Artificial intelligence and machine learning already deliver plenty of practical value to enterprises, from fraud detection to chatbots to predictive analytics. But the audacious creative writing skills of ChatGPT have raised expectations for AI/ML to new heights. IT leaders can't help but wonder: Could AI/ML finally be ready to go beyond point solutions and address core enterprise problems? Take the biggest, oldest, most confounding IT problem of all: Managing and integrating data across the enterprise. Today, that endeavor cries out for help from AI/ML technologies, as the volume, variety, variability, and distribution of data across on-prem and cloud platforms climb an endless exponential curve.

All the sessions from Transform 2021 are available on-demand now. "An underlying issue that most enterprise organizations struggle with is that their data is a disaster," noted Anthony Deighton, chief product officer at AI-powered data unification company Tamr. Deighton was moderating a panel at VentureBeat's Transform 2021 event today, which delved into practical and academic perspectives on how companies -- particularly financial institutions -- can use machine learning (ML) to improve the quality and reliability of their data. Deighton was joined by Tamr cofounder Michael Stonebraker, winner of the 2015 Turing award and a renowned computer scientist who specializes in database research; and Jonathan Holman, head of digital transformation at financial services company Santander U.K., a Tamr customer. So what is the problem that Tamr, ultimately, is setting out to solve?

As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical "internet of things" devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data. But drawing meaningful conclusions from that data is a difficult problem that can involve piecing together different data types and manipulating huge data sets in response to varying scientific inquiries. The problem is as much about computer science as it is about other areas of science. That's where Paradigm4 comes in. The company, founded by Marilyn Matz SM '80 and Turing Award winner and MIT Professor Michael Stonebraker, helps pharmaceutical companies, research institutes, and biotech companies turn data into insights.

As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical "internet of things" devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data. But drawing meaningful conclusions from that data is a difficult problem that can involve piecing together different data types and manipulating huge data sets in response to varying scientific inquiries. The problem is as much about computer science as it is about other areas of science. That's where Paradigm4 comes in. The company, founded by Marilyn Matz SM '80 and Turing Award winner and MIT Professor Michael Stonebraker, helps pharmaceutical companies, research institutes, and biotech companies turn data into insights.

ODSC events for 2019 are all wrapped up, and now it's time to start thinking and planning for the next decade. Our first major event of 2020 will be ODSC East April 13-17 in Boston, MA, and we're excited to announce the first fifty speakers out of 280 schedule. ODSC East 2020 is expected to host over 250 speakers. Tom Mitchell is known as the "Father of Machine Learning" having founded the Machine Learning Department at Carnegie Mellon University and led it as Department Head for its first 10 years, teaching many famous students including Andrew Ng. He is a world-renowned researcher in machine learning, artificial intelligence, and cognitive neuroscience.

Why do so many companies still struggle to build a smooth-running pipeline from data to insights? They invest in heavily hyped machine-learning algorithms to analyze data and make business predictions. Then, inevitably, they realize that algorithms aren't magic; if they're fed junk data, their insights won't be stellar. So they employ data scientists that spend 90% of their time washing and folding in a data-cleaning laundromat, leaving just 10% of their time to do the job for which they were hired. What is flawed about this process is that companies only get excited about machine learning for end-of-the-line algorithms; they should apply machine learning just as liberally in the early cleansing stages instead of relying on people to grapple with gargantuan data sets, according to Andy Palmer, co-founder and chief executive officer of Tamr Inc., which helps organizations use machine learning to unify their data silos.

This is the first in Blue Hill Research's occasional blog series "Questioning Authority with Toph Whitmore." As co-founder (with friend Michael Stonebraker) of Vertica, Andy Palmer ambitiously sought nothing less than to reinvent the database. In 2013, he and Stonebraker moved up the data value chain and founded Tamr, the Cambridge, MA-based software company aiming to provide a unified view of data in the modern enterprise. Palmer joined me for a discussion in which he talked Tamr, predicted the future of enterprise data management, and introduced a rather colorful (yet apt) analogy of which, he admits, his marketing team is less than fond. TOPH WHITMORE: Tell me about the genesis of Tamr.