For chief data officers and data scientists, the business case for DataOps can be obvious. DataOps, correctly done, can streamline data workflows, reduce errors, and offers transparency to the entire data operations. It improves efficiency, increases data trust, and gives more time to do analysis. For business executives, such benefits are not immediately apparent. So, getting the budget to build your DataOps can run into snags -- right up until a business problem challenges your company's core value proposition. That's what happened for Screenrights.

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?

AI Daily Roundup starts today! We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence, Machine Learning, Robotic Process Automation, Fintech and human-system interactions. We will cover the role of AI Daily Roundup and their application in various industries and daily lives. Intel Federal LLC announced a three-year agreement with Sandia National Laboratories (Sandia) to explore the value of neuromorphic computing for scaled-up computational problems.

Machine learning is transforming the way an Air Force office analyzes and certifies new flight configurations. The Air Force SEEK EAGLE Office sets standards for safe flight configurations by testing and looking at historical data to see how different stores--like a weapon system attached to an F-16--affect flight. A project AFSEO developed along with industry partners can now automate up to 80% of requests for analysis, according to the office's Chief Data Officer Donna Cotton. "The application is kind of like an eager junior engineer consulting a senior engineer," Cotton said. "It makes the straightforward calls without any input, but in the hard cases it walks into the senior engineer's office and says: 'Hey, I did a bunch of research and this is what I found out. Can you give me your opinion?'" Cotton spoke at a Tuesday webinar hosted by Tamr, one of the industry partners involved in the project.

Data prepper Tamr Inc. will assist the U.S. Air Force in boosting utilization of its air assets under a five-year contract designed to use machine learning techniques to accelerate the flight certification process for new aircraft configurations. Those configurations include equipping front-line aircraft with new weapons, sensors and defenses such as electronic warfare pods. Tamr said the contract with the Air Force's Seek Eagle Office could be worth as much $60 million. The office based at Eglin Air Force Base, Fla., is responsible for integration new technologies into front-line aircraft. The Air Force office will use Tamr's machine learning platform to organize more than 30 years of aircraft performance studies dispersed across the organization.

After a successful early career in R&D in Silicon Valley, I spent 12 years working as a carpenter. This may sound like a big U-turn. But, while I loved the intellectual piece of science, I really loved the people aspect of construction. I got to build something and turn raw materials into gratifying, highly visible results: houses that enabled life and buildings that enabled commerce. I get the same kind of rush daily as lead data-ops engineer for Life Sciences at Tamr.*

You have probably experienced a simplified version of low-latency matching in action. When starting the checkout process at many popular online marketplaces you start typing your address, and the address bar starts showing suggestions to autocomplete your address, even if you have never shopped at the site before. This approach is low-latency, as the autofill suggestion shows up nearly-instantaneously, but it does not use any sophisticated matching on the back-end which means that a single-letter deviation from the correct street spelling will no longer yield the same suggestions. It is near-instantaneous for a reason – the entry that you started to type is matched one-to-one to existing records to find a match, and if no match is found, it simply shows nothing. This is where machine learning comes into play – allowing the more sophisticated matching to happen on the back-end, while still allowing near-instantaneous matching.

Every manufacturer I have engaged with in the past 10 years claims to be data-driven and almost all of them are investing in "Digital Transformation and Industry 4.0" initiatives. These initiatives have business-critical goals like better asset utilization, inventory reduction, improving quality and throughput or optimizing their supply chain. The required Key Performance Indicators (KPIs) like OEE, MTTF, OTD, FPY are all well understood by Kaizen teams ready with a commitment to continuous improvement. Given that much of manufacturing is digitized and with the adoption of IoT on top of "smarter" machines, the stage appears to be set for manufacturing analytics to provide the critical insights every business seeks. The unfortunate reality is that almost all of these analytics-driven initiatives stay in the crawl-stage for too long and fail to deliver on the promise in a reasonable period of time.

Despite improvements in technology, implementation of master data management (MDM) solutions have long been a known pain for many organizations pushing to improve data quality and competency. The source of this pain is often due to the fact that traditional MDM solutions solve the data mastering problem using deterministic, rule-based approaches that do not easily accommodate nor scale for the increasing flow of messy, diverse data coming from disparate data systems. Faster technology has not been able to remove this pain, but it can be relieved with a fresh approach to MDM. In previous blog posts, my colleagues have examined in detail this new approach while explaining the need for organizations to adopt anagile approach to the data mastering problem, as well as why this approach is critical to anorganization's digital transformation. Tamr's API-driven, machine learning capability makes agile data mastering possible as it fundamentally changes the way we can tackle the data mastering problem.

The Tech Tribune staff has compiled the very best tech startups in Cambridge, Massachusetts. Additionally, all companies must be independent (un-acquired), privately owned, at most 10 years old, and have received at least one round of funding in order to qualify. Looking for a badge to celebrate your awesome accomplishment? "Cambridge Mobile Telematics (CMT) pioneered telematics for usage-based and behavior-based programs making roads and drivers safer around the world. Founded in 2010 by two MIT professors, CMT's accomplished team of expert scientists and experienced entrepreneurs developed DriveWell, an advanced mobile-sensing and big data platform delivering an end-to-end smartphone telematics solution. DriveWell provides valuable feedback to users, helping them to improve driving performance and become more aware of unsafe behaviors. DriveWell is the first telematics platform in the industry to provide both traditional vehicle-centric, usage-based-insurance (UBI) and driver-centric, behavior-based insurance (BBI) solutions. Through the DriveWell program, CMT's partners can easily measure mileage, time of day, roadways and risky driving behaviors – giving them a complete picture of every trip and allowing them to segment high-risk vs low-risk customers easily."