Data quality is a business problem, as well as a tech problem. It is the biggest enemy of data-driven business and machine learning. Bad quality data can block or render a data project or machine learning use case unusable and thus a waste of money, human resources and time. Tackling data quality needs to be a targeted, systemic and ongoing, rather than a huge, one time cathartic event.
Startups im Gesundheitswesen wie doctorly sehen die Chance, mit SaaS-Plattformen, die auf offenen Standards im Gesundheitswesen basieren, das Rad neu zu erfinden. Das etablierte Gesundheitssystem basiert auf einer fragmentierten Landschaft von Softwareanbietern und Datenlösungen. Dateninteroperabilität bietet die Chance, Innovationen im Gesundheitswesen in Deutschland und darüber hinaus zu ermöglichen.
Healthtech startups such as doctorly and others see the opportunity to reinvent the wheel with SaaS platforms based on open standards in healthcare. The incumbent healthcare system is based on a fragmented landscape of software providers and data solutions. Data interoperability presents an opportunity to unblock innovation in healthcare in Germany and beyond.
Tech debt is often unavoidable in most early stage startups. Not fixing the tech debt as a company gets traction and scales is more of a problem than the original creation of the tech debt. Turning a blind eye to tech debt has implications beyond the stack and creates an unstructured and sloppy culture.
Facilitating the interface between IT and business is data governance, which is filled with opportunity. There is no specific career path into data governance, but the ability to understand metadata and contextualize organizational insights to executives holds much opportunity.
Irinia Nikiforova explains what data governance is and why it is essential for any business that not only wants to survive, but use insights from data or data-driven products to drive growth.
