Data Analytics: 3 steps to kick off your startup’s data-driven journey, with or without a data team
Steps for data analytics in startups: defining key metrics, centralizing in a data warehouse, and building the first dashboards—with or without a dedicated team.
When business data is properly collected, transformed, and leveraged, every company can achieve an ROI, regardless of their stage or size.
With modern technology, you no longer need a team of 5 to 10 data specialists to get there. Here is how to kick off your startup's data-driven journey, with or without an in-house data team.
1. Define your goals and objectives
What are you doing right now that data could help you improve? In 3 months, 6 months, and 1 year, what do you want to see as a result of your data maturity?
This is essential because it provides a roadmap to build your data strategy. By knowing what decisions you want data to inform before investing in it, you can design a data strategy that serves your goals, not the other way around. To start, think about your overall business goals, independent of data.
Write them down. Next, consider how data can be used to inform and achieve each of these goals. Write that down too, and you will have a clearer vision of what data can and should do for your business.
2. Set up a Modern Data Stack
A Modern Data Stack is the collection of systems you use to collect, transform, and activate your data. It is broken down into three main parts:
ELT (Extract, Load, and Transform) pipelines: extract data from your production database and the various SaaS applications your team is using. This is how you centralize your data into your data warehouse.
Data warehouse: where all your data is stored, cleaned, and centralized. It keeps everything organized, structured, and secure. It is your single source of truth.
Reverse ETL pipelines: send your transformed data back to the business apps, software, and systems your team uses daily, operationalizing it and democratizing access to this information.
In the past, having a Modern Data Stack meant having a custom data architecture built from scratch by a team of data engineers. These setups consisted of multiple niche tools interconnected by pipelines, taking months or even years to build and requiring significant time and resources to maintain.
But now, there are options to build or buy a Modern Data Stack, and you no longer need an in-house data team to build or maintain your infrastructure. For startups and SMBs, all-in-one solutions like Erathos are a much faster and simpler way to jumpstart your data operations. If you want to understand in more depth how to prioritize each piece of this stack based on your company's stage, it is worth reading Data Engineering for Startups.
With ELT and modeling pipelines built by data engineering experts, you can get your Modern Data Stack up and running in a matter of hours. Furthermore, having an interface designed for non-technical users means company data can be accessed across the organization without the need for an in-house data team.
To learn more about how Erathos can help you achieve data maturity at your startup, book a call with one of our data specialists.
3. Establish data governance
One of the most important aspects of investing in data analytics is ensuring you have the proper systems in place to keep your data accurate, reliable, secure, and accessible. A proper data stack will lay the foundation for this, but beyond that, it is crucial to establish ownership and accountability when it comes to your business data. Relying on a data team or hiring a data analyst is one way to achieve this.
If you have a dedicated data practitioner or team, they can take charge of your data analytics operations, being responsible for the ongoing maintenance and management of your data and how it is consumed.
But with an all-in-one Modern Data Stack built for non-technical users, you can also establish this governance internally within your current teams, without a dedicated data department.
By establishing clear KPI definitions and ownership, every stakeholder or team knows exactly how to measure success for what they are responsible for. The days when data analytics was reserved only for enterprise-level companies are long gone.
With today's modern technology, early-stage startups can leverage data and reap the benefits, driving innovation and staying ahead of the competition. If you are still deciding whether it is the right time to start, it is worth reading Why your startup should start its data-driven journey now, which details the actual cost of delaying this decision.
FAQ about starting the data-driven journey in your startup
Do I need a dedicated data team to follow these three steps? Not necessarily. With a Modern Data Stack designed for non-technical users, a founder or an analyst can set up and maintain operations solo, at least in the early stages of the company.
Which of these three steps is usually the most overlooked? The first one—defining clear goals before collecting data. It is common to jump straight into tooling or ingestion without clarity on exactly what decisions the data needs to inform, resulting in dashboards that no one uses to make decisions.
Are ELT and Reverse ETL the same thing? No. ELT moves data from your sources into the warehouse, centralizing the information. Reverse ETL does the opposite: it takes already modeled data from the warehouse and sends it back to the operational tools the team uses daily, like your CRM or marketing automation platform.
How long does it take to set up a basic Modern Data Stack? With a managed platform, initial ingestion can be running in a matter of hours. The bulk of the time is usually spent on defining goals and metrics in step 1, not on the technical implementation.
Conclusion
Starting your startup's data-driven journey no longer requires a team of 5 to 10 specialists or months of setup. With clear goals, a simple Modern Data Stack, and well-defined data ownership, any startup, with or without a dedicated data team, can start making data-driven decisions.
Create your free Erathos account and get your Modern Data Stack up and running in hours, not months.
When business data is properly collected, transformed, and leveraged, every company can achieve an ROI, regardless of their stage or size.
With modern technology, you no longer need a team of 5 to 10 data specialists to get there. Here is how to kick off your startup's data-driven journey, with or without an in-house data team.
1. Define your goals and objectives
What are you doing right now that data could help you improve? In 3 months, 6 months, and 1 year, what do you want to see as a result of your data maturity?
This is essential because it provides a roadmap to build your data strategy. By knowing what decisions you want data to inform before investing in it, you can design a data strategy that serves your goals, not the other way around. To start, think about your overall business goals, independent of data.
Write them down. Next, consider how data can be used to inform and achieve each of these goals. Write that down too, and you will have a clearer vision of what data can and should do for your business.
2. Set up a Modern Data Stack
A Modern Data Stack is the collection of systems you use to collect, transform, and activate your data. It is broken down into three main parts:
ELT (Extract, Load, and Transform) pipelines: extract data from your production database and the various SaaS applications your team is using. This is how you centralize your data into your data warehouse.
Data warehouse: where all your data is stored, cleaned, and centralized. It keeps everything organized, structured, and secure. It is your single source of truth.
Reverse ETL pipelines: send your transformed data back to the business apps, software, and systems your team uses daily, operationalizing it and democratizing access to this information.
In the past, having a Modern Data Stack meant having a custom data architecture built from scratch by a team of data engineers. These setups consisted of multiple niche tools interconnected by pipelines, taking months or even years to build and requiring significant time and resources to maintain.
But now, there are options to build or buy a Modern Data Stack, and you no longer need an in-house data team to build or maintain your infrastructure. For startups and SMBs, all-in-one solutions like Erathos are a much faster and simpler way to jumpstart your data operations. If you want to understand in more depth how to prioritize each piece of this stack based on your company's stage, it is worth reading Data Engineering for Startups.
With ELT and modeling pipelines built by data engineering experts, you can get your Modern Data Stack up and running in a matter of hours. Furthermore, having an interface designed for non-technical users means company data can be accessed across the organization without the need for an in-house data team.
To learn more about how Erathos can help you achieve data maturity at your startup, book a call with one of our data specialists.
3. Establish data governance
One of the most important aspects of investing in data analytics is ensuring you have the proper systems in place to keep your data accurate, reliable, secure, and accessible. A proper data stack will lay the foundation for this, but beyond that, it is crucial to establish ownership and accountability when it comes to your business data. Relying on a data team or hiring a data analyst is one way to achieve this.
If you have a dedicated data practitioner or team, they can take charge of your data analytics operations, being responsible for the ongoing maintenance and management of your data and how it is consumed.
But with an all-in-one Modern Data Stack built for non-technical users, you can also establish this governance internally within your current teams, without a dedicated data department.
By establishing clear KPI definitions and ownership, every stakeholder or team knows exactly how to measure success for what they are responsible for. The days when data analytics was reserved only for enterprise-level companies are long gone.
With today's modern technology, early-stage startups can leverage data and reap the benefits, driving innovation and staying ahead of the competition. If you are still deciding whether it is the right time to start, it is worth reading Why your startup should start its data-driven journey now, which details the actual cost of delaying this decision.
FAQ about starting the data-driven journey in your startup
Do I need a dedicated data team to follow these three steps? Not necessarily. With a Modern Data Stack designed for non-technical users, a founder or an analyst can set up and maintain operations solo, at least in the early stages of the company.
Which of these three steps is usually the most overlooked? The first one—defining clear goals before collecting data. It is common to jump straight into tooling or ingestion without clarity on exactly what decisions the data needs to inform, resulting in dashboards that no one uses to make decisions.
Are ELT and Reverse ETL the same thing? No. ELT moves data from your sources into the warehouse, centralizing the information. Reverse ETL does the opposite: it takes already modeled data from the warehouse and sends it back to the operational tools the team uses daily, like your CRM or marketing automation platform.
How long does it take to set up a basic Modern Data Stack? With a managed platform, initial ingestion can be running in a matter of hours. The bulk of the time is usually spent on defining goals and metrics in step 1, not on the technical implementation.
Conclusion
Starting your startup's data-driven journey no longer requires a team of 5 to 10 specialists or months of setup. With clear goals, a simple Modern Data Stack, and well-defined data ownership, any startup, with or without a dedicated data team, can start making data-driven decisions.
Create your free Erathos account and get your Modern Data Stack up and running in hours, not months.