What is a data lake? Everything you need to know

Most teams think of a data lake as a technical concern—something for data engineers to worry about. But data lakes hold almost limitless possibilities for marketing and analytics teams. 

By bringing together different types of data at scale, data lakes help teams discover customer insights that traditional data warehouses can’t surface. This deeper understanding enables you to deliver better customer experiences, stronger business results, and higher ROI. 

Ready to wade in? Read on for our complete guide to data lakes: 

• What a data lake is

• When to use it

• How it differs from a data warehouse

• Practical guidance for what data to bring in

• How to use your data lake to improve analytics, fuel machine learning (ML) models, and enhance decision-making

Key insights

• Without strong governance, your data lake can quickly become a ‘data swamp’: messy, unmanaged, and hard to use. Best practices—like clear policies, data quality checks, and a data catalog—ensure your data lake is reliable and usable.

• Many organizations lack a critical source of data in their data integration strategy: behavioral intelligence. Contextualize marketing, product, and operational data with quantitative and qualitative behavioral data to uncover insights that isolated datasets and structured data alone can’t provide.

• The value of a data lake compounds over time. Because raw data is preserved exactly as it was collected, you can revisit it months or years later with new questions, new models, and new context—something a data warehouse, which only stores processed data, can never offer.

What is a data lake?

A data lake is a centralized repository that stores raw unstructured, semi-structured, and structured data at any scale. 

It uses a schema-on-read approach, which means data is stored in its native format and the schema (or structure) is only applied when it's queried or analyzed. This allows teams to store vast amounts of diverse data without predefining how they’ll use it, giving them greater flexibility to explore the original, preserved data in new ways as business needs evolve.

Data lake vs. data warehouse: what’s the difference?

A data warehouse is a centralized repository that stores clean, structured, and processed data. It uses a schema-on-write approach, meaning that data is structured and transformed before it’s stored. This enables fast business intelligence (BI) and reporting, but offers less flexibility than a data lake to explore raw data and find connections between different data types (like structured and unstructured data).

The main differences between a data lake and a data warehouse are in how data is stored, processed, and used:

Data lake vs. data warehouse: key differences

Pro tip: instead of maintaining a data lake and separate warehouse, many modern organizations are adopting a data lakehouse—a hybrid data architecture built on top of a data lake that adds the structure, speed, and data management capabilities of a data warehouse to the scalable, flexible storage of a data lake. 

This enables teams to support both data science and business intelligence needs from a single platform while reducing costs, improving data quality, and strengthening data governance.

Why you need a data lake

By consolidating and storing raw data from multiple sources, data lakes help teams:

• Break down data silos: connect data from all areas of the business—like marketing, product, support, and operations—to spot relationships and trends you would otherwise miss

• Fuel advanced analytics and artificial intelligence: train machine learning models on your complete dataset to identify patterns and predict outcomes like churn before they happen

• Create a single source of truth: centralize data to improve cross-functional alignment, increase transparency, and enable more informed decision-making across the organization

• Get a 360° view of your customer: combine behavioral data with product, marketing, and transaction data (like feature adoption rates, campaign performance, and annual recurring revenue) to fill in blind spots and truly understand what moves the needle

Who needs a data lake?

Any organization that works with big data or complex data pipelines can benefit from a data lake, with those benefits extending across multiple teams:

• Marketing teams: deeply understand campaign performance, attribution, and customer journeys to quantify impact on KPIs like conversions and revenue

• Product teams: accurately map feature adoption, product usage, and the impact of frustration and user experience (UX) issues on long-term retention 

• Analytics and data science teams: enhance data exploration and investigation with complete, preserved data, enabling retroactive analysis and ML/AI capabilities that improve data usage

What types of data can you store in a data lake?

One of data lakes’ defining features—and core strengths—is their ability to store all types of data. Here’s what to add and why:

1. Structured data

Structured data is data with a predefined schema that fits into rows and columns. Examples include database exports like

• Customer profiles and transaction data from CRM and sales software

• Operational data from enterprise resource planning (ERP) tools

• Customer experience and behavior data from experience intelligence platforms

• Campaign performance data from marketing automation tools

Traditionally, this data has lived in data warehouses, not data lakes—but including it here lets you combine it with other data sources and types for deeper analyses and machine learning, making it an extremely valuable addition.

Pro tip: structured behavioral data is only useful in your data lake if it's actually there—clean, current, and ready to combine with your other sources. Contentsquare's Data Connect handles that automatically, piping behavioral data like engagement metrics and Frustration Score directly into your lake or warehouse of choice (like Snowflake, Google BigQuery, Databricks, Redshift, or Amazon S3)—without any engineering lift. 

That means your data scientists and analysts can start joining behavioral signals with CRM, product, and transaction data straight away, instead of waiting on a pipeline to be built.

2. Semi-structured data

Semi-structured data has some organizational properties (like metadata) but doesn’t have a rigid schema that fits into a table. Examples include 

• App and integration data, like JSON files and API responses

• Event and log data, like raw user interaction data and JavaScript error logs

Storing this type of data in your data lake enables you to query it in multiple ways without transforming it into a single fixed schema first. This gives you greater flexibility and enables retroactive analysis that’s especially powerful for investigating customer journeys and friction (like analyzing semi-structured error logs alongside structured frustration data).

3. Unstructured data

Unstructured data has no predefined schema or format. Examples include 

• Audio and video files, like session recordings

• Images, like heatmap visualizations

• Text documents, like email logs or support transcripts

Because this type of data doesn’t fit into relational databases or tables, most data warehouses can’t handle it. Storing it in your data lake lets you unlock qualitative insights about user behavior and experience and combine it with other data sources, like churn records from your CRM, to find critical connections.

Pro tip: quantitative data shows you what happened, but qualitative data shows you why. Incorporate unstructured data from Contentsquare capabilities like

• Session Replay: video playbacks of users navigating your site, app, or product from beginning to end

• Heatmaps: visualizations of where on a page users click, tap, scroll, and get frustrated

When your analysis finds noteworthy patterns, like high frustration scores or drop-offs, watch associated session replays to see exactly what happened—and understand how to fix it.

CTA: Learn more about why you need digital experience data in your data lake in our ebook→ 

4 key data lake use cases for marketing and analytics teams

By enabling connected data, data lakes help you perform real-time analytics, understand your customers in innovative ways, and ultimately deliver better experiences for them. Here’s how.

1. Audience segmentation

Connect session, behavioral, and transaction data to identify the common traits and actions of your highest-value customers. Then, target your marketing and advertising campaigns to this segment to attract more ideal customers and encourage users to follow similar paths, enhancing adoption and long-term success.

Pro tip: Smart Capture in Contentsquare automatically records all customer interactions on your app or website—like clicks, scrolls, and form fills—without requiring manual tagging, so you get a comprehensive, retroactive dataset from day one.

2. Churn prediction

Combine behavioral, product analytics, and customer support data with CRM and subscription records to identify the warning signs of customer churn. 

Find the patterns associated with customers leaving—like drops in engagement, multiple support tickets, increased errors, and frustration—then use churn prediction models to identify at-risk users and take proactive measures, like making product fixes or launching re-engagement campaigns, to prevent churn.  

3. Campaign attribution modelling

Unite experience intelligence with marketing and transaction data to see which touchpoints have the greatest impact on revenue and growth. Understand the behaviors, user journeys, and channels that influence conversions across different user cohorts to improve your attribution models and optimize your marketing ROI. 

Pro tip: use Impact Quantification in Contentsquare to see how digital experiences affect business outcomes like conversions and drill down into specific segments to compare the impact across different groups.

Then, use Data Connect to export the behavioral data that powers your analysis—like session data and engagement metrics—into your data lake or warehouse to combine it with data from your marketing automation platforms. 

4. Complete picture of the customer experience

Bring quantitative and qualitative data together to get a complete view of the user experience. Preserving raw data from every interaction lets you explore unexpected connections as you add new data sources to your lake, revisit historical data as your goals change, and stay user-centric by contextualizing quantitative numbers with qualitative experience insights from session replays, heatmaps, and user feedback.

Your data lake is only as valuable as the data that flows into it

To get the most from your data lake, you need to treat it as more than just storage. The real value comes when you connect diverse data sources across your organization and contextualize them with rich behavioral data—transforming your data lake from a repository to a source of actionable insights.

FAQs about data lakes