Data centralization: a practical guide for tech companies without a dedicated data team
A step-by-step guide to consolidating sales, support, and financial data in one place—even without a dedicated data engineer on your team.
Every tech company running on multiple management tools (support, sales, finance, CRM) eventually reaches a point where the data from these tools never meets. Whether your product is a SaaS, an e-commerce platform, or a service, the problem is the same: each system keeps its own version of the truth, and no one has a single view of the business.
Usually, the founder is the first to feel this, because they were the one who configured most of these tools. They know where the support numbers, sales data, and consolidated financial metrics live, but they lack the time, budget, or technical knowledge to stitch it all together themselves. Still, the company needs this centralized view, whether for internal decision-making or investor reporting.
This is a practical guide to breaking out of this loop, without having to hire a data team before you're ready.
Step 1: Map out where your critical data lives today
Before connecting anything, list the tools your company uses and the key metrics each one stores that actually drive decision-making: customer support, sales, finance, product. There's no need to be exhaustive—start with the 3 or 4 sources that come up most often when someone asks how the business is doing.
Step 2: Choose a single destination for all your data
Setting up a data warehouse (BigQuery, Redshift, Databricks, etc.) before connecting any source prevents the most common mistake: turning each new tool into yet another siloed dashboard, rather than another source feeding the same warehouse.
Step 3: Separate connecting from maintaining
Building a one-off integration is fast. What actually drains time is maintaining it: expired auth tokens, schema changes without warning, and sync errors that fail silently. Before deciding who will build your pipelines, decide who will maintain them—because usually, those are not the same answer.
Step 4: Use a managed ingestion layer instead of building pipelines in-house
For a company that doesn't have—and doesn't need yet—a dedicated data engineer, a managed ingestion layer solves exactly what we discussed in Step 3: it handles authentication, schema evolution, backfills, and delivers structured data straight to the warehouse chosen in Step 2.
This is exactly what Erathos provides: out-of-the-box connectors for sources like Bling, CRMs, support platforms, and financial tools, delivering data directly to warehouses like BigQuery, Redshift, Databricks, or Snowflake, without writing a single line of pipeline code. You can try it free for 14 days, no credit card required.
This shrinks the effort of data consolidation from a multi-week engineering project down to a few hours of configuration.
Step 5: Define who owns the metrics, not who maintains the pipeline
With ingestion taken care of, the question shifts from "who will build this?" to "who will analyze this data every week?" It could be the founder, an analyst, or whoever handles this informally today. The key difference is that this person now spends their time modeling data and making decisions, rather than wrestling with APIs.
Step 6: Put your data to work
With everything centralized, metrics like NPS and churn are tracked continuously, not just when someone remembers to cobble together spreadsheets. And reporting to investors, partners, or banks stops being a week-long fire drill every time someone asks for an updated number.
Proof of concept: The UooU Solutions case study
This isn't just theory. UooU Solutions, an e-commerce platform based in Blumenau, followed this exact path: customer support via WhatsApp, sales, and finance platforms were running in silos, with no one dedicated to consolidating this data.
Using Erathos, UooU centralized 6 data sources, including Bling, online stores, and support tickets, directly into BigQuery on day one—without hiring new staff and without writing a single line of ingestion code. Today, two people manage the data for a 34-employee company, neither of whom is a data specialist, and one of them is a co-founder.
In the words of Kauê Raizer de Jesus, co-founder of UooU: "If I were to advise another founder, I would say: centralize your data from day 1."
Read the full UooU Solutions case study to see their journey and how they achieved results with NPS and churn.
Start today
Create your free Erathos account and test it for 14 days, no credit card required.
If you'd like to discuss your specific use case before deciding, you can book a call with one of our specialists.
Every tech company running on multiple management tools (support, sales, finance, CRM) eventually reaches a point where the data from these tools never meets. Whether your product is a SaaS, an e-commerce platform, or a service, the problem is the same: each system keeps its own version of the truth, and no one has a single view of the business.
Usually, the founder is the first to feel this, because they were the one who configured most of these tools. They know where the support numbers, sales data, and consolidated financial metrics live, but they lack the time, budget, or technical knowledge to stitch it all together themselves. Still, the company needs this centralized view, whether for internal decision-making or investor reporting.
This is a practical guide to breaking out of this loop, without having to hire a data team before you're ready.
Step 1: Map out where your critical data lives today
Before connecting anything, list the tools your company uses and the key metrics each one stores that actually drive decision-making: customer support, sales, finance, product. There's no need to be exhaustive—start with the 3 or 4 sources that come up most often when someone asks how the business is doing.
Step 2: Choose a single destination for all your data
Setting up a data warehouse (BigQuery, Redshift, Databricks, etc.) before connecting any source prevents the most common mistake: turning each new tool into yet another siloed dashboard, rather than another source feeding the same warehouse.
Step 3: Separate connecting from maintaining
Building a one-off integration is fast. What actually drains time is maintaining it: expired auth tokens, schema changes without warning, and sync errors that fail silently. Before deciding who will build your pipelines, decide who will maintain them—because usually, those are not the same answer.
Step 4: Use a managed ingestion layer instead of building pipelines in-house
For a company that doesn't have—and doesn't need yet—a dedicated data engineer, a managed ingestion layer solves exactly what we discussed in Step 3: it handles authentication, schema evolution, backfills, and delivers structured data straight to the warehouse chosen in Step 2.
This is exactly what Erathos provides: out-of-the-box connectors for sources like Bling, CRMs, support platforms, and financial tools, delivering data directly to warehouses like BigQuery, Redshift, Databricks, or Snowflake, without writing a single line of pipeline code. You can try it free for 14 days, no credit card required.
This shrinks the effort of data consolidation from a multi-week engineering project down to a few hours of configuration.
Step 5: Define who owns the metrics, not who maintains the pipeline
With ingestion taken care of, the question shifts from "who will build this?" to "who will analyze this data every week?" It could be the founder, an analyst, or whoever handles this informally today. The key difference is that this person now spends their time modeling data and making decisions, rather than wrestling with APIs.
Step 6: Put your data to work
With everything centralized, metrics like NPS and churn are tracked continuously, not just when someone remembers to cobble together spreadsheets. And reporting to investors, partners, or banks stops being a week-long fire drill every time someone asks for an updated number.
Proof of concept: The UooU Solutions case study
This isn't just theory. UooU Solutions, an e-commerce platform based in Blumenau, followed this exact path: customer support via WhatsApp, sales, and finance platforms were running in silos, with no one dedicated to consolidating this data.
Using Erathos, UooU centralized 6 data sources, including Bling, online stores, and support tickets, directly into BigQuery on day one—without hiring new staff and without writing a single line of ingestion code. Today, two people manage the data for a 34-employee company, neither of whom is a data specialist, and one of them is a co-founder.
In the words of Kauê Raizer de Jesus, co-founder of UooU: "If I were to advise another founder, I would say: centralize your data from day 1."
Read the full UooU Solutions case study to see their journey and how they achieved results with NPS and churn.
Start today
Create your free Erathos account and test it for 14 days, no credit card required.
If you'd like to discuss your specific use case before deciding, you can book a call with one of our specialists.