Data Transformers Podcast

Data Transformers Podcast

By Data Transformers PodcastScienceTechnologyEducation
Download on the App Store

Data Transformers Podcast episodes

  • Data Science career advice, future trends, and LinkedIn Top Voice Vin Vashishta
    Career advice in data science is one area of Vin Vashishta’s many published articles that got a lot of attention. Vin talked about the gap between job descriptions and actual hiring. The episode also focused on future trends and especially on adversarial machine learning that Vin Vashishta believes will be important. Vin also talked about the impact of being recognized as LinkedIn Top Voice.
    31 min
  • Data Science career advice, future trends, and LinkedIn Top Voice Vin Vashishta
    Data Transformers Podcast
    Data Science career advice, future trends, and LinkedIn Top Voice Vin Vashishta
    1x
    00:00
    /
    00:30:09
    Subscribe
    Share
    Apple Podcasts
    Google Podcasts
    Spotify
    Stitcher
    TuneIn
    RSS Feed
    Share
    Link
    Embed
    Data Science career advice, future trends, and LinkedIn Top Voice Vin Vashishta
    '
    />
    Apple Podcasts
    Google Podcasts
    Spotify
    Stitcher
    https://youtu.be/u-Dy5F0lBS0
    Episode Title: Data Science career advice, future trends, and LinkedIn Top Voice Vin Vashishta
    Episode Summary: Career advice in data science is one area of Vin Vashishta’s many published articles that got a lot of attention. Vin talked about the gap between job descriptions and actual hiring. The episode also focused on future trends and especially on adversarial machine learning that Vin Vashishta believes will be important. Vin also talked about the impact of being recognized as LinkedIn Top Voice.
    Topics discussed in this episode:
    Job description Vs. who gets hired (02:00): Job description is typically a recommendation and is poorly built. There is a gap between what the description says, who gets interviewed and who gets hired. A hiring manager will be looking for capabilities of what you can make and what you can build versus just knowledge. So the advice to job aspirants is to show capabilities and not jus knowledge.
    Vin’s evolution into research into career advice (08:26): Vin mentioned that what he works on now is not what he gets used now. So his research on careers was from few years ago and getting more attention now. Another example is his research into adversarial intelligence which he believes will be commonplace discussion in a few years.
    Future trends in AI & ML (13:38): Vin believes that adversarial machine learning or cybersecurity for AI models will be a huge topic in a couple of years. The hackers will try to inject ‘poison’ into models along with data. Or it could happen with a couple of machine learning models ‘talking’ to each other. It may even be difficult to figure out the differences between ‘collaborative’ versus ‘adversarial’ approach.
    Inequality between companies because of AI (19:55): The concept of ‘business dynamism’ where the forerunners, like Google & Facebook,  are consolidating capabilities and IP and this consolidated IP is not tricked to other companies. So companies, especially the laggard ones, need to understand their core capabilities if they want to stay relevant.
    What it means to be LinkedIn’s Top Voice (25:30): The Top Voice recognition definitely helped Vin extend his visibility and impact. With respect to business, Vin said what he writes about and what hoe does for a living are two separate things. So the Top Voice may not have contributed much to his business.
    Resources mentioned in this episode:
    The ML Rebellion blog: https://themlrebellion.com/
    Data By V-Squared: https://databyvsquared.com/ 
    Call to action:
    Connect with Vin Vashishta: https://www.linkedin.com/in/vineetvashishta/ 
    Follow Data Transformers on Twitter: @DataTransforme2 
    Listen to other Data Transformer episodes and Rate the episodes:
    Data Transformers Podcast
    Listen Now!
    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.
    Apple Podcasts
    Google Podcasts
    Spotify
    Stitcher
    31 min
  • Monetizing Machine Learning – Vin Vashishta
    Vin Vashishta is passionate about many things but most important things are: (1) Exhorting and consulting with leaders of the organizations to use Machine Learning strategically to monetize (2) Influencing the broader community using social media to make them more data science aware in many aspects (3) Delivering decision support products/services to accelerate the ML adoption.
    28 min
  • Monetizing Machine Learning - Vin Vashishta

    Data Transformers Podcast Monetizing Machine Learning - Vin Vashishta

    1x 00:00 /

    00:27:07

    Subscribe Share

     

     

     

    Apple Podcasts Google Podcasts Spotify Stitcher TuneIn RSS Feed Share Link Embed

    Monetizing Machine Learning – Vin Vashishta

    ' /> Apple Podcasts Google Podcasts Spotify Stitcher https://youtu.be/u4yv-FXu8kg Episode Title: Monetizing Machine Learning  - Vin Vashishta Guest Title: Chief Data Scientist, Data by V Squared | Editor,  The ML Rebellion, LinkedIn Top Voice 2019 Episode Summary: Vin Vashishta is passionate about many things but most important things are: (1) Exhorting and consulting with leaders of the organizations to use Machine Learning strategically to monetize (2) Influencing the broader community using social media to make them more data science aware in many aspects (3) Delivering decision support products/services to accelerate the ML adoption.

    Topics discussed in this episode:

    Vin Vashishta’s 3 ring circus (01:38): Vin’s focus is 3 fold (1) Strategic discussions with senior execs about monetizing Machine Learning for their own business (2) Engaging the community/social media about Machine Learning (3) Implementing decision support systems based on machine learning. Assessing organizations’ monetization capability (04:55): An organization has to go through the assessment of whether they are a machine learning organization first or are they going to use ML to monetize their existing products/services. An example is Netflix where Netflix uses ML (ex: recommendation engine) to drive engagement . Predictions based on the amount of data (09:50): There are 3 factors to ML. (1) Models and their effectiveness (2) Data and its context (3) Effectiveness of models against the datasets. If there is insufficient data or data of lower quality, models should be able to flag that out. Taking Covid as an example, in many cases, the models’ performance against pre-covid data may be still Ok within some context. But when the context changes, good models should be able to flag and say they can’t make predictions. Using ML models for hiring practices (15:17): Large companies like Facebook, Amazon, Google, IBM want to diversity in their workforce and are figuring out if they can use ML along the way. Unfortunately, these companies may have to undo years of infrastructure laid out first. They may initially say the candidate pool is not big enough. But that’s because they are looking at the problem incorrectly. Checking all boxes to get hired (23:58): There is this feeling from the candidate pool that they have to check all boxes to get hired. This is definitely a work in progress and the issue can’t be ignored.

    Resources mentioned in this episode:

    The ML Rebellion blog: https://themlrebellion.com/ Data By V-Squared: https://databyvsquared.com/ 

    Call to action:

    Connect with Vin Vashishta: https://www.linkedin.com/in/vineetvashishta/  Follow Data Transformers on Twitter: @DataTransforme2  Listen to other Data Transformer episodes and Rate the episodes:

     

    Data Transformers Podcast

    Listen Now!

    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. Apple Podcasts Google Podcasts Spotify Stitcher

    28 min
  • The Path To Being a Data Management Leader From Human Resources - Abel Aboh

    Data Transformers Podcast Abel Aboh's 10 Commandments of Data Management

    1x 00:00 /

    00:23:49

    Subscribe Share

     

     

     

    Apple Podcasts Google Podcasts Spotify Stitcher TuneIn RSS Feed Share Link Embed

    Abel Aboh’s 10 Commandments of Data Management

    ' /> Apple Podcasts Google Podcasts Spotify Stitcher https://youtu.be/q8tUkm5nDIU Episode Title: The path to being a Data Management Leader from Human Resources - Abel Aboh Episode Summary: Abel Aboh’s personal journey started from Nigeria but ended up inUK. On the way, many mentors helped him get a grip on human resources and later on data management & data governance. Abel believes that data will be critical in organizations so people can start a career in data anywhere. Abel also believes that CDO should not be buried under a CFO or CTO but should be given a higher pedestal given its importance. Topics discussed in this episode: From Nigeria to UK (02:06): Abel Aboh started his journey in Nigeria and settled in UK for higher studies. By participating in 2012 London Olympics, Abel got introduced to people resource management and using technology to do that. That was the first time Abel got introduced to Data and human resources can manage people data on race, gender etc. to create a very diverse environment. Importance of mentors (05:16): Abel stressed the importance of mentors and how Jean, the HR Director in his first company helped him understand the relationship between data and technology and people. Later, as he worked at BAE systems, Abel got mentored by Nicola Ashkam about data governance and data management. He also learned a lot by reading McKinsey research and other people like Caroline Cruthers and Peter Jackson etc. Being a minority in tech/data management (10:12): Abel noted the challenges of being a minority but also the responsibility of inspiring/mentoring others. Abel is deliberately working on succession plans with a representation from minority to put a diverse team together. Role and place of a Chief Data Officer (CDO) (13:11): Organizations that treat data as an important asset do not put a CDO role under another key function like CFO or CTO as that diminishes the importance. But organizations are still not there. Starting a career in data (16:40): Abel believes that roles in data will always be plentiful given the regulation about privacy and the automation that AI is driving. So Abel’s advice is to start somewhere and not worry about where. Resources mentioned in this episode:         DMBOK: Data Management Book Of Knowledge Call to action: Connect with Abel Aboh on LinkedIn: https://www.linkedin.com/in/abelaboh/ Connect with Abel Aboh on Twitter: https://twitter.com/abel_DMChampion

    Subscribe to Data Transformers podcast on iTunes: https://apple.co/3ky5LlU

     

    Data Transformers Podcast

    Listen Now!

    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. Apple Podcasts Google Podcasts Spotify Stitcher

    26 min
  • The Path To Being a Data Management Leader From Human Resources – Abel Aboh
    Abel Aboh’s personal journey started from Nigeria but ended up inUK. On the way, many mentors helped him get a grip on human resources and later on data management & data governance. Abel believes that data will be critical in organizations so people can start a career in data anywhere. Abel also believes that CDO should not be buried under a CFO or CTO but should be given a higher pedestal given its importance.
    26 min
  • Abel Aboh’s 10 Commandments of Data Management
    Abel Aboh started his professional journey in Human Resources but transformed himself into a data management leader. Over the years, Abel was able to deal with all aspects of data management like data governance & data quality and establish a framework of 10 commandments of data management. These 10 commandments helped Abel to establish data culture and increase data literacy in organizations where he worked.
    24 min
  • Abel Aboh's 10 Commandments of Data Management

    Data Transformers Podcast Draft

    1x 00:00 /

    00:23:49

    Subscribe Share

     

     

     

    Apple Podcasts Google Podcasts Spotify Stitcher TuneIn RSS Feed Share Link Embed

    Draft

    ' /> Apple Podcasts Google Podcasts Spotify Stitcher https://youtu.be/squy6EYeK6g Episode Title: Abel Aboh’s 10 commandments of Data Management Guest Title: Data Management Leader | Data Quality & Governance Expert Episode Summary: Abel Aboh started his professional journey in Human Resources but transformed himself into a data management leader. Over the years, Abel was able to deal with all aspects of data management like data governance & data quality and establish a framework of 10 commandments of data management. These 10 commandments helped Abel to establish data culture and increase data literacy in organizations where he worked. Topics discussed in this episode:

    •  Human Resources to Data Management (03:21): Abel started off in HR with BAE systems but was selected to join a team to put together data governance and data quality because of his ability to understand business needs and how technology can help.
    • Passionate about Data, People, Process, Technology (05:50): Abel believes that people with a passion for data and focus on people, process, and technology will do quite well in their data management career.
    • 10 commandments of data management (10:12): (1) Right team who understand business & technical aspects (2) Great communication (3) Change management (4) Relevant framework (5) Blueprint/Enterprise view (6) Technology (7) Sponsorship (8) Accessibility (9) Active listening (10) Support
    • Importance of data literacy (14:55): The essence of the 10 commandments is to increase data literacy  and establish a data culture in organizations.
    • Data quality (18:34): Taking BAE systems as an example, the primary business goal was delivering projects on time. To do that, needed to make sure that the data they were dealing with was of the highest quality. Without that, there was no guarantee that projects would be delivered as projected.
    • Data maturity and data cycle (21:11): Many industries in UK had to deal with massive influx of data over the last few years. And not all companies were in the same position to deal with this influx of data. Alos, they needed to deal with tracking the data across the overall data cycle from initial capture to all the way to archiving it.

      • Call to action: Connect with Abel Aboh on LinkedIn: https://www.linkedin.com/in/abelaboh/  Connect with Abel Aboh on Twitter: https://twitter.com/abel_DMChampion Subscribe to Data Transformers podcast on iTunes: https://apple.co/3ky5LlU

       

      Data Transformers Podcast

      Listen Now!

      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. Apple Podcasts Google Podcasts Spotify Stitcher

      24 min
    • Building AI-driven insurance policy review startup – Chris Cheatham
      Chris Cheatham is a lawyer who transformed into an entrepreneur focusing on AIML enabled insurance policy review software. He stumbled into starting his own company after experiencing laborious manual document reviews. His initial product of claims review SW was enhanced to become insurance policy review SW with AIML. His company Risk Genius was recently acquired by BOLD Penguin. The episode not only covers the specifics of data labelling, AIML models that relate to the insurance industry but also his entrepreneurial journey.
      35 min
    • Building AI-driven insurance policy review startup - Chris Cheatham

      Data Transformers Podcast Building AI-driven insurance policy review startup - Chris Cheatham

      1x 00:00 /

      00:34:05

      Subscribe Share

       

       

       

      Apple Podcasts Google Podcasts Spotify Stitcher TuneIn RSS Feed Share Link Embed

      Building AI-driven insurance policy review startup – Chris Cheatham

      ' /> Apple Podcasts Google Podcasts Spotify Stitcher https://youtu.be/V7sZUOutGFY Episode Title : 40+ years of data experience in 30 minutes with Peggy Tsai and Ramesh Dontha Episode Summary: The kickoff episode goes into the professional and personal lives of the co-hosts Peggy Tsai and Ramesh Dontha. Who are they? What have they been doing before the podcast? Why do they want to do a podcast? Where did they meet? What do they want to accomplish with Data Transformers’ Podcast? Topics discussed in this episode:

      • Automated process to review legal documents (3m:00s): The pain of having to pore over hundreds of claim documents manually and issues with existing software forced Chris and team to build their own software using machine learning techniques.
      • Customer feedback for first pivot (5m:00s): Customer feedback helped Chris and team to pivot their company to develop an insurance policy review software from what used to be an insurance claims software.
      • Need for AIML software for policy reviews (9m:00s): Polciy holders don’t usually know what is covered in their policy or not covered. Underwriters also don’t what their competitors are covering. AIML with NLP will go thru all these documents and different terminologies and quickly summarize the findings.
      • How a lawyer pivoted into AIML software? (12M:00s): Chris learned about Technology Assisted Review (TAR) and how it helped sift through a large volume of documents. Also, the outsourcing of work to cheaper places forced Chris to think about using AIML to enhance TAR capabilities and also do the work cheaper.
      • Why classification and data tagging is important (16M:00s): Chris noticed that most of the insurance taxonomy is not classified/tagged and he took that opportunity to label the data properly. The more data they labelled, the more valuable the company Risk Genius became.
      • Going vertical or horizontal (19m:00s): Chris talks about the differences of going vertical by digging deep into commercial insurance versus horizontal across other industries with their technology. He prefers to go deeper as there is plenty of opportunity there.
      • LinkedIn influencer: How (22m: 00s): Chris has a large following on LinkedIn and he talked about how he acquired the following. He talks about publishing regularly on insurance and how LinkedIn reached out to him to make him an influencer. He mixes fun stuff with professional to engage with his audience.
      • Influencers in his own life (29m:00s): Seth Godin was the major influencer for Chris. His marketing program really helped him. Chris wants to focus more on written word and focus on a minimum viable audience.

        • Resources mentioned in this episode: Podcast website: https://DataTransformersPodcast.Com Ramesh consulting website: https://DigitalTransformationPro,COm

         

        Data Transformers Podcast

        Listen Now!

        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. Apple Podcasts Google Podcasts Spotify Stitcher

        35 min

      About Data Transformers Podcast

      From the publisher's feed

      The primary goal of Data Transformers podcast is to accelerate digital transformation by bridging the gap between business goals and technology initiatives using Data as glue. Visit https://DataTransformersPodcast.Com for more details.