Data Engineering for Startups
Startups don’t need a complex data engineering stack. How to prioritize connectors, analytics destinations, and transformations by business stage.
Do you know what Data Engineering is and how it can help your Startup grow quickly and with the best possible quality? In this article, we will explain the impact of data engineering on business growth and leverage, and how Startups can benefit from understanding and investing in this area.
What is Data Engineering?
In general, when we talk about startups’ data infrastructure, it is rarely well structured from the beginning, which is natural in dynamic scenarios where many things need to “get off the ground” and be executed with agility to leverage the business. For this reason, in this type of organization, there is initial work to organize and map all of the company’s data sources, collect this data and, after the data ingestion process, store it following a treatment and modeling rationale, such as a Data Lakehouse, for example.
In an ideal scenario, these processes should be automated and easy to access in real time. The main idea of this process is to _get the house in order_ and make data useful for users and systems, rather than something centralized in specific departments or roles. 😉 In the data field, there is what we call Data Engineering precisely to solve these issues.
What is the importance of data for a Startup?
Being Data-Driven means making decisions based on empirical evidence, using data collected throughout the organization’s activities to effectively guide next steps.
When a company is at the beginning of its journey, especially when focused on developing one or more products, as is the case with startups, creating a culture of constant innovation is extremely necessary.
In this context, we often hear about “fail fast, learn fast, and get it right fast,” which is one of the most important cultural points for companies seeking accelerated growth. Having the right strategy to become a data-driven company ends up being a major turning point.
Fundamental questions to implement efficient Data Engineering:
What tools are needed to make this a reality?
How can you understand that something is not working without efficient metrics indicating it?
How can you learn from these mistakes without agile monitoring of what was implemented?
How do you know if a win is really a win without a reliable benchmark to understand process evolution?
These questions are important to understand the best way to create a data-driven culture. It all starts with a good data engineering process within the company, and for Startups this is essential.
What do you need to leverage Data Engineering in your startup?
There are several important implementations to boost your Startup’s Data Engineering, but the following are excellent starting points:
Modern Data Stack
The Modern Data Stack is a set of six data tools or components aimed at achieving a specific final outcome. In it, each component can be a set of different technologies working together so that an organization’s data is properly processed, easily accessible to everyone, and ultimately interpreted more efficiently, thus enabling a data-driven organization.
1) Data source
Today, companies depend on several different platforms for each part of their organizational processes: CRM for lead management and sales processes; help desks for Customer Success; as well as employee experience management platforms, financial management, and logistics. Each tool used generates its own set of data and insights, and these need to be organized in a way that makes sense and is easy to access.
In the Modern Data Stack, this is achieved through the use of a data storage architecture, in which after the ingestion process, once the data is processed, it is stored. These structures can be a Data Warehouse, a Data Lake, or even a Data Lakehouse.
2) Ingestion
Data ingestion is about exporting and processing company data so it can be stored in a standardized way and accessed as needed within the storage architecture used. In this process, there are two very important technical concepts: ETL or ELT.
Basically, each letter refers to stages used for data ingestion: Extraction is the process of obtaining data, Transformation is adapting it to the format (or formats) used by your MDS, and Loading is uploading the data into the storage architecture.
The difference between ETL and ELT lies in the order of the stages. In the first, after extraction, data is processed before being loaded; in the second, data is first loaded into the data storage system and then transformed as needed.
4) Modeling
Data modeling tools are used to obtain processed data within your storage architecture and convert it into standards that are more accessible and interpretable.
5) Visualization
This component of the Modern Data Stack is no longer about Data Engineering, but rather data visualization and application. This is where BI tools are found, and they are extremely important for understanding data, as they transform raw data into charts, tables, and dashboards that enable faster and more assertive analysis.
6) Activation
This process operationalizes data, obtaining it through your stack and allowing value to be extracted from it in real time. ### Count on the right people! As with any complex change process within your organization, being able to count on the right people is essential. This is even more notable when we think about startups.
For this reason, companies like Erathos are key players in helping your company start or continue its data-driven journey.
Conclusion
Every organization’s data-driven journey starts with good data engineering. For startups, then, this is a fundamental need, and we’ll explain why:
Data-based innovation is more assertive and minimizes operational errors, ensuring accurate analysis and the creation of more realistic forecasts to guide action plans.
Data engineering “gets the house in order” and allows information to flow more freely across different departments and hierarchical levels of the company. For startups, this ensures accelerated value generation with increasingly objective processes and growth-focused decisions.
Not having a good strategic data engineering partner to leverage the company’s strategy can increase decision-makers’ response time and allow small errors to go unnoticed, consuming many resources.
Want to know more about data engineering? On the Erathos blog, we have very complete content on several vital elements and processes on this topic. Check it out!
Request contact and learn how Erathos can help your company become data-driven in less time!
Do you know what Data Engineering is and how it can help your Startup grow quickly and with the best possible quality? In this article, we will explain the impact of data engineering on business growth and leverage, and how Startups can benefit from understanding and investing in this area.
What is Data Engineering?
In general, when we talk about startups’ data infrastructure, it is rarely well structured from the beginning, which is natural in dynamic scenarios where many things need to “get off the ground” and be executed with agility to leverage the business. For this reason, in this type of organization, there is initial work to organize and map all of the company’s data sources, collect this data and, after the data ingestion process, store it following a treatment and modeling rationale, such as a Data Lakehouse, for example.
In an ideal scenario, these processes should be automated and easy to access in real time. The main idea of this process is to _get the house in order_ and make data useful for users and systems, rather than something centralized in specific departments or roles. 😉 In the data field, there is what we call Data Engineering precisely to solve these issues.
What is the importance of data for a Startup?
Being Data-Driven means making decisions based on empirical evidence, using data collected throughout the organization’s activities to effectively guide next steps.
When a company is at the beginning of its journey, especially when focused on developing one or more products, as is the case with startups, creating a culture of constant innovation is extremely necessary.
In this context, we often hear about “fail fast, learn fast, and get it right fast,” which is one of the most important cultural points for companies seeking accelerated growth. Having the right strategy to become a data-driven company ends up being a major turning point.
Fundamental questions to implement efficient Data Engineering:
What tools are needed to make this a reality?
How can you understand that something is not working without efficient metrics indicating it?
How can you learn from these mistakes without agile monitoring of what was implemented?
How do you know if a win is really a win without a reliable benchmark to understand process evolution?
These questions are important to understand the best way to create a data-driven culture. It all starts with a good data engineering process within the company, and for Startups this is essential.
What do you need to leverage Data Engineering in your startup?
There are several important implementations to boost your Startup’s Data Engineering, but the following are excellent starting points:
Modern Data Stack
The Modern Data Stack is a set of six data tools or components aimed at achieving a specific final outcome. In it, each component can be a set of different technologies working together so that an organization’s data is properly processed, easily accessible to everyone, and ultimately interpreted more efficiently, thus enabling a data-driven organization.
1) Data source
Today, companies depend on several different platforms for each part of their organizational processes: CRM for lead management and sales processes; help desks for Customer Success; as well as employee experience management platforms, financial management, and logistics. Each tool used generates its own set of data and insights, and these need to be organized in a way that makes sense and is easy to access.
In the Modern Data Stack, this is achieved through the use of a data storage architecture, in which after the ingestion process, once the data is processed, it is stored. These structures can be a Data Warehouse, a Data Lake, or even a Data Lakehouse.
2) Ingestion
Data ingestion is about exporting and processing company data so it can be stored in a standardized way and accessed as needed within the storage architecture used. In this process, there are two very important technical concepts: ETL or ELT.
Basically, each letter refers to stages used for data ingestion: Extraction is the process of obtaining data, Transformation is adapting it to the format (or formats) used by your MDS, and Loading is uploading the data into the storage architecture.
The difference between ETL and ELT lies in the order of the stages. In the first, after extraction, data is processed before being loaded; in the second, data is first loaded into the data storage system and then transformed as needed.
4) Modeling
Data modeling tools are used to obtain processed data within your storage architecture and convert it into standards that are more accessible and interpretable.
5) Visualization
This component of the Modern Data Stack is no longer about Data Engineering, but rather data visualization and application. This is where BI tools are found, and they are extremely important for understanding data, as they transform raw data into charts, tables, and dashboards that enable faster and more assertive analysis.
6) Activation
This process operationalizes data, obtaining it through your stack and allowing value to be extracted from it in real time. ### Count on the right people! As with any complex change process within your organization, being able to count on the right people is essential. This is even more notable when we think about startups.
For this reason, companies like Erathos are key players in helping your company start or continue its data-driven journey.
Conclusion
Every organization’s data-driven journey starts with good data engineering. For startups, then, this is a fundamental need, and we’ll explain why:
Data-based innovation is more assertive and minimizes operational errors, ensuring accurate analysis and the creation of more realistic forecasts to guide action plans.
Data engineering “gets the house in order” and allows information to flow more freely across different departments and hierarchical levels of the company. For startups, this ensures accelerated value generation with increasingly objective processes and growth-focused decisions.
Not having a good strategic data engineering partner to leverage the company’s strategy can increase decision-makers’ response time and allow small errors to go unnoticed, consuming many resources.
Want to know more about data engineering? On the Erathos blog, we have very complete content on several vital elements and processes on this topic. Check it out!
Request contact and learn how Erathos can help your company become data-driven in less time!