Data analytics and Power BI: getting everyone to agree on the data is the real work.

Transform your business with our data science consulting, improving decision-making and optimizing strategic processes.

Hardly anyone calls us because they lack data. They call us because two departments report different numbers for the same thing and nobody has a way to settle it. That's where we start: where each piece of data comes from, who is accountable for it and what rule defines it.

Assessment before tools
On your platform, not ours
Every metric with its definition

Source inventory for a company: where each piece of data comes from, how it arrives and what state it is in —ready to use, pending cleanup or with no one accountable for it. It contains no real or simulated data.

Our Scope

Data analysis and management capabilities, from raw data to decision

Efficient data capture — We define the capture method for each source: what is read, how often, and who raises the alert when it stops arriving.
Cleansing and transformation — Formats, units and names follow a single standard, so two systems stop calling the same thing by different names.
Quality improvement — Every field is checked against the business rule that defines it, and whatever fails is removed with a record of why.
Statistics and artificial intelligence — Descriptive, predictive and prescriptive models for the questions a pivot table can no longer answer.
Interactive visualizations — Dashboards you explore, not screenshots: whoever has the question can drill down to the detail without asking anyone for a report.
Implementation — Establishing policies and roles, ETL development, dashboard building, model programming and tool implementation.
Proven tools — We work on market platforms rather than custom builds: what we build can be maintained by another team.

The order it's done in, which is what decides the outcome

Order matters: the tool comes fourth, not first.

Assessment — We build the source inventory and measure the condition each one arrives in. The result is a roadmap of what can be used today and what can't.
Owners and definitions — Before building anything, we agree on who is accountable for each domain and exactly what each metric means. This is the stage that prevents arguments over numbers.
Data pipelines — We develop the loads that bring in, clean and validate the data. Whatever fails a rule doesn't get in, and there's a record of why.
Dashboards and models — We build what answers the business questions, with each number's definition and cutoff date in plain view.
Adoption and support — We train the people who will use it and provide ongoing support during the first quarter. A dashboard nobody opens is a project that never finished.

What an ETL is, without the acronym

Bringing in the data is the easy part. Deciding what doesn't get through is where the work is.

The journey of a piece of data from its source to the dashboard, stage by stage: Capture, Cleansing, Quality, Model, Dashboard. Each stage shows what happens and what information is left out, with the reason logged.

A dashboard that breaks down six months after delivery almost never fails because of the tool. It fails because nobody knows where a number that doesn't add up came from, and finding out means opening up the entire process. That's why discards are logged. When data fails a check, the record shows which rule it broke and when, so the question “why did this number drop?” is answered by looking, not by reconstructing.

Scheduled loads, with an alert when a source stops arriving
Business rules written down and versioned, not in someone's head
Traceability of discarded data, with the reason
Reprocessing a period without touching the rest of the history
Everything runs on your platform, with your keys

Our data and analytics services

Contracted separately or as a single program, depending on where you're starting from

Analytics maturity assessment and roadmap — We assess the current state of the company's data management and analytics capabilities, and design a roadmap ordered by what unlocks value first.
Data governance — We establish policies, roles and processes that ensure quality, security and compliance. We facilitate the workshops, define the metrics and support the implementation.
Advanced analytics and BI — We design interactive management dashboards and reports, and apply predictive and prescriptive models where the decision justifies it.
Data quality and master data — We analyze quality, identify issues and design the continuous improvement strategy. We define the business rules and master data flows.
Strategic alignment — Every data initiative is tied to a named business objective. Anything that can't be tied to one doesn't get built.
Scalability and sustainability — We design solutions that grow with you, with the governance structure and support that sustain them when the team changes.

How we work, in four numbers

These aren't project results: they're the commitments we start with.

7 — Capabilities in scope, from data capture to implementation
3 — Phases with their own deliverable: consulting, implementation and support
1 — One definition per metric, agreed before it's built
0 — Numbers on a dashboard without a written rule to back them

What changes when data has an owner

Data governance isn't a policy document: it means there's someone you can ask

Every domain has an owner — A role, not a person's name. Whoever uses the data is the one who defines what it means, because they're the only one who can.
Written rules, not verbal agreements — Each metric's definition lives alongside the metric. If it changes, it changes with a date and a record of who approved it.
End-to-end traceability — From the number on the dashboard to the source record. The question “where did this come from?” gets answered in minutes.
Role-based access, reviewed — Who sees what is decided by profile and reviewed periodically, not inherited from the last project.
An alert when something stops arriving — A source that fails silently is worse than one that fails loudly: the dashboard keeps showing old numbers as if they were today's.
On your platform, with your keys — This is consulting, not a hosted service: what we build stays in your subscription and can be run by your team or ours.

Who is accountable for each piece of data

The table that keeps two departments from showing up to the committee with different numbers.

Arguments over numbers are almost never a calculation problem. It's that two departments use the same word for different things—what counts as a sale, when it's recognized, what happens with returns—and neither has the authority to settle the matter. That's why assigning owners comes before the first dashboard, not after. The rows that land in “committee” are the important ones: they are exactly the domains where today's contradictions are born.

One owner per domain, identified by role
One rule per domain: without a rule, ownership is in name only
Shared domains go to a committee that decides
It's reviewed when the process changes, not when the dashboard fails

Domain matrix for data governance: each information domain with the area accountable for it —the business, technology or a committee when it is shared—, the specific role and the rule it enforces. It contains no names of people or organizations.

Three phases, each with its own deliverable

Arcetec turns large volumes of information into decisions you can defend. You can start with any of the three.

Consulting — Assessment of the current state, roadmap design, metric definition, workshop facilitation and presentation of findings. It's useful even if you decide to work with someone else.
Implementation — Establishing policies and roles, ETL development, dashboard building, model programming and tool implementation.
Support — Data quality analysis, issue identification, business rule definition, profiling and master data flow development.

Questions we get before we start

Does our data need to be organized before we start?

No—and if it were, you wouldn't need to call us. The assessment starts from the real mess: which sources exist, what condition they arrive in and which ones can be used today. That determines the order in which it makes sense to fix them.

What tool do you work with?

The one you already have, if it works. We use market platforms—the Microsoft ecosystem, among others—rather than custom builds, precisely so that another team can maintain what we build if one day it isn't us.

Does our data leave our company?

No. This is consulting: the pipelines, the model and the dashboards are deployed on your platform, with your keys and your billing. We design, build and provide ongoing support; the information doesn't move to a service of ours.

How long until we see the first result?

It depends on the number of sources and their condition, and that's exactly what the assessment measures. What is constant: the first deliverable is the roadmap of what can be used today, not a dashboard. A dashboard built before the definitions are agreed on ends up being rebuilt.

Can we contract just one part?

Yes. The three phases—consulting, implementation and support—each have their own deliverable and are contracted separately. It's common to start with just the assessment and decide afterward.

What if we already have dashboards nobody uses?

That's the most common case. They almost always go unused because nobody trusts the numbers, and that's fixed through data governance, not by redoing the visualization. The assessment tells which is which before proposing anything.

How many versions of the same number are going around your committee?

A one-hour session is enough to build the source inventory and tell you which ones can be used today. No commitment and nothing to install.

Tell us where your data comes from today

We reply in less than one business day to set up the assessment session.

Two ways to work with us

We work in two ways, and this is one of them. Data and analytics projects are built on the client's platform, with their billing and their keys: the information doesn't move to any service of ours, and what we deliver can be run by your own team. It's the same approach as our Azure projects. Our own products— CataAI, CoworkOne and Lienzo de Arcetec —work the other way around: they're a service Arcetec runs on its own cloud, with a dedicated instance per client and nothing for you to manage.