How a Data Warehouse Helps Your Company Grow
A Data Warehouse centralizes data from disparate systems for consistent analysis. Learn how it eliminates silos, accelerates decision-making, and how to choose the right architecture.
How a Data Warehouse Helps Your Company Grow
Let's state the obvious: you need to learn how to leverage your data! The best way to build a data-driven company is by having the right mix of tools at your fingertips: technology, professionals, metrics, infrastructure, and a Modern Data Stack. Within it, a central element is the Data Warehouse.
What is a Data Warehouse?
We've already talked here on the blog about what an analytical database is and its advantages, so we decided to dive a bit deeper into this topic and explain how a Data Warehouse helps your company grow and transform more and more into a data-driven organization.
First of all, it's important that you understand what a Data Warehouse is. One of the challenges of your data-driven transformation is finding a data management and storage infrastructure that is appropriate and makes sense to support and bring scalability to your data operation.
It is also important that, in addition to having the capacity to store a large volume of data, it has an interface and assists in supporting your Analytics and BI operations. Generally speaking, that is what a Data Warehouse does. BigQuery, Redshift, Snowflake, and Databricks are the most widely used examples in the market today, each with its own architecture and pricing model.
It works like the nervous system of your data operations, or even like a large archive that holds and maintains your entire volume of information generated through the operation of your organization's business areas in a centralized directory, making it available in a standardized and fast way whenever it is needed.
Over time and with the creation of new insights, those responsible for data in each department—such as marketing, sales, HR, product, and finance—will make new data available to be included in your Data Warehouse, so that it is safely stored and can help support strategic decision-making, complementing the consolidated history and the interrelation of each area with one another.
This helps enable the future use and application of BI and Analytics tools to support decision-making, scenario forecasting, and more realistic indicators of the organization's situation. In other words: the Data Warehouse is an innovation that emerged primarily to help support the analytical use of a company's massive volume of data, so that it can be used holistically to guide decision-making.
What are the benefits of using a Data Warehouse?
Now that you know what a Data Warehouse does, we need to talk about the main benefits of this technology for your company.
Preservation of historical data
You know all the insights, data, and charts generated by your various systems, such as ERP, CRM, spreadsheets, emails, and other tools used in the daily operations of your company's departments? What does your company do with them, and how do they guide important decision-making? Having all this standardized and centralized information inside your Data Warehouse is a very important step on your data-driven journey.
After all, to understand your past and perform well in the present (or even to be able to make future projections), it is vital to have historical data with reliable information and the agility to retrieve what is needed. In cases where the organization already has legacy databases, they usually lack the ability to store large volumes of data, which is fundamental to remaining relevant in such a connected world.
Improves data efficiency and quality
In any modern business, data is obtained in different formats and from different sources, and is usually structured or unstructured. Without a defined structure to standardize everything into a single format, making sound strategic decisions is simply impossible.
A Data Warehouse keeps information up to date, standardized according to the format determined by the BI and Analytics tools used by the organization, and consequently facilitates the auditing and quality assurance process. This makes your data reliable and your analysis efficient.
Another important scenario to consider is when you need to quickly retrieve information to solve a problem or build a strategic action plan. Your Data Warehouse can help you find what you need quickly and efficiently, saving your IT professionals valuable time.
Prevents Data Silos
Maybe you know this scenario: sales and marketing metrics don't speak to each other, sales and finance each organize themselves with their own systems and spreadsheets without interfacing with one another, companies depend on reports from data managers and take too long to make decisions, and the end-of-month closing is always a painful experience.
If this sounds familiar, it's because you have worked (or still work) in a company that suffers from Data Silos. Since a Data Warehouse helps centralize information, these silos don't happen, saving time, valuable resources, and countless meetings to discuss seasonal results.
Offers scalability
We know that nowadays "scalable" is just another one of those buzzwords people love to use to describe their departments, solutions, and companies. This is because the more room for growth, the better—and this same logic applies to your database. Today, the most modern Data Warehouses are built with the ability to add more elements to enable their growth.
Data Warehouse, Data Lake, or Data Lakehouse: which to choose
A Data Warehouse is not the only database that can be implemented to support your data-driven journey. Understanding the difference between these three options helps you make an informed choice, rather than just following a trend.
Data Warehouse stores structured, already modeled data, optimized for fast and recurrent analytical queries. It is the right choice when you already know which business questions you want to answer consistently and need predictable query performance.
Data Lake stores raw data in any format, structured or unstructured. It is cheaper for large volumes and serves well as a landing zone before any modeling, but requires more work when querying because the data is not analytics-ready.
Data Lakehouse combines both: it organizes raw data into progressive quality layers (Bronze, Silver, Gold) within the same environment, merging the lower cost of a Data Lake with some of the query performance of a Data Warehouse.
In practice, most growing companies start with a simple Data Warehouse because their data volume doesn't yet justify the complexity of a Lakehouse, and they migrate to a layered architecture as the number of sources and data volume grow.
How data gets to your Data Warehouse
Choosing the right Data Warehouse solves the storage and querying part, but it doesn't solve how data from your original sources (CRM, ERP, spreadsheets, product) gets there. This is the ingestion part, and that's precisely where Erathos comes in: connecting your company's data sources directly to the destination warehouse, in an incremental and managed way, without requiring you to write or maintain custom pipeline code.
Frequently Asked Questions about Data Warehouse
Do I need a Data Warehouse from day one of my company? Not necessarily. Early-stage companies with few sources and low volume can answer most of their business questions directly in the source systems or in spreadsheets. A Data Warehouse becomes worth it when the number of sources and data volume grow enough to make manual consolidation unfeasible.
What is the difference between a Data Warehouse and a Data Lake? A Data Warehouse stores structured, already modeled data optimized for fast querying. A Data Lake stores raw data in any format, which is cheaper but requires more modeling work before becoming useful analysis.
Are BigQuery, Redshift, and Snowflake the same thing? They are all cloud Data Warehouses, but they have different architectures and pricing models. The choice between them usually depends on the rest of your cloud stack and your expected usage patterns, rather than a fundamental difference in capability.
How does data get to the Data Warehouse? Through an ingestion process, which can be a custom script maintained by your team, or a managed ingestion tool like Erathos, which connects original sources to the destination warehouse without requiring you to build your own pipeline.
Conclusion
A Data Warehouse is a modern and innovative way to centralize an organization's data, and it is a key element of the Modern Data Stack.
There are several advantages to using it, but in essence, it is a way to make your organization's information more centralized, easier to access, and simpler to integrate with your BI and Analytics tools.
Create your free Erathos account and centralize your company's data in the right Data Warehouse for your stage, without having to write or maintain ingestion pipelines.
How a Data Warehouse Helps Your Company Grow
Let's state the obvious: you need to learn how to leverage your data! The best way to build a data-driven company is by having the right mix of tools at your fingertips: technology, professionals, metrics, infrastructure, and a Modern Data Stack. Within it, a central element is the Data Warehouse.
What is a Data Warehouse?
We've already talked here on the blog about what an analytical database is and its advantages, so we decided to dive a bit deeper into this topic and explain how a Data Warehouse helps your company grow and transform more and more into a data-driven organization.
First of all, it's important that you understand what a Data Warehouse is. One of the challenges of your data-driven transformation is finding a data management and storage infrastructure that is appropriate and makes sense to support and bring scalability to your data operation.
It is also important that, in addition to having the capacity to store a large volume of data, it has an interface and assists in supporting your Analytics and BI operations. Generally speaking, that is what a Data Warehouse does. BigQuery, Redshift, Snowflake, and Databricks are the most widely used examples in the market today, each with its own architecture and pricing model.
It works like the nervous system of your data operations, or even like a large archive that holds and maintains your entire volume of information generated through the operation of your organization's business areas in a centralized directory, making it available in a standardized and fast way whenever it is needed.
Over time and with the creation of new insights, those responsible for data in each department—such as marketing, sales, HR, product, and finance—will make new data available to be included in your Data Warehouse, so that it is safely stored and can help support strategic decision-making, complementing the consolidated history and the interrelation of each area with one another.
This helps enable the future use and application of BI and Analytics tools to support decision-making, scenario forecasting, and more realistic indicators of the organization's situation. In other words: the Data Warehouse is an innovation that emerged primarily to help support the analytical use of a company's massive volume of data, so that it can be used holistically to guide decision-making.
What are the benefits of using a Data Warehouse?
Now that you know what a Data Warehouse does, we need to talk about the main benefits of this technology for your company.
Preservation of historical data
You know all the insights, data, and charts generated by your various systems, such as ERP, CRM, spreadsheets, emails, and other tools used in the daily operations of your company's departments? What does your company do with them, and how do they guide important decision-making? Having all this standardized and centralized information inside your Data Warehouse is a very important step on your data-driven journey.
After all, to understand your past and perform well in the present (or even to be able to make future projections), it is vital to have historical data with reliable information and the agility to retrieve what is needed. In cases where the organization already has legacy databases, they usually lack the ability to store large volumes of data, which is fundamental to remaining relevant in such a connected world.
Improves data efficiency and quality
In any modern business, data is obtained in different formats and from different sources, and is usually structured or unstructured. Without a defined structure to standardize everything into a single format, making sound strategic decisions is simply impossible.
A Data Warehouse keeps information up to date, standardized according to the format determined by the BI and Analytics tools used by the organization, and consequently facilitates the auditing and quality assurance process. This makes your data reliable and your analysis efficient.
Another important scenario to consider is when you need to quickly retrieve information to solve a problem or build a strategic action plan. Your Data Warehouse can help you find what you need quickly and efficiently, saving your IT professionals valuable time.
Prevents Data Silos
Maybe you know this scenario: sales and marketing metrics don't speak to each other, sales and finance each organize themselves with their own systems and spreadsheets without interfacing with one another, companies depend on reports from data managers and take too long to make decisions, and the end-of-month closing is always a painful experience.
If this sounds familiar, it's because you have worked (or still work) in a company that suffers from Data Silos. Since a Data Warehouse helps centralize information, these silos don't happen, saving time, valuable resources, and countless meetings to discuss seasonal results.
Offers scalability
We know that nowadays "scalable" is just another one of those buzzwords people love to use to describe their departments, solutions, and companies. This is because the more room for growth, the better—and this same logic applies to your database. Today, the most modern Data Warehouses are built with the ability to add more elements to enable their growth.
Data Warehouse, Data Lake, or Data Lakehouse: which to choose
A Data Warehouse is not the only database that can be implemented to support your data-driven journey. Understanding the difference between these three options helps you make an informed choice, rather than just following a trend.
Data Warehouse stores structured, already modeled data, optimized for fast and recurrent analytical queries. It is the right choice when you already know which business questions you want to answer consistently and need predictable query performance.
Data Lake stores raw data in any format, structured or unstructured. It is cheaper for large volumes and serves well as a landing zone before any modeling, but requires more work when querying because the data is not analytics-ready.
Data Lakehouse combines both: it organizes raw data into progressive quality layers (Bronze, Silver, Gold) within the same environment, merging the lower cost of a Data Lake with some of the query performance of a Data Warehouse.
In practice, most growing companies start with a simple Data Warehouse because their data volume doesn't yet justify the complexity of a Lakehouse, and they migrate to a layered architecture as the number of sources and data volume grow.
How data gets to your Data Warehouse
Choosing the right Data Warehouse solves the storage and querying part, but it doesn't solve how data from your original sources (CRM, ERP, spreadsheets, product) gets there. This is the ingestion part, and that's precisely where Erathos comes in: connecting your company's data sources directly to the destination warehouse, in an incremental and managed way, without requiring you to write or maintain custom pipeline code.
Frequently Asked Questions about Data Warehouse
Do I need a Data Warehouse from day one of my company? Not necessarily. Early-stage companies with few sources and low volume can answer most of their business questions directly in the source systems or in spreadsheets. A Data Warehouse becomes worth it when the number of sources and data volume grow enough to make manual consolidation unfeasible.
What is the difference between a Data Warehouse and a Data Lake? A Data Warehouse stores structured, already modeled data optimized for fast querying. A Data Lake stores raw data in any format, which is cheaper but requires more modeling work before becoming useful analysis.
Are BigQuery, Redshift, and Snowflake the same thing? They are all cloud Data Warehouses, but they have different architectures and pricing models. The choice between them usually depends on the rest of your cloud stack and your expected usage patterns, rather than a fundamental difference in capability.
How does data get to the Data Warehouse? Through an ingestion process, which can be a custom script maintained by your team, or a managed ingestion tool like Erathos, which connects original sources to the destination warehouse without requiring you to build your own pipeline.
Conclusion
A Data Warehouse is a modern and innovative way to centralize an organization's data, and it is a key element of the Modern Data Stack.
There are several advantages to using it, but in essence, it is a way to make your organization's information more centralized, easier to access, and simpler to integrate with your BI and Analytics tools.
Create your free Erathos account and centralize your company's data in the right Data Warehouse for your stage, without having to write or maintain ingestion pipelines.