data lineage (i.e., lifecycle of data), metadata management, and access controls that lower the risks and costs of governance challenges.

Without clear governance and organization, a data lake can turn into a "data swamp" where records are disorganized and difficult to read, which can create compliance issues.

Scaling up your storage infrastructure or analyzing messy datasets can lead to unexpected costs or performance issues, too, which can slow down your operations.

Another common issue is that raw records often come with inconsistencies or errors, making analytics a bit challenging.

Addressing these hurdles requires a strong architecture, a thoughtful strategy for governance, metadata management, and the right tools to turn raw data into useful insights.

Businesses can use the raw pool of data for many purposes. Let’s explore some common use cases.

Data lakes provide a central repository for diverse datasets. With these deep wells of data, you can perform analysis and explore previously siloed records to uncover trends, refine operations, and drive innovation.

• Customer insights: By combining web activity, social media engagement, and purchase history, marketing and sales teams can identify patterns in customer behavior and create more targeted campaigns.
• Operational efficiency: Supply chain managers can analyze shipment records alongside warehouse logs to detect bottlenecks and forecast future demand with greater accuracy.
• Innovation: Product teams can analyze product quality and sales trends to make decisions about new, innovative offerings to their customers.

AI and agentic AI thrive on diverse and significant volumes of information, and because data lakes can store unstructured and semi-structured data, they can be used to train, deploy, and manage AI models.

• Predictive modeling: AI can analyze historical sales data alongside customer demographics to predict which products are likely to perform well in specific regions or seasons.
• Customer engagement: Service teams can integrate data from customer chat logs, email inquiries, and loyalty programs to train AI models that deliver personalized resolutions or recommend proactive support.

Data lakes help you make decisions grounded in a deep understanding of your business. You can use tools to search, filter, and visualize data stored in the lake to make informed decisions about when to launch a new product, where to cut costs, or how to optimize inventory levels. You can also pinpoint anomalies and get ahead of emerging trends in real time by analyzing data continuously as it flows into the lake.

• Customer churn prediction (AI models): A telecommunications business can integrate customer information, call logs, billing details, and social media interactions from data lakes. Then, using machine learning techniques, it can train an AI model to identify factors that contribute to customer churn and make real-time predictions to reduce churn.
• Patient treatment (Decision-making): Healthcare organizations can store many types of datasets in a data lake, including records, images, and even research papers. Providers can then use predictive modeling to inform patient care.

Below are two basic elements of a data lake architecture.

Data ingestion is the process of collecting and importing records from different sources into a data lake. These sources include structured data from databases, unstructured data from documents or social media, and semi-structured data from logs or sensor readings. The data is stored as is, without specific order, so it can be explored and analyzed in its original state.

Once the data is in the data lake, it can be processed and changed to make it easier to read and use for analysis. Processing involves filtering, combining, or summarizing data to find meaningful insights. Transformation converts the raw data into a more organized format, like tables or columns, allowing quick and accurate analysis.

• Definition: A data lake is a vast reservoir that stores raw and unprocessed data from numerous sources. It allows information to be stored as-is, without predefined structures.
• Use cases: Use data lakes when you need to store and explore vast amounts of diverse data. For instance, a healthcare provider might use a data lake to store patient records, medical images, and research data.
• Pros and cons: Data lakes offer the benefit of storing raw data in its original form, which provides flexibility for exploration. However, they can become "data swamps" if data quality, governance, and security aren’t properly managed.

• Definition: A data warehouse is a structured database that stores processed and organized data. It’s designed for efficient querying and analysis, with data organized into tables and columns.
• Use cases: Use data warehouses when you need to analyze historical data for reporting, business intelligence, and decision-making. For example, a retail company can use a data warehouse to evaluate sales trends, customer behavior, and inventory management.
• Pros and cons: Data warehouses provide fast and reliable querying capabilities but lack agility in handling large volumes of unstructured or rapidly changing data.

• Definition: A data lakehouse combines the best of both worlds. It stores raw data like a data lake but also incorporates structured elements like a data warehouse.
• Use cases: Use data lakehouses when you need to combine the flexibility of data lakes with the structured querying capabilities of data warehouses. This hybrid approach is ideal for real-time analytics, machine learning, and data exploration.
• Pros and cons: Data lakehouses aim to bridge the gap between data lakes and data warehouses, pairing the flexibility of data lakes with the structured querying of data warehouses. However, implementing and managing a data lakehouse can be complex and require careful planning.

Following best practices ensures your data is organized, reliable, and secure. These guidelines will help you make the most of your data lake.

• Strategize your data lake design and organization: A well-structured data lake starts with defining a clear design and organizational strategy. To do so, establish a logical folder structure and naming conventions that make locating and understanding data easy. Categorizing data based on business domains or data sources can simplify exploration and analysis.
• Manage data quality and metadata integrity: Data quality checks and validation processes will help you identify and correct inconsistencies or errors, creating more accurate analytics results. Additionally, metadata management plays a vital role in understanding the context and characteristics of the data. Cataloging metadata, such as data sources, format, and transformation processes, makes datasets easier to find and explore.
• Plan for scalability and performance: As your data lake grows, it's important to maintain its scalability and performance using distributed storage and processing technologies. This means you can store records across multiple servers or nodes to allow for parallel processing and faster retrieval of information. Partitioning data, using compression techniques to reduce the size of files, and refining how queries are requested from your data lake make retrieval and analytics far more efficient.

Data security is paramount in a data lake environment to protect against breaches or loss. Beyond basic security hygiene tactics, such as encryption and multi-factor authentication, you should also perform regular security audits and vulnerability assessments to identify potential risks or vulnerabilities. Additional measures include:

• Maintain data privacy and protection: Protecting sensitive information is crucial to maintaining customer trust and complying with privacy regulations. Anonymizing personally identifiable information (PII) ensures data privacy while still allowing for analysis. Implementing data masking techniques and access controls further strengthens data protection.
• Control user access and permissions: Controlling access to data is essential to maintaining data integrity and preventing unauthorized usage. Implementing role-based access controls (RBAC) ensures that users have appropriate permissions based on their roles and responsibilities. Regularly reviewing and updating user permissions helps ensure that only authorized users can read or access sensitive records.
• Comply with data regulations: Adhering to data regulations , such as GDPR, HIPPA, or CCPA, is crucial to avoid legal and financial consequences. Ensure that your data solutions support compliance.

Missing the mark on compliance isn’t just a financial risk—it can seriously impact your reputation. For example, under GDPR, fines can go up to €20 million or 4% of your global annual revenue . And while the financial impact stings, the real challenge is rebuilding trust after a breach. Customers expect their information to be safe with your company, and strong compliance measures show them you’ve got their back.

Every piece of information your business collects tells a story—but without the right tools, those stories might never be read or heard. The right data platform gives you the freedom to gather it all, fuel AI and agentic innovation, personalize customer experiences, inform decisions, and mitigate risks. Learn more about Data 360, the activation engine for your enterprise data, native to Salesforce.