Data analytics that turns numbers into decisions
Gjethe helps businesses use their data: collecting and cleaning data from spreadsheets, databases and tools, joining it into one reliable source, analysing it with SQL and Python, and presenting findings in clear reports and dashboards in Looker Studio, Power BI or Metabase. Every analysis starts from a business question and ends with a plain-language answer.
- Free first consultation
- Written quote for your project
- Flexible payment milestones
At a glance
- Covers
- Cleaning, integration, analysis, dashboards
- Tools
- SQL, Python, BigQuery, BI tools
- Output
- Answers, reports, dashboards
- Approach
- Question first
Definition
What is data analytics?
Data analytics is the process of collecting, cleaning, combining and analysing data to answer questions, find patterns and support decisions.
Most businesses have more data than they use. Sales live in one system, marketing in another, operations in spreadsheets; numbers don't match and nobody trusts them. Analysis on that foundation produces arguments rather than answers. We start by getting data into a reliable, documented shape, then answer the questions that matter: which customers are most valuable, where costs leak, what drives churn, which products to stock.
Who is it for?
E-commerce
Customer value, product performance, repeat purchase and returns.
SaaS
Activation, retention, churn and revenue metrics.
Operations-heavy businesses
Logistics, inventory and cost analysis.
Teams living in spreadsheets
Manual reports replaced with reliable, automated ones.
What's included, and what you keep
What's included
- Business question definition
- Data source inventory
- Data extraction from tools and databases
- Data cleaning and validation
- Joining sources into one model
- Metric definitions
- Exploratory analysis
- Customer segmentation and cohort analysis
- Forecasting where data supports it
- Dashboards in Looker Studio, Power BI or Metabase
- Automated data refreshes
- Written findings and recommendations
What you receive
Handed over at the end. Yours to keep.
- Cleaned, documented dataset
- Analysis report with findings
- Dashboards
- Metric definitions
- Queries and notebooks
Start with the question
“Analyse our data” isn't a question. “Which acquisition channel brings customers who stay longest?” is. Clear questions decide which data matters, which methods fit and what a useful answer looks like.
We start every project by writing down the decisions you need to make and the questions behind them, then check whether your data can answer them. Sometimes the honest answer is that it can't yet, and the first step is collecting the right data.
Clean data before clever analysis
Duplicate customers, inconsistent product names, missing dates and different definitions of “active” distort every result built on them. Cleaning and validating data is often most of the work, and it's what makes the analysis trustworthy.
We document every cleaning step and metric definition, so results can be reproduced and anyone can see how a number was calculated.
Common analyses
| Analysis | Answers |
|---|---|
| Cohort analysis | Do customers acquired in different periods behave differently over time? |
| Customer lifetime value | How much is a customer worth, by segment or channel? |
| Segmentation | Which groups of customers behave alike? |
| Funnel analysis | Where do people drop off? |
| Churn analysis | Who leaves, and what happened before they left? |
| Pricing analysis | How do price changes affect volume and revenue? |
Data protection in analytics
- Only the personal data needed for the question
- Pseudonymised or aggregated data where possible
- Access limited to people who need it
- Processing documented under a data processing agreement
- Data deleted or returned at the end of the project
Explaining findings clearly
An analysis is only useful if the people making decisions understand it. We present findings as short answers to the original questions, supported by a few clear charts, with caveats stated plainly: sample sizes, data gaps and what the analysis can't tell you.
Technical details (queries, methods, assumptions) are documented separately for anyone who wants to check the work.
From one-off analysis to ongoing reporting
Many projects start with a one-off question and reveal metrics worth tracking continuously. When that happens, we turn the analysis into a maintained data model and dashboard that refreshes automatically.
We keep ongoing reporting small and focused: a few metrics with clear owners and definitions, rather than dozens of charts nobody checks.
Working with spreadsheets
Spreadsheets are where most businesses' data actually lives, and they're not going away. We can make them more reliable (consistent structures, validation, lookup tables and protected formulas) or connect them to a database and BI tool as the next step.
Moving away from spreadsheets only makes sense when it solves a real problem, such as errors, size limits or manual merging.
Data quality checks we run
Before any analysis, we check for duplicates, missing values, impossible values such as negative quantities or future dates, inconsistent categories and totals that don't reconcile with source systems.
Issues are logged and either fixed with documented rules or raised with you when a business decision is needed, such as which of two conflicting records is correct.
How data analytics works, step by step
Every step has a clear output you can review. Timing is agreed in your quote.
- 1
Questions
Decisions and questions defined with you.
- 2
Data audit
Sources, quality and gaps reviewed.
- 3
Prepare
Data extracted, cleaned, joined and documented.
- 4
Analyse
Methods chosen to fit the question and data.
- 5
Present
Findings explained in plain language, with dashboards where useful.
- 6
Hand over
Queries, definitions and refresh set-up documented.
Tools, and what shapes your quote
Pricing is based on your project and agreed in your quote. These are the things that move it.
| Factor | Why it matters |
|---|---|
| Number of sources | More systems to connect means more integration. |
| Data quality | Messy data takes longer to clean. |
| Complexity of questions | Forecasting and modelling take more time than descriptive reports. |
| Dashboards | Interactive dashboards add design and build work. |
| Automation | Automated refreshes need pipelines and maintenance. |
| Area | Tools |
|---|---|
| Data | SQL, BigQuery, PostgreSQL, Google Sheets, Excel |
| Analysis | Python, pandas, Jupyter |
| BI | Looker Studio, Power BI, Metabase |
| Pipelines | scheduled extracts, APIs, dbt for larger projects |
Common mistakes to avoid
What we see go wrong most often, and how a good project avoids it.
Analysis without a question
Exploring data aimlessly produces charts, not decisions.
Inconsistent definitions
If teams define “customer” differently, numbers will never match.
Confusing correlation with cause
Patterns need careful interpretation before acting on them.
Dashboards nobody owns
Build reports for specific people and decisions.
Frequently asked questions
What data sources can you work with?
Databases, spreadsheets, CRMs, e-commerce platforms, analytics tools and most systems with an export or API.
Which BI tool should we use?
Looker Studio is free and works well with Google data; Power BI suits Microsoft environments; Metabase is a good open-source option. We recommend based on your stack.
Can you automate our weekly reports?
Yes. We connect sources and build dashboards that refresh automatically, replacing manual spreadsheet work.
Do you build data warehouses?
For growing data needs, we can set up BigQuery or PostgreSQL as a central store with documented models.
How is our data protected?
We use only the data needed, limit access, sign a data processing agreement and delete or return data at the end of the project.
Can you build forecasts?
Where historical data supports it, yes, with clear explanations of uncertainty.
Is this different from marketing analytics?
Marketing analytics focuses on tracking and campaign reporting. Data analytics covers broader business questions across sales, operations and finance.
Do you use AI in analysis?
We may use AI tools to assist with code and exploration, and every result is checked by a person.
Do we get the code?
Yes. Queries, notebooks and pipeline code are documented and handed over.
Can you train our team?
Yes. We can show your team how to use dashboards and maintain queries.
Can you analyse survey results?
Yes. We clean survey data, analyse responses by segment and summarise findings, including open-text answers.
How do we get started?
Send us the questions you want answered and a description of your data sources. We'll review a sample and propose an approach.
Can you work with data from Shopify or our CRM?
Yes. We extract data through exports or APIs from Shopify, HubSpot, Pipedrive and similar tools and combine it with your other sources.
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Business plans, market research and models.
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Custom systems, or HubSpot, Salesforce and Odoo set up properly.
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Machine Learning
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Data Analytics by industry
Get answers from your data.
Tell us what you need. You get an honest assessment, a clear plan and a written quote. The first consultation is always free.
- What happens next
- 1You tell us what you need
- 2We aim to reply within 24 hours with questions
- 3You get a written quote for your project