• The top ten strategic data and analytics technology trends and what they enable. How these trends enable you to build an intelligent and emergent data and analytics portfolio of capabilities that scale to the needs of digital business. Why these trends are growing and having an impact now. How these trends will change your organization, data and analytics program and skills needed. Strategic technology trends have significant disruptive potential over the next 5 years. You must examine your business impacts of these trends and appropriately adjust investments, business models and operations or else your company is at risk of losing competitive advantage to those who do. Data and analytics leaders cannot afford to ignore these 2019 top data and analytics trends.
Currently, the Cleveland Clinic Biorepository is an assembly of several biobanks together with the Lerner Research Institute. Excel spreadsheets track almost everything – and there are no universal LIS, SOPs, universal, centralized freezer monitoring, or means to electronically track patient tissue. The Cleveland Clinic is embarking on an enterprise-wide initiative to accelerate its biobanking capacity, leveraging its clinical volume and disease expertise. As part of this initiative, the Cleveland Clinic is partnering with Brooks Life Sciences to build a state of the art, 21,000 square-foot structure biobanking facility, set to open in the summer of 2019.
What is product analytics and how does it differ from marketing/web analytics or even business intelligence? Which option answers the questions you’re trying to ask? Just why, and how are digital-first businesses using product analytics?
Search capability is ingrained into our daily life. How many arguments these days are settled with the conclusion, “Just Google it”??! We all expect some type of search functionality in every application and website. Meanwhile, advances in computer vision, natural language processing, large language models, and generative AI have made it possible to extract semantic properties from unstructured data in the form of vector embeddings.
It can be daunting to query this kind of data, which combines K-Nearest Neighbors algorithms and lexical search, unless you have the right tool for the job. When you fail, performance suffers. Web users typically expect search results under one second.