04

Transform your data into actionable insights

Putting data to good use is the key to becoming a data-driven organization. We leverage big data and smart algorithms to help you gain new insights to form data-driven decisions. We enable organizations to consolidate massive volumes of structured, semi-structured, and unstructured data coming from different sources into a holistic environment that can be used for modeling and predicting new market opportunities.

What We Do

Technologies

MySQL
Amazon AWS
Python
PowerBI
Tableau

Industries

Financial Technology
Medical Technology
Telecommunications
E-commerce
Energy

Tech Roles

Data Scientist
Data Analyst
Data Architect

Our Expertise

Data Analysis

Data can come in many forms and many formats, often it’s not clear what can be achieved with this data. We can help you with understanding your data. It is achieved by applying different techniques to create insights in your data.

Data Modeling

Big data is everywhere, fitting that into your business processes or working with it can be challenging. Data modeling is the process of creating a structure between the captured data and the business needs. The end-goal of this process is to understand the relationships between data types, ways the data can be grouped/sliced, and transformed. This understanding is then visualised with a concrete database design.

Algorithm Development

Just having data stored and formatted in a usable way does not make it smart, the application of the data does. This is achieved by applying different mathematical algorithms to the data via Machine Learning techniques. This step of the process results in a trained model that can be used to complement the business process.

Data Visualisation

Looking at data in tables can be very boring and most likely will not present the insights that are gathered during the research and development part of a data science project. Data Visualization improves the end user’s insights drastically and often proves the results of the research. Data Visualisation is a complex task and besides Data-Scientist also involves UX/UI-designer to achieve the best outcome.

Workflow

We can start working on any part of this workflow – either taking all the steps together with you or joining after you completed some of the initial steps yourself.

WORK WITH US
01
Problem identification

Understanding the problem and gathering the business needs is very important, this ensures that all involved parties understand the task and the problem domain. During this process, the general hypothesis is also derived.

02
Hypothesis validation

After establishing the needs and the hypothesis, research into the validity of the hypothesis is performed. This ensures that during the next steps no major unexpected surprises come up. This step includes domain experts and identifies all the required data sources.

03
Data Modelling

Data comes in many formats and forms, during this step all the previously identified data sources are transformed, combined, and stored in an accessible way for the data scientist to run experiments.

04
Algorithm Development

Based on the previous research and data preparation a machine learning model can be trained and tested in a real scenario. This is achieved by experimenting with different machine learning techniques.

05
Data Visualisation

A machine learning model is a black box that follows the principal data-in is data-out and this can be very hard to work with for humans. Result Visualisation enables the insights, generated by the machine learning model, to be adopted by the company.

data science and analytics

Our approach

Technology partner

What makes pioneers different from regular people is their curious tech-optimism. We’re happiest when we can be the technology partner for companies that share this drive and see value in AI applications. We go an extra mile to ensure a long- lasting partnership with the technology believers.

Empowered teams

Happy and eager teams find their way to excellent results sooner than those who dedicate a lukewarm amount of care. That’s what we believe – building great code and masterpiece algorithms should have a decent amount of fun and joy in it. We want our people to be max creative and invested, and we do everything prescribed to keep them empowered and happy.

Mastery and ownership

We do love code. Getting it right and getting the best of it are our two working modes, and we take personal and professional growth seriously. Our teams initiate and participate in various activities oriented toward improvement, such as group coding (Coding Dojos), code reviews and knowledge sharing sessions between different technology teams.

Ready to make data-driven decisions?