Analytics & data
Analytics dashboards and data pipelines for growth
How we would bring scattered sales, marketing and finance data into one trusted warehouse and a set of dashboards that leaders actually use.
A look at how Grocito approaches building analytics and data pipelines for a growing company: the usual challenge, our approach and what to expect.
Challenge
As a company grows, its numbers end up in many places: the CRM, the website, ad platforms, the billing system and spreadsheets. Reports are built by hand, two people can quote different figures for the same metric, and leaders wait days for answers. The business wants one trusted source of data and dashboards the whole team can understand.
Our approach
We agree the questions first and the tools second. A short workshop with sales, marketing, finance and operations produces a metric dictionary: what each number means, how it is calculated and who owns it.
- Source audit. List every data source, how it can be accessed and how clean it is.
- Pipelines. Scheduled extraction from the CRM, website analytics, ad platforms and billing into a central warehouse, with checks for missing or unusual data.
- Modelling. Transform raw data into clean, documented tables for customers, revenue, pipeline and campaigns, version-controlled and tested.
- Dashboards. Role-specific views for leadership, sales, marketing and finance, designed around decisions rather than charts for their own sake.
- Access and governance. Role-based access, row-level rules where needed and an audit trail for changes to definitions.
- Handover. Documentation, training and a clear owner for each dashboard so it keeps being useful.
Solution
The front end of the solution is our Analytics & Business Intelligence Dashboard, connected to a warehouse and pipelines built through our Data Analytics service. Where data must flow in from other systems, our API Solutions and Cloud Solutions work provides the connectors and the hosting. For finance teams we can extend this to the Finance & Billing Automation System so billing data lands in the same place.
What to expect
An analytics project like this typically aims to replace manual reporting with refreshed dashboards, to give everyone the same definition of each metric and to make it easier to ask new questions of the data. Teams often find data quality problems early in the process, which is useful in itself. The value depends on how well the metrics match real decisions, so we review usage after launch and adjust.
Technology
- BigQuery, Snowflake or PostgreSQL as the warehouse
- Airflow and dbt for pipelines and modelling
- Python for extraction and data checks
- Power BI, Tableau, Looker or Metabase for dashboards
- Google Analytics, Google Ads and Meta data sources
- Google Cloud, AWS or Azure for hosting
- Grafana for pipeline monitoring
