Alation, Inc. is hiring for Full Time Senior Product Manager - Data Management & Lineage - Redwood City, California - a Senior-level AI/ML/Data Science role offering benefits such as Career development, Competitive pay, Equity, Flex hours, Flex vacation, Health care, Startup environment

Alation continues to hire for roles at various locations with all interviewing and on-boarding done virtually due to COVID-19 crisis. At Alation, we help people find, understand, and trust data, so they not only excel in their work -- they drive value for their enterprise, team and role. In the words of one customer, "Alation makes me look like a rockstar." We help companies like Pfizer, PepsiCo, and Munich Re empower their people with the best data every day. As a platform for innovation, Alation helps customers create game-changing solutions and products (like a program for early-stage disease detection with Pfizer, or a wind farm offering a guaranteed ROI with Munich Re).

WIRE)--Alation Inc., the leader in enterprise data intelligence solutions, today announced the acquisition of Lyngo Analytics, a Los Altos, Calif.-based data insights company. The acquisition will elevate the business user experience within the data catalog, scale data intelligence, and help organizations drive data culture. Lyngo Analytics CEO and co-founder Jennifer Wu and CTO and co-founder Joachim Rahmfeld will join the company. Lyngo Analytics uses a natural language interface to empower users to discover data and insights by asking questions using simple, familiar business terms. Alation offers the most intelligent and user-friendly machine-learning data catalog on the market.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel's Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture. Please see the Roundup Of Machine Learning Forecasts And Market Estimates, 2020 for additional market research on A.I. and machine learning. The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today: Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel's Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture. The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today: Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise.

A clear majority of employees (87%) peg data quality issues as the reason their organizations failed to successfully implement AI and machine learning. That's according to Alation's latest quarterly State of Data Culture Report, produced in partnership with Wakefield Research, which also found that only 8% of data professionals believe AI is being used across their organizations. For the report, Wakefield conducted a quantitative research study of 300 data and analytics leaders at enterprises with more than 2,500 employees in the U.S., U.K., Germany, Denmark, Sweden, and Norway. The enterprises were polled regarding their progress in establishing a culture of data-driven decision-making and the challenges they continue to face. According to Alation, 87% of professionals say inherent biases in the data being used in their AI systems produce discriminatory results that create compliance risks for their organizations.

You can't do anything with data – let alone use it for machine learning – if you don't know where it is. In the age of big data, this is not a trivial matter. It is also the main driver that's propelling the rise of machine learning data catalogs, which the analysts at Forrester recently ranked and sorted. Just a word of warning: the name at the top of the list might surprise you. According to Michelle Goetz's June 21 Forrester Wave report, the percentage of analytic decision makers managing more than 1 petabyte of data (either structured, semi-structured, or unstructured) has essentially tripled from 2016 to 2017.

Pfizer Inc.'s recently-built analytics platform is helping employees from different divisions within the company collaborate in an effort to identify patients with rare diseases that might previously have gone undiagnosed, company executives said. With the help of machine-learning algorithms, the analytics platform is now yielding new insights that were previously difficult to identify in a short amount of time because there too many disparate datasets to sift through. The cloud-based Virtual Analytics Workbench tool officially launched in 2017 and more than 350 employees are using it including mainly data analysts and research and development teams. The goal is to scale it more broadly across different departments. "While data science is a critical skill set in our company, you don't need a mathematician or data science expert to use (it)," said Jeff Keisling, Pfizer's chief information officer, in an email.

The next BriefingsDirect Voice of the Customer big-data case study discussion focuses on the Tower of Babel problem for disparate data, and explores how Alation manages multiple data types by employing machine learning and crowdsourcing. We'll explore how Alation makes data more actionable via such innovative means as combining human experts and technology systems. To learn more about how enterprises and small companies alike can access more data for better analytics, please join Stephanie McReynolds, Vice-President of Marketing at Alation in Redwood City, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions. Gardner: I've heard of crowdsourcing for many things, and machine learning is more-and-more prominent with big-data activities, but I haven't necessarily seen them together.

At lunch last week, I learned that a couple colleagues were engaged in a little duel--trying to out-walk each other, as tracked by their new Fitbits. Self-improvement was definitely the goal, but seeing peer performance and benchmarks provided the required motivation to achieve that goal. If you're a data science or analytics leader, your job is to manage analysts who produce insights. So, if you want to drive your business through these insights, you have two options. You can either hire more analysts or you can increase the productivity of your existing analysts.