From scattered data to decisions you can defend.
We unify the information spread across your ERP, CRM, and spreadsheets, build dashboards leadership actually uses, and, when it pays off, custom predictive models to anticipate demand, churn, or risk.
What it is
What is data analytics consulting?
Data analytics consulting helps a company turn its data into information it can decide with: sales by channel, margin by product, collections, inventory, productivity. The most visible output is the dashboard, but the real work comes first: bringing the data together, cleaning it, and making every department count the same numbers.
That foundation is data engineering: the pipelines that pull data from each system, transform it, and keep a single source of truth up to date without anyone exporting spreadsheets. On top of it come the dashboards and, when looking back is not enough, predictive models that anticipate what comes next.
Symptoms
Signs your data is not working for you
If two or more sound familiar, the problem is not a lack of data: it is that the data lives in the wrong place.
The report is built by hand. Someone exports from three systems, pastes into a spreadsheet, and sends it on Monday, if they are not out sick.
The numbers do not match. Sales says one figure, finance says another, and the meeting goes into arguing which is right.
Answers take days. A simple question from leadership turns into a ticket for IT.
Decisions look backward. Purchasing, inventory, and staffing are sized on last year's experience, not a forecast.
Nobody knows which version is current. There are five spreadsheets with the same name and different dates.
What we do
Data engineering services and analytics we deliver
Three layers built in order. Without the first, the other two do not hold.
Data engineering
We connect the ERP, CRM, e-commerce, and spreadsheets into a single source of truth, with pipelines that keep it updated on their own.
Dashboards and BI
Metrics defined with each department, in dashboards that refresh themselves and answer the questions leadership asks every week.
Custom predictive models
Demand forecasting, customer churn risk, credit risk, or predictive maintenance, trained on your own history.
Reports that send themselves
The weekly report or the variance alert, generated and delivered automatically to whoever needs it.
Data quality and governance
Rules so every record is entered once and correctly, with clear lineage and access control.
Natural language questions
An assistant that answers questions about your data (“how much did we sell in March by location?”) without building a new report.
Dashboards that get used
How to build a dashboard leadership actually uses
Most dashboards fail because they start with the design. We start with the decision.
The decision
Who decides what, and how often. A dashboard with no decision behind it is decoration.
The metrics
Five to ten per role, not fifty. Each with a written definition every department agrees on.
The sources
Which system each figure comes from, how it is cleaned, and how often it refreshes.
The design
What matters most at the top left, compared against the target or the previous period, readable in ten seconds.
The cadence
Which meeting it is reviewed in and who owns each variance. Without that ritual, nobody opens it.
Metrics by role
Which metrics each team watches
A starting point. The final ones come from each company's real decisions.
| What they decide | Typical metrics | |
|---|---|---|
| Leadership | Where to grow and where to cut. | Sales and margin by line, cash flow, budget attainment. |
| Sales | Who to sell to and what to push. | Sales by rep and channel, conversion, average order value, at-risk customers. |
| Operations | How much to produce, buy, and staff. | Inventory and turnover, on-time delivery, demand forecast. |
| Finance | How to collect and pay on time. | Collections and days sales outstanding, overdue balances, pending payables. |
Dashboard or model
Dashboard or predictive model?
You do not always need a model. Often the first leap in value is simply seeing clearly what already happened.
| Dashboard | Predictive model | |
|---|---|---|
| Answers | What happened, and how are we doing? | What will happen, and what should we do? |
| Needs | Integrated, well-defined data. | The same, plus enough history of what you want to predict. |
| Worth it when | You cannot see clearly what already happened. | You already see the past, and anticipating it changes an expensive decision: purchasing, inventory, staffing, credit. |
| Not worth it when | Nobody has time to look at it. | There is no history, or the decision would not change with the forecast. |
| Example | Collections by customer and aging. | Predictive maintenance: anticipating a machine failure from its sensor data. |
Tools
BI tools or custom development
The tool is chosen last, not first. This is what we use and when.
Power BI, Looker Studio, or Metabase for dashboards, depending on what your company already uses and who will maintain them.
A cloud data warehouse (such as BigQuery) when there are several sources and high volume.
Your ERP's own reports when they are enough: if the information already lives in one system, you may not need anything else.
Custom development for predictive models, automated reports, and dashboards embedded in your own systems.
Your data stays yours. Everything runs in your cloud account, documented, so you never depend on us to access your own information.
What we build on
Data that already works on its own
EMAS S.A.
Manufacturing
An ERP with more than 15 modules that unified purchasing, inventory, production, sales, and administration: the single source of truth any dashboard needs.
M.I.C.A · Integrando Salud
Healthcare
Beyond serving patients, the agent reconciles payments, calculates balances, and sends audit reports to each center's administration team without anyone asking.
FAQ
Frequently asked questions
What is data analytics consulting, and what does it do?
It helps a company decide with data: it identifies the decisions that matter, brings the data behind them into a single source of truth, and builds the metrics, dashboards, and, when useful, the predictive models that answer those questions.
What do data engineering services include?
Connecting the source systems, building the pipelines that extract, clean, and load the data, designing the data warehouse, and monitoring data quality so dashboards and models always work on reliable information.
Our data is spread across spreadsheets and systems. Can we still start?
Yes, that is the most common case. The first step of the work is exactly to bring those sources together and define a single version of each figure.
When do you need a predictive model?
When you already see the past clearly and anticipating something would change an expensive decision: how much to buy, how much inventory to hold, which customers are about to leave, or which machine will fail. There has to be enough history, and the model has to beat the current estimate.
What is predictive maintenance?
It is anticipating an equipment failure before it happens, using data such as vibration, temperature, power draw, or failure history. It lets you schedule maintenance when it is needed instead of waiting for a breakdown or following a fixed calendar.
Which BI tools do you work with?
Power BI, Looker Studio, and Metabase for dashboards, and cloud data warehouses such as BigQuery for storage. The best one is the one your team will actually maintain and use.
Who owns the data, the models, and the code?
Your company. Everything runs in your cloud account and is documented, so you can access your information and keep using the models without depending on us.
Is AI replacing data analysts?
It replaces the mechanical part: gathering data, rebuilding the same report every week, answering simple questions. Interpretation, judgment about what to measure, and the decision itself stay human, and gain time for it.
Related guides
Go deeper on the topic
KPI dashboard: what it is, what it's for and how to build one people actually use
A KPI dashboard is worth the decisions it helps you make, not how good it looks. What it is, which types exist and how to build one starting from the decision, not the chart.
Read the guidePredictive maintenance with AI: what it is, how it works, and where to start
Anticipating a machine failure before it stops the line does not require a massive project. It requires picking the right asset, having the right data, and testing the model against the past before you trust it.
Read the guide
Other services
What else we build
AI agent development
AI agent development for businesses that need agents to do the work.
See the serviceIntelligent document processing
Invoices, receipts, and contracts that read and file themselves.
See the serviceAI automation and systems integration
Business process automation with AI, and systems that finally talk to each other.
See the serviceCustom software development
Custom software development for companies that have outgrown off-the-shelf tools.
See the serviceAI consulting
AI consulting that ends in production, not in a slide deck.
See the service
What would you decide differently if you had the number on time?
Tell us which decision it is, and we will tell you where that data comes from, how to get it into a dashboard, and whether you need anything more.