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Data Management Trends From An Investor Perspective
Podcast |
Data Engineering Podcast
Publisher |
Tobias Macey
Media Type |
audio
Podknife tags |
Data Science, 
Interview, 
Technology
Categories Via RSS |
Technology
Publication Date |
Jun 08, 2020
Episode Duration |
00:54:58

Summary

The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking to improve their capabilities to work with data.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar to get you up and running in no time. With simple pricing, fast networking, S3 compatible object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.
  • Your host is Tobias Macey and today I’m interviewing Astasia Myers about the trends in the data industry that she sees as an investor at Redpoint Ventures

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by giving an overview of Redpoint Ventures and your role there?
  • From an investor perspective, what is most appealing about the category of data-oriented businesses?
  • What are the main sources of information that you rely on to keep up to date with what is happening in the data industry?
    • What is your personal heuristic for determining the relevance of any given piece of information to decide whether it is worthy of further investigation?
  • As someone who works closely with a variety of companies across different industry verticals and different areas of focus, what are some of the common trends that you have identified in the data ecosystem?
  • In your article that covers the trends you are keeping an eye on for 2020 you call out 4 in particular, data quality, data catalogs, observability of what influences critical business indicators, and streaming data. Taking those in turn:
    • What are the driving factors that influence data quality, and what elements of that problem space are being addressed by the companies you are watching?
      • What are the unsolved areas that you see as being viable for newcomers?
    • What are the challenges faced by businesses in establishing and maintaining data catalogs?
      • What approaches are being taken by the companies who are trying to solve this problem?
        • What shortcomings do you see in the available products?
    • For gaining visibility into the forces that impact the key performance indicators (KPI) of businesses, what is lacking in the current approaches?
      • What additional information needs to be tracked to provide the needed context for making informed decisions about what actions to take to improve KPIs?
      • What challenges do businesses in this observability space face to provide useful access and analysis to this collected data?
    • Streaming is an area that has been growing rapidly over the past few years, with many open source and commercial options. What are the major business opportunities that you see to make streaming more accessible and effective?
      • What are the main factors that you see as driving this growth in the need for access to streaming data?
  • With your focus on these trends, how does that influence your investment decisions and where you spend your time?
  • What are the unaddressed markets or product categories that you see which would be lucrative for new businesses?
  • In most areas of technology now there is a mix of open source and commercial solutions to any given problem, with varying levels of maturity and polish between them. What are your views on the balance of this relationship in the data ecosystem?
    • For data in particular, there is a strong potential for vendor lock-in which can cause potential customers to avoid adoption of commercial solutions. What has been your experience in that regard with the companies that you work with?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

  • Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary

The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking to improve their capabilities to work with data.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar to get you up and running in no time. With simple pricing, fast networking, S3 compatible object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.
  • Your host is Tobias Macey and today I’m interviewing Astasia Myers about the trends in the data industry that she sees as an investor at Redpoint Ventures

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by giving an overview of Redpoint Ventures and your role there?
  • From an investor perspective, what is most appealing about the category of data-oriented businesses?
  • What are the main sources of information that you rely on to keep up to date with what is happening in the data industry?
    • What is your personal heuristic for determining the relevance of any given piece of information to decide whether it is worthy of further investigation?
  • As someone who works closely with a variety of companies across different industry verticals and different areas of focus, what are some of the common trends that you have identified in the data ecosystem?
  • In your article that covers the trends you are keeping an eye on for 2020 you call out 4 in particular, data quality, data catalogs, observability of what influences critical business indicators, and streaming data. Taking those in turn:
    • What are the driving factors that influence data quality, and what elements of that problem space are being addressed by the companies you are watching?
      • What are the unsolved areas that you see as being viable for newcomers?
    • What are the challenges faced by businesses in establishing and maintaining data catalogs?
      • What approaches are being taken by the companies who are trying to solve this problem?
        • What shortcomings do you see in the available products?
    • For gaining visibility into the forces that impact the key performance indicators (KPI) of businesses, what is lacking in the current approaches?
      • What additional information needs to be tracked to provide the needed context for making informed decisions about what actions to take to improve KPIs?
      • What challenges do businesses in this observability space face to provide useful access and analysis to this collected data?
    • Streaming is an area that has been growing rapidly over the past few years, with many open source and commercial options. What are the major business opportunities that you see to make streaming more accessible and effective?
      • What are the main factors that you see as driving this growth in the need for access to streaming data?
  • With your focus on these trends, how does that influence your investment decisions and where you spend your time?
  • What are the unaddressed markets or product categories that you see which would be lucrative for new businesses?
  • In most areas of technology now there is a mix of open source and commercial solutions to any given problem, with varying levels of maturity and polish between them. What are your views on the balance of this relationship in the data ecosystem?
    • For data in particular, there is a strong potential for vendor lock-in which can cause potential customers to avoid adoption of commercial solutions. What has been your experience in that regard with the companies that you work with?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

  • Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary

The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking to improve their capabilities to work with data.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar to get you up and running in no time. With simple pricing, fast networking, S3 compatible object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.
  • Your host is Tobias Macey and today I’m interviewing Astasia Myers about the trends in the data industry that she sees as an investor at Redpoint Ventures

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by giving an overview of Redpoint Ventures and your role there?
  • From an investor perspective, what is most appealing about the category of data-oriented businesses?
  • What are the main sources of information that you rely on to keep up to date with what is happening in the data industry?
    • What is your personal heuristic for determining the relevance of any given piece of information to decide whether it is worthy of further investigation?
  • As someone who works closely with a variety of companies across different industry verticals and different areas of focus, what are some of the common trends that you have identified in the data ecosystem?
  • In your article that covers the trends you are keeping an eye on for 2020 you call out 4 in particular, data quality, data catalogs, observability of what influences critical business indicators, and streaming data. Taking those in turn:
    • What are the driving factors that influence data quality, and what elements of that problem space are being addressed by the companies you are watching?
      • What are the unsolved areas that you see as being viable for newcomers?
    • What are the challenges faced by businesses in establishing and maintaining data catalogs?
      • What approaches are being taken by the companies who are trying to solve this problem?
        • What shortcomings do you see in the available products?
    • For gaining visibility into the forces that impact the key performance indicators (KPI) of businesses, what is lacking in the current approaches?
      • What additional information needs to be tracked to provide the needed context for making informed decisions about what actions to take to improve KPIs?
      • What challenges do businesses in this observability space face to provide useful access and analysis to this collected data?
    • Streaming is an area that has been growing rapidly over the past few years, with many open source and commercial options. What are the major business opportunities that you see to make streaming more accessible and effective?
      • What are the main factors that you see as driving this growth in the need for access to streaming data?
  • With your focus on these trends, how does that influence your investment decisions and where you spend your time?
  • What are the unaddressed markets or product categories that you see which would be lucrative for new businesses?
  • In most areas of technology now there is a mix of open source and commercial solutions to any given problem, with varying levels of maturity and polish between them. What are your views on the balance of this relationship in the data ecosystem?
    • For data in particular, there is a strong potential for vendor lock-in which can cause potential customers to avoid adoption of commercial solutions. What has been your experience in that regard with the companies that you work with?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

  • Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

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