We capture the hidden valueof your data
Stratify purpose: support decision-making and increase operational efficiency of our clients through intelligent data management.
Our Company
Stratify?
The Stratify Team was once in a situation very similar to yours. Relying on tools that didn’t work out and unable to find the needed support, we were forced to learn how to manage data the hard way.
At Stratify, we understand your struggles and know how to help you.
DATA PAIN POINTS
Inaccessible data, manual data processing, inadequate reports, to name just a few pain points… sounds familiar?
That’s where the team behind Stratify started.
Technology Challenge
Datawarehouse or Data Lake? ETL or ELT? Tableau or PowerBI? Azure, GCP or AWS? Whoever you ask, it’s always the right solution. Turns out, it really isn’t most of the time.
The team behind Stratify learned that lesson the hard way.
Organizational Challenge
We spent all that money on fancy tools and still don’t have what we need in reporting and analysis. Our data is all over the place, and our reports aren’t even used.
One of the toughest challenges that forged the team behind Stratify.
Service Challenge
We opted for the most renown consulting service, and now we can’t do anything without them, we spend a fortune every month but getting the most basic reports takes ages.
That’s how Stratify came to life.
Meet our team, the"hidden gems" behind Statify.
Passionate and highly trained minds in the world of data. Each member contributes with their unique expertise and dedication to ensure the highest service levels. From data analytics experts to technology visionaries, our team works in harmony to fuel your business success.
Albert Fabritius
Founder
Diego Caceres
Trainee Data Engineering/IoT
Diego Arenas
Advisory Board Member
Miguel Cintolesi
(I) IoT Partner
Stratify
Carlos Calfio
Executive Director
Agustín Llaña
Data Engineering
Matías Vergara
Data Engineering
Founder
Albert Fabritius
Albert combines years of management consulting experience in Europe, LATAM, and the US with line positions in Finance, Business Transformation, and IT.
(I)IoT Partner
Miguel Cintolesi
Miguel is a veteran in mechanical and electrical R&D and engineering, specializing in patent management, production line design, and IIoT solutions
Advisory Board Member
Diego Arenas
Diego is an engineer specialized in data science in an EngD program at the renowned School of Computer Science at the University of St Andrews in the UK.
Data Engineering
Agustín Llaña
Agustín is a data engineering with a strong foundation in data modelling and IoT solutions
Especialista IA
Matias Vergara
Matias es inginerio de computación con foco en soluciones aplicadas de inteligencia artificial. Combina su foco con experiencia en soluciones de IOT y de ingeniería de datos, para resolver desafíos de negocio.
Trainee Data Engineering / Science
Elías Ocque
Elias is engaged in data engineering projects, tackling data warehousing, ETLs/ELTs, and machine learning/AI challenges.
Trainee Data Engineering/IoT
Diego Cáceres
Diego is engaged in client data management and IoT development projects.
Advisory Board Member
Carlos Calfio
Carlos is an MIT-certified data scientist excelling in finance, sales, and LATAM/US expertise.
Data Pain Points
In 2017, a team of dedicated finance and IT professionals headed the financial operations of a US-run global network of more than 70 renowned universities. They faced a daunting challenge: data management was a maze of inconsistencies and unreliability. Simple data extracts took days or weeks to coordinate. Excel files were all over the place, with no single source of truth to be seen. Meetings started out by comparing data – in the few cases where data was available.
The team was granted a generous budget with the task of fixing the myriad of data issues.
The Technology Challenge
“Big names cannot be wrong, so let’s go with the biggest.” One of the hardest lessons learned was that in data management solutions big names equal big costs. But more ofthen than not they don’t represent best in class. However, getting the right tool for the job (a) makes a huge difference and (b) requires understanding the fundamentals.
The highly functional, cutting edge Stratify toolset emerged from countless proof of concepts.
The Organizational Challenge
“Squeezing the value out of your data is 80% organizational challenge.” As it turned out, unhealthy dependencies of individuals, data quality issues, internal controls, regulation requirements, support, maintenance, provider management, data governance and so much more are required to make data management sustainable. None of it has anything to do with the technology you choose.
The team bumped their head much more than once. Fancy buzzwords and academic concepts evolved into workable and pragmatic solutions.
The Service Challenge
“What’s the difference between a consultant and a pirate? The pirate will take the money and run. The consultant takes the money and sticks around for as long as he can.” The team behind Stratify realized that data management consultants always aim to learn your business instead of teaching their clients in advanced tools and practices. An easy entry is almost always followed by a rude awakening. Service costs go through the roof while service levels plummet. Stratify aims to be the partner the team would have loved to have when they were clients. Be there when needed, teach and coach in essentials, but sit on the bench when things are going fine.
Of all the lessons learned, the service challenge was the toughest for the team. It was the spark that ignited the foundation of Stratify.
