Researchable

How Dutch startup Researchable partners companies to drive innovation through data

In the AI Startup of the Week, the editorial staff of ai.nl is featuring promising AI startups, their innovations, solutions and challenges. In this eleventh episode, we are taking a look at Researchable, a Dutch AI startup facilitating data-driven innovations by becoming a partner in data science and data engineering.

All companies, regardless of their business vertical or size, have a large amount of data at their disposal. All of this data, collected by them through various operational endpoints, can be tapped to unlock new business value. However, not every business today possesses the technical skills, know-how, or ability to unlock that potential.

The data science and data engineering field remains nascent enough for these companies to look for partners. One such partner is Researchable, a Dutch startup that helps companies unlock business value and gain “high-value data innovations.” Here is a look at how Researchable is becoming a valuable partner in data, science, and technology.

Innovation through data

Data is the new oil is no longer a quote attributed to Clive Humby but one that businesses live by. A number of businesses have used data to identify limitations in their business operations, find new business opportunities, and even improved their topline and bottomline. However, to really make sense of the data at your disposal, companies need tech talent, especially data scientists and data engineers.

Researchable fills this gap with a team of computer scientists, software engineers, and data engineers. The startup was founded by former computer scientists to help other researchers automate their data collection and data analytics. With companies getting interested in data and analytics capabilities, Researchable transformed into a partner.

The Dutch startup operates as both a software partner as well as a data partner for science and business. It is the scientific background of the team at Researchable that sets it apart from others. With their computer science background, the Researchable team is able to analytically look at technical challenges.

This also results in the startup developing scalable applications and advanced data analyses for businesses. With these advanced scientific developments, businesses can use data to add value and naturally scale their operations.

Researchable: how does it become a data partner?

The raison d’etre for Researchable is the fact that many organisations struggle in building data-driven solutions. In order to solve this, Researchable adopts an human-centric approach where the focus is on building a true end-to-end partnership and developing software and data-driven solutions.

  • Software development: One of the ways Researchable partners with companies is by building software for them. The team at Researchable works with the development team of the client and to build scalable web applications and platforms. It also helps businesses with development of their entire data platform or application. Whether it is a small piece of your data puzzle or entire data platform, Researchable does everything related to software development.
  • Data engineering: One of the fundamental problems faced by companies is the way they collect and store data. Researchable makes sure that it helps companies collect and store data in the most suitable way to realise data innovations. As part of its data engineering operations, it helps companies enable data to be easily available for analyses. It generally helps organisations with “automatic collection of data, structured storage of data, and the integration and unlocking of systems and other data sources.”
  • Data science: Once data collection is done and data is stored in a structured way, the next step is to clean and analyse the data. This process leads to finding answers to questions and provides metrics to solve business problems. Researchable team also helps businesses with its ability to unlock business opportunities for clients.

Researchable’s work in building data platform and ML tool

In the Netherlands, there is a number of research being done on sport and physical activity. However, these researches are done in a fragmented way and carried out in unstandardised ways. In order to connect all the fragmented research activities on a so-called knowledge island, sport data valley was created as a digital data platform by Sportinnovator to serve sport, science, government and businesses.

Researchable built one large scalable data platform in which “recreational athletes, coaches, researchers, companies, municipalities and others can safely manage, analyse and access data through a central infrastructure.” In order to derive insights by combining different data sources, the platform has multiple links with “existing sensors, fitness apps, smartwatches and athlete management systems.”

In a quintessential Dutch fashion, the platform is built with the “privacy by design” nature where privacy, quality of data, and security of storage and links are taken care of. The sport data valley went live in September, 2020, and it overcomes the challenges associated with sports data such as fragmentation caused by multiple connected devices and accessibility of this top-class data to everyone.

“We have asked many software development companies to make a proposal and Proof-of-Concept for the national Sport Data Platform. Researchable was selected for this project because they are flexible and proactive, very efficient and they exceed expectations,” says Auke Damstra, Managing Director of Sport Data Valley.

Another example of Researchable’s scientific background is their work with the I-SHARED project, a research project that focuses on shared decision making between patient and clinician within the psychiatry department of the University Medical Center Groningen (UMCG).

About 5 per cent of people in the Netherlands suffer from depression every year. There are various treatments available for depression with varying outcomes that are influenced by patient characteristics like age, severity of illness, history of illness, et cetera. To solve this varying outcome, Researchable came up with the idea to develop an instrument that supports “the choice of treatment for depression.”

The I-SHARED instrument uses various socio-demographic and clinical data to provide a personalised overview of possible treatments. In order to make this possible, Researchable contributed to the development of a machine learning-based decision making tool. The startup delivered a microservice architecture structure for the generation of visualisations and reports for patients and practitioners.

These personalised reports allow the practitioner and the patient to enter into a dialogue to determine what could be the best treatment. This ML model overcomes the challenge of combining the data and then automatically generating a personalised report using machine learning.

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