Real impact from meaningful data.

We at Bean Solutions believe that data collection and reporting must have a real-life impact. To have a real and measurable impact, data must be valid and serve a purpose.

Through modern data-stack solutions we can refine collected data into meaningful actions and deliver useful insights to healthcare industry.

What do we do?

Healthcare is a complex industry. Data is scattered across multiple systems, making it difficult to find the right information to support timely and accurate decision-making.

As a result, many healthcare organizations are struggling to get a complete picture of their processes, organization and working environment.

This is where Bean Solutions comes in. With a vast experience in healthcare industry, data-analysis, and healthcare industry specific IT-solutions, we recognize, collect, validate, and refine your data into meaningful reporting and other solutions carrying a real impact.

Full-Stack Data Science and Analysis solutions

Machine Learning & AI

Centralized log management

Software development

Use-case recognition and data discovery

Everything starts with knowing what to ask. When seeking solutions with real life impact, it is crucial to understand the underlying problem and its context. Having a holistic insight into the use-case is a key in delivering an actionable solution.

The second step is knowing where to look and what to look for. When the problem is clear, it is time to break it into smaller manageable pieces and clarify the requirements for the data.

Defining the models

Transforming raw data into valid and rational models is a necessity in delivering valuable insights and solutions with real life impact. The key is discovering what variables and metrics depict the situation accurately and why.

Compressing datasets into actionable triggers is a combination of three things:

  • understanding the underlaying problem,
  • knowing the data and especially its limitations,
  • proficiency in data modelling techniques.

Modern data-stack solutions

Modern data-stack turns all the theoretical problematization and planning into reality. With state-of-the-art open-source software we make sure that not only are the used solutions appropriate for the customer they are also adaptable and possess a significantly lesser life cycle cost when compared to licensed software.
Implementation of open-source solutions starts with a customer specific proof of concept after which the customer can make an informed decision concerning the desired solutions for their problem and their data strategy at large.

Continuous improvement

Data driven decision making requires timely revisiting of the underlying data and models. In fact, one of the first insights arising from datasets and data models usually has to do with their limitations.

Our philosophy of continuous improvement aspires to get the most out of organization’s data by ensuring that the underlying logic (encompassing the whole process from data ingestion to modelling) is up to date. This means constant revisiting of the data sources, data itself, modelling techniques, visualization layers, and so on. Most of the tasks are automated but occasionally human touch is needed.

When the amount of collected data grows, so do the possibilities. With large enough data sets, it is possible eventually to progress towards machine learning solutions and partially or entirely automated decision making that take advantage of the latest ML & AI technologies available.

Want to hear more about our way of working?

Contact us!