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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.