Data centralization: a practical guide for tech companies without a dedicated data team

Every tech company that runs on multiple management tools (support, sales, finance, CRM) eventually hits a point where the data from these tools never meets. Whether the company's product is a SaaS, an e-commerce, or a service, the problem is the same: each system keeps its own version of the truth, and nobody has a single view of the business.

Usually, it's the founder who feels this first because they were the one who set up most of these tools. They know where the support tickets are, the sales data, the consolidated financial figures, but they don't have the time, budget, or technical expertise to stitch it all together alone. Even so, the company needs this centralized view, whether for internal decision-making or reporting to an investor.

This is a practical guide to getting out of this situation, without having to hire a data team ahead of time.

Step 1: Map where every critical piece of data lives today

Before connecting anything, list the tools the company uses and what each one stores that actually matters for decision-making: customer support, sales, finance, product. You don't 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 everything

Setting up a data warehouse (BigQuery, Redshift, Databricks, among others) before connecting any sources prevents the most common mistake: every new tool becoming yet another isolated dashboard, rather than another source feeding the same data store.

Step 3: Separate connecting from maintaining

Building a quick integration is fast. What actually drains time is maintaining it: expired authentications, schemas changing without warning, syncs failing without alerting anyone. Before deciding who will build the pipelines, decide who will maintain them, because usually, that's not the same answer.

Step 4: Use a managed ingestion layer instead of building it in-house

For a company that doesn't have, and doesn't need yet, a dedicated data engineer, a managed ingestion layer solves exactly Step 3: it handles authentication, schema drift, reprocessing, and delivers organized data directly to the warehouse chosen in Step 2.

This is the kind of layer Erathos provides: out-of-the-box connectors for sources like Bling, CRMs, support, and financial platforms, delivering data straight into 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 reduces the effort of centralizing data from a project that would take weeks to a setup that takes just a few hours.

Step 5: Define who monitors the metrics, not who maintains the pipeline

With ingestion taken care of, the question shifts from "who is going to build this?" to "who is going to look at this data every week?" It could be the founder, an analyst, or whoever handles this informally today. The key is that this person starts spending their time modeling and making decisions, not debugging APIs.

Step 6: Put the data to work

With everything centralized, metrics like NPS and churn are tracked continuously, not just when someone remembers to stitch spreadsheets together. And reporting to investors, partners, or banks stops being a week-long project every time someone asks for an updated figure.

Proof: The UooU Solutions case study

This isn't just theory. UooU Solutions, an e-commerce platform from Blumenau, followed this exact path: WhatsApp customer service, sales platform, and finance running in silos, with no one dedicated to merging this data.

With Erathos, UooU centralized 6 data sources, including Bling, stores, and support tickets, directly into BigQuery on day one, without hiring anyone new or 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 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 one."

Read the full UooU Solutions case study to see the entire journey and the impact on NPS and churn.

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