Continuous Data Quality Monitoring with Gangesh Ganesan

Is continuous data quality monitoring a myth? Not so fast. That is according to Ganesh Gangesan, Founder & CEO of PeerNova. Traditional data quality monitoring requires data to be in a repository and data quality platforms apply certain business rules to measure the data quality. And the exceptions are referred back to the data sources/owners to fix the exceptions. PeerNova’s Cuneiform solution, with its origins in data security & networking, applies a no-code approach to solving the measurement and monitoring problem. Additionally, Gangesh talked to us about measuring the business impact of the exceptions that are identified. With a technologist background, Gangesh got an opportunity to switch to the business side when the company he was working for decided to spinoff a division. After a couple of iterations, Gangesh sold his previous company to Qualcomm. And the he embarked on the data quality journey with special focus on financial institutions.

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The Future of Women in AI is bright according to The Data Leader of The year, Adita Karkera

Even though women are 47% of the workforce, less than 28% of them are in tech and even less in senior data roles. Adita Karkera, CDO advisor and the data leader of the year with WIT, explains why there is that gap. With broad experience in state government and federal governments with data management, Adita discussed the nuances of leveraging data to impact public policy. With respect to AI, Adita believes that there is a tremendous opportunity to educate the professionals as well as the general public on AI and the benefits associated with AI. Adita also talked to us about her evolution from a small city in Allahabad, India to a data leader in the US.

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Five Types of Thinking for High Performing Data Scientists

Artificial Intelligence is all the rage currently. But there was a time when AI has gone through ‘AI Winter’ when there was not much interest in AI. Dr. Anand Rao has gone through those AI Winters. To avoid AI winter, we need to be cognizant of AI risks. Should be balance between AI innovation and risks. Should not reduce customer risk. In thos episode, Anand talks about five types of thinking that data scientists should focus on to be high performing data scientists. As the Global head of AI at PWC, Anand knows a lot about the customer uptake of AI and (un)surprisingly only 20% of the companies are actually deploying AI. Listen to the episode to find out which functions are adopting AI the most.

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Data Analytics By Design with Dr. Kirk Borne

Managing data at the speed of business versus managing business at the speed of data. Data moves the fastest so business should be moving at the speed of data. Analytics is the main beneficiary of this data. Dr. Kirk Borne has always been a scientist with jobs in data science, Astrophysics, and data analytics. After a very successful stint with NASA, George Mason University, and Booz Allen, Kirk started a new chapter with a startup that matches job seekers with companies using ML algorithms to match. This fascinating episode traverses his journey across these organizations and functions.

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Cybersecurity and AI working together to make systems safe with Dr. Madiha Jafri

Artificial Intelligence and Cybersecurity are large and complex domains each by themselves. When you combine both of them, it could be overwhelming. Dr. Madiha Jafri, Associate Fellow at Lockheed Martin for AI & Cybersecurity navigates these domains to make systems safer. In this episode. Dr. Jafri articulates how AI can help speed up cybersecurity threat detection. She also talked about her journey starting from cryptography to nanotechnology to AI. The discussed addressed challenges such as data engineering, relevance of Ops, and navigating the plethora of technologies to find that needle in a stack.

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Data Transformers Podcast

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Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.