Data Lake Use Cases for Business Leaders: Real Examples and Measured Outcomes

At the start of 2025, Gartner predicted that through 2026, organizations will drop 60% of their AI projects because their data isn’t ready. Data storage and processing are among the most important parts of AI adoption for any organization, but no one talks about them. In AI project meetings, most of them discuss which AI model to choose and which AI technologies to consider, but hardly anyone talks about data storage and its importance in AI projects.  

The concern related to data storage in AI projects can be addressed with a data lake. In this blog cum guide on Data Lakes and their top use cases, we will help CIOs, CTOs, heads of data, and business leaders understand where a lake fits in their AI projects, how to configure it, and how much they need to spend on data lakes for their AI projects.   

In simple terms, Data Lake is a centralized digital storage (repository) that can store a large volume of data. It can store data in structured, semi structured, and raw format. It can store data in structured tables, semi structured logs or unstructured files such as documents, images and audio. 

The data that is collected and stored is cleaned and structured as per the need of the business. This organized data is then used by AI models to reply to specific questions and handle tasks assigned to them. The collected and stored data is now being processed. It is cleaned and organized so it can be used to train AI models for different business requirements.  

• Low-cost cloud object storage: Cloud platforms like Amazon S3, Azure Data Lake Storage, and Google Cloud Storage are used to store any amount of data.
• Native format storage: Data from different sources lands here in every format.
• Schema on read: Data is organized when you use it, not when you save it. 

By using open data formats, modern data lakes add transactions, versioning, and governance on top of this storage. This makes them far more powerful and reliable than the basic cloud storage of a decade ago. 

A data lake is an online storage system that works in five different stages. The stages are Ingestion Layer, Storage Layer, Schema-on-Read, Processing and Analytics Layer, and Metadata and Governance. In each of these stages, data is handled and processed in a defined manner and forwarded to the next stage. 

Here’s how a data lake operates:

• Ingestion layer: In this stage, data is collected from different sources. The data is stored in raw and original formats and sizes.
• Storage layer: The data is stored in affordable and scalable cloud storage such as Amazon S3, Azure Data Lake Storage, or Hadoop HDFS.
• Schema-on-read stage: The data collected from different sources and in different formats is kept as-is. The data is processed and structured only when queried.
• Processing and analytics layer: In this stage, stored files are processed. Analytics layers are applied using tools such as machine learning, SQL queries, and big data transformations.
• Metadata & governance stage: In this stage, the system applies data usage rules and governance to the cleaned, structured data.   

Different research companies estimate the size of the data lake market differently, which often happens when a technology is changing fast. For 2025, one company (IMARC) valued the market at $17.8 billion, while another (The Business Research Company) valued it at $26.46 billion. However, both agree that the market is growing rapidly, by about 20% to 25% each year into the early 2030s. 

3 main reasons are driving this growth:

• More types of data: New AI tools create massive amounts of complex, unorganized data. The data comes in different forms, such as documents, phone recordings, and photos, that traditional databases cannot store.
• Low costs: Cloud storage is now very cheap, making it easy for companies to save all their data to use later.
• Unprepared companies: A 2024 survey showed that 63% of organizations do not feel confident that their data is properly organized or ready for AI. 

Surveys from technology sellers show similar trends, though we should remember they are trying to sell their products: 

• Architecture shifts: By 2027, as per Dremio’s 2026 report, 87% of tech leaders expect to use a modern data lake setup (called a lakehouse). 
• New possibilities: According to a survey by Ryft, a large majority (93%) say a popular data technology called Apache Iceberg allows them to use their data in completely new ways. 

Most businesses still get confused between a Data Lake, a Data Warehouse, and a Data Lakehouse. Each is different, so we created a practical comparison table to help you understand the differences and decide what’s right for your business.

No business trusts a data swamp because it is a collection of data without ownership, consistency, and quality. This is one of the reasons why analysts give up searching for what they need. 

Main reasons why a data lake fails and what steps are required to avoid a data swamp: 

1. No business question behind the build: When teams collect and store data only because storage is cheap, they create a data swamp. This data is useless to the business or teams.
2. Security added late: When security protocols or rules are not applied to sensitive data stored on shared storage, the data is at risk.
3. Weak Governance: Data stored with unclear ownership and loose access rules leads to compliance risk and confusion.
4. Poor quality: Poor-quality data ultimately creates a data swamp because it can’t be used to train AI models. 
5. A missing catalog: Data stored without metadata is useless. This is because no one in the organization knows what it contains or whether it is current or outdated. 

If you are a business and looking to invest in Data Lake, there are here’s how you can start with.   

1. Pick the problem that concerns you: First, decide which problem you want to solve with the help of a data lake. Pick the problems that concern you the most such as reporting, fraud, downtime o failures.
2. Audit the sources: Create a list of systems that you are using to store the relevant data. Who owns the respective system and how often it changes.
3. Choose the cloud and table format: Have a look at your in-house resources and services offered in the market, then select an open table format of your choice. You have options such as Apache Iceberg, Delta Lake, and Apache Hudi.
4. Set governance first: Setting up governance is really important. You need to decide on ownership, access rules, a catalog, and quality checks before the first dataset lands.
5. Run a pilot: Deliver the single use case from source to result, measure it against the starting number and record what broke.
6. Scale deliberately: After a successful pilot run, it is advisable to add one use case at a time on the same foundation. 

There are a lot of things that you need to consider when implementing a data lake to avoid data loss and service interruptions. 

However, if you are new to data lakes or cloud storage, then it is better to contact specialized data consulting firms, cloud platform providers, or enterprise software vendors.

A data lake earns its cost when it is tied to a specific use case, not to a storage plan.  

The strongest published results come from programs with a measured baseline, such as Nestlé’s retired systems, Uber’s shorter pipelines and Duly’s lower readmissions. Governance and data quality decide whether a lake becomes an asset or a swamp.  

Talk to SHALIGRAM‘s data engineering team about where a data lake fits your roadmap.