What Is a Data Lake?
Key Concepts and Benefits

According to Forbes, 95% of businessesopens in a new window grapple with managing unstructured data, while Forrester reports 73% of enterprise dataopens in a new window goes unused for analytics.

With 94% of leaders yearning for more value from their data, the urgency to harness the power of data lakes has never been more important, especially in the age of AI. This article will show you how.

What is a data lake?

A data lake is a central repository of large volumes of data that’s stored in its original form. Most of that data 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
  • And more!

Data lakes can also store structured and semi-structuredopens in a new window information. This data can then be processed (i.e., cleaned, organized, and transformed) and used for data analytics, AI/machine learningopens in a new window, and customer experience personalization.

That all adds up to insights companies can use for a competitive advantage. In fact, data-leading companiesopens in a new window experience a whopping 89% improvement in customer acquisition and retention. Now, that’s a solid way for businesses to get ahead and stay there.

Data lakes also make data management easier. Experts estimate that unstructured data makes up 80 to 90% of all dataopens in a new window, meaning organizations that cannot process and analyze it aren’t getting the full picture of their business. Additionally, Forrester predictsopens in a new window that the amount of unstructured data enterprises manage will double in 2024. Data lakes provide an affordable, agile environment to store all this information without having to process and structure it first – saving time and money.

Benefits of a data lake

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

  • Centralized data storage
    Data lakes store a range of raw, unprocessed data in one central place. This saves organizations the time and the challenge of performing complex data transformations or organizing data into predefined schemas (i.e., a specific order) beforehand, making data storage convenient and accessible.
  • Data unification and analysis
    Data lakes are treasure troves of raw information that data scientists can turn into insights for decisions. They bring together data from multiple sources, both internal – like your CRM or ERP systems – and external, such as websites and social media. Unifying all this data in one location breaks down data silosopens in a new window that prevent companies from getting holistic views of their business health and a full understanding of customers. Through tools like Data Cloud, you can unify and activate your data across customer interactions and make the most of your data lake investment.
  • Trusted AI enablement
    Data lakes let you build AI initiatives on a vast and diverse data 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.
  • Scalability and cost efficiency
    Data lakes can store structured, semi-structured, and unstructured data without extensive data transformation or schema changes. This flexibility eliminates the need for costly data pre-processing, reducing overall storage and maintenance costs. 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. Cloud-based data lakes provide the flexibility to scale up storage capacity as your data grows. And pay-as-you-go models only charge you for what you use, reducing upfront costs.

Discover how Data Cloud helps you make the most of your data – regardless of where it resides.

Data lake use cases

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

Data exploration and analysis

Data lakes provide a central repository for storing diverse datasets from sources, ranging from CRM and ERP systems to social media applications and web and mobile applications. With these deep lakes of information, data scientists can perform analysisopens in a new window and advanced queries to expose previously hidden trends and gain business intelligence that can be used for innovation.

Machine learning and AI applications

The data stored in data lakes provides a great foundation for developing and training machine learning models and AI applications. Nine in 10 analytics and IT leaders agree that AI is only as good as the data it is built on. AI thrives on diverse and significant volumes of data to drive accurate and comprehensive models. Since data lakes integrate with machine learning platforms and frameworks, training and deploying AI models with data lakes can be managed efficiently.

Data-driven decision making

Data lakes help leaders make decisions grounded in a deep understanding of their businesses since they combine data from diverse sources. Plus, they can use tools to search, filter, and visualize data stored in the lake to make informed decisions about things like when to launch a new product, where to cut costs, or how to optimize inventory levels. Additionally, organizations can pinpoint anomalies and get ahead of emerging trends in real time by analyzing data continuously as it flows into the lake. And by powering AI and machine learning models with data stored in data lakes, you can get recommendations to streamline decision-making.

Here are a few industry use cases in action:

  • Customer experiences (Data exploration) A retailer can collect data from all the different ways a customer interacts with the brand – via a website, in person, on social media, via mobile, and more – to create a personalized omnichannel experience for each customer.
  • Customer churn prediction (AI models) A telecommunications business can integrate customer data, call logs, billing information, 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 data in a data lake, including records, images, and even research papers. Providers can then use predictive modeling to inform patient care.

Data lake architecture

Data lake architecture supports the storage of diverse data sets in their native formats: unstructured, semi-structured, and structured. But more importantly, the architecture should provide solutions to support data cataloging, governance, and security. This ensures that the data lake doesn't become a “data swamp” of assets that aren’t trusted or used. The key components of an effective data lake architecture include the following:

Data ingestion and storage

Data ingestion is the process of collecting and importing data 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 any specific order, so it can be explored and analyzed in its original state.

Data processing and transformation

Once the data is in the data lake, it can be processed and changed to make it easier to understand 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.

Data governance and security

Data governance and security are critical to maintaining the integrity of data lake architecture. Data governance entails rules and processes to manage data properly and adhere to compliance. These rules include who owns the data, who can access it, and how long it should be kept. Companies use security measures like encryption, authentication, and authorization to protect the data from being accessed by unauthorized people or stolen.

What is a data lake vs. data warehouse vs. data lakehouse?

In the world of data management, three terms often come up: data lake, data warehouse, and data lakehouse. While they all serve the purpose of storing and analyzing data, they have distinct characteristics and use cases. Choosing the right approach starts with understanding your business needs as well as the advantages and disadvantages of each solution.

1. Data lake

  • Definition: A data lake is a vast reservoir that stores raw and unprocessed data from numerous sources. It allows data 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, such as social media feeds, sensor data, or log files. 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 data exploration. However, they can become a "data swamp" if data quality, governance, and security aren’t properly managed.

Data Lakehouse 101

Explore the basics of Salesforce Data Cloud, our customer data platform built on data lakehouse tech. This trail is a helpful guide that breaks it all down clearly.

2. Data warehouse

  • Definition: A data warehouse is a structured database that stores processed and organized data, often following a predefined order. 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 might use a data warehouse to evaluate sales trends, customer behavior, and inventory management.
  • Pros and cons: Data warehouses, with their structured approach, provide fast and reliable querying capabilities but lack agility in handling large volumes of unstructured or rapidly changing data.

3. Data lakehouse

  • 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. Data Cloud is a prominent example of a data lakehouse.
  • 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.

Data lake vs. data warehouse vs. data lakehouse: key differences at a glance

Data lake best practices

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
    Implementing data quality checks and validation processes helps identify and correct any inconsistencies or errors. Additionally, metadata management plays a vital role in understanding the context and characteristics of the data. Documenting metadata, such as data sources, format, and transformation processes, makes data 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 (i.e., storing data across multiple servers or nodes to allow for parallel processing and faster retrieval of data). Partitioning data (dividing it into manageable chunks), using compression techniques to reduce the size of data files, and refining how queries are requested from your data lake can significantly enhance the speed and efficiency of data retrieval and analysis.

Data lake security and compliance

Data security is paramount in a data lake environment to protect against data 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 data 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 dataopens in a new window 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 maintain data security and compliance.
  • Comply with data regulations
    Adhering to data regulationsopens in a new window, such as GDPR, HIPPA, or CCPA, is crucial to avoid legal and financial consequences. Ensure that your data solutions support complianceopens in a new window.

Learn how to make the most of your data lake investment with Data Cloud

In a business world where data means differentiation, harnessing its full potential is the key to maintaining competitive advantage. Data lakes help companies do just that, storing vast amounts of unprocessed data that can fuel AI innovation, personalize customer experiences, inform decisions, and mitigate risks. With solutions like Data Cloud, which integrates your data lake with your CRM, you can ensure that data doesn’t sit idle in a silo but rather gets used to its fullest potential. Discover how Data Cloud can help you improve the return on your data lake investment.

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