How AI is changing personal data tracking

Every industry, from oil & gas to agriculture to chemicals & materials is undergoing a digital transformation. Whether it be keeping up with a changing market or optimizing internal operations, tracking and implementing the right emerging technology for your organization is no small feat.

At Udeh''s HUB, we help innovators find the right technologies and companies to bet on. This world has all kinds of tools and frameworks: gate processes, innovation funnels, idea managers, databases, and more. In recent years, I’ve had the sneaking suspicion that as we continue to add these tools, we are simply adding to the list and bloating the process, rather that replacing old methods with newer, and more effective ones. Take data, for instance: There is a ton of “innovation” data out there – news, investments, patents, papers, and more – and new platforms to serve up this data to you pop up seemingly every day. Sourcing the data isn’t the real problem; unifying and making the most of the data to identify weak signals and make the appropriate bets is.

This is why I am excited about artificial intelligence (AI) and machine learning (ML).

Few need convincing that AI/ML is disruptive. I just punched “AI and innovation” into Google, and on the first page alone I found trillion-dollar forecasts, thought pieces, book chapters, and events dedicated to the topic. Having advised clients on the impact of AI on their businesses, I’ve found that most of the public discussion has been severely lacking in real uses cases that people can act on. As I’m tasked with improving Lux’s core product and have access to a talented data science and software team, I’ve been fortunate to apply AI to real problems that my colleagues and I face in supporting our clients day to day.

As the bulk of the innovation data available is unstructured text, the natural tool set to explore is natural language processing (NLP). This broad bucket of analytical techniques underpins some ubiquitous platforms like Amazon Alexa or even Microsoft spell check. There have been tremendous advances in this field in a very short time, and any talented data scientist/programmer can access numerous libraries to try out the latest and greatest algorithms.