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Mapping The Customer Journey For B2B Companies At Dreamdata
Podcast |
Data Engineering Podcast
Publisher |
Tobias Macey
Media Type |
audio
Podknife tags |
Data Science, 
Interview, 
Technology
Categories Via RSS |
Technology
Publication Date |
May 25, 2020
Episode Duration |
00:46:59

Summary

Gaining a complete view of the customer journey is especially difficult in B2B companies. This is due to the number of different individuals involved and the myriad ways that they interface with the business. Dreamdata integrates data from the multitude of platforms that are used by these organizations so that they can get a comprehensive view of their customer lifecycle. In this episode Ole Dallerup explains how Dreamdata was started, how their platform is architected, and the challenges inherent to data management in the B2B space. This conversation is a useful look into how data engineering and analytics can have a direct impact on the success of the business.

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 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. 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 Ole Dallerup about Dreamdata, a platform for simplifying data integration for B2B companies

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing what you are building at Dreamata?
    • What was your inspiration for starting a company and what keeps you motivated?
  • How do the data requirements differ between B2C and B2B companies?
  • What are the challenges that B2B companies face in gaining visibility across the lifecycle of their customers?
    • How does that lack of visibility impact the viability or growth potential of the business?
    • What are the factors that contribute to silos in visibility of customer activity within a business?
  • What are the data sources that you are dealing with to generate meaningful analytics for your customers?
  • What are some of the challenges that business face in either generating or collecting useful information about their customer interactions?
  • How is the technical platform of Dreamdata implemented and how has it evolved since you first began working on it?
  • What are some of the ways that you approach entity resolution across the different channels and data sources?
  • How do you reconcile the information collected from different sources that might use disparate data formats and representations?
  • What is the onboarding process for your customers to identify and integrate with all of their systems?
  • How do you approach the definition of the schema model for the database that your customers implement for storing their footprint?
    • Do you allow for customization by the customer?
    • Do you rely on a tool such as DBT for populating the table definitions and transformations from the source data?
  • How do you approach representation of the analysis and actionable insights to your customers so that they are able to accurately intepret the results?
  • How have your own experiences at Dreamdata influenced the areas that you invest in for the product?
  • What are some of the most interesting or surprising insights that you have been able to gain as a result of the unified view that you are building?
  • What are some of the most challenging, interesting, or unexpected lessons that you have learned from building and growing the technical and business elements of Dreamdata?
  • When might a user be better served by building their own pipelines or analysis for tracking their customer interactions?
  • What do you have planned for the future of Dreamdata?
  • What are some of the industry trends that you are keeping an eye on and what potential impacts to your business do you anticipate?

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

Gaining a complete view of the customer journey is especially difficult in B2B companies. This is due to the number of different individuals involved and the myriad ways that they interface with the business. Dreamdata integrates data from the multitude of platforms that are used by these organizations so that they can get a comprehensive view of their customer lifecycle. In this episode Ole Dallerup explains how Dreamdata was started, how their platform is architected, and the challenges inherent to data management in the B2B space. This conversation is a useful look into how data engineering and analytics can have a direct impact on the success of the business.

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 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. 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 Ole Dallerup about Dreamdata, a platform for simplifying data integration for B2B companies

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing what you are building at Dreamata?
    • What was your inspiration for starting a company and what keeps you motivated?
  • How do the data requirements differ between B2C and B2B companies?
  • What are the challenges that B2B companies face in gaining visibility across the lifecycle of their customers?
    • How does that lack of visibility impact the viability or growth potential of the business?
    • What are the factors that contribute to silos in visibility of customer activity within a business?
  • What are the data sources that you are dealing with to generate meaningful analytics for your customers?
  • What are some of the challenges that business face in either generating or collecting useful information about their customer interactions?
  • How is the technical platform of Dreamdata implemented and how has it evolved since you first began working on it?
  • What are some of the ways that you approach entity resolution across the different channels and data sources?
  • How do you reconcile the information collected from different sources that might use disparate data formats and representations?
  • What is the onboarding process for your customers to identify and integrate with all of their systems?
  • How do you approach the definition of the schema model for the database that your customers implement for storing their footprint?
    • Do you allow for customization by the customer?
    • Do you rely on a tool such as DBT for populating the table definitions and transformations from the source data?
  • How do you approach representation of the analysis and actionable insights to your customers so that they are able to accurately intepret the results?
  • How have your own experiences at Dreamdata influenced the areas that you invest in for the product?
  • What are some of the most interesting or surprising insights that you have been able to gain as a result of the unified view that you are building?
  • What are some of the most challenging, interesting, or unexpected lessons that you have learned from building and growing the technical and business elements of Dreamdata?
  • When might a user be better served by building their own pipelines or analysis for tracking their customer interactions?
  • What do you have planned for the future of Dreamdata?
  • What are some of the industry trends that you are keeping an eye on and what potential impacts to your business do you anticipate?

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

Gaining a complete view of the customer journey is especially difficult in B2B companies. This is due to the number of different individuals involved and the myriad ways that they interface with the business. Dreamdata integrates data from the multitude of platforms that are used by these organizations so that they can get a comprehensive view of their customer lifecycle. In this episode Ole Dallerup explains how Dreamdata was started, how their platform is architected, and the challenges inherent to data management in the B2B space. This conversation is a useful look into how data engineering and analytics can have a direct impact on the success of the business.

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 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. 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 Ole Dallerup about Dreamdata, a platform for simplifying data integration for B2B companies

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing what you are building at Dreamata?
    • What was your inspiration for starting a company and what keeps you motivated?
  • How do the data requirements differ between B2C and B2B companies?
  • What are the challenges that B2B companies face in gaining visibility across the lifecycle of their customers?
    • How does that lack of visibility impact the viability or growth potential of the business?
    • What are the factors that contribute to silos in visibility of customer activity within a business?
  • What are the data sources that you are dealing with to generate meaningful analytics for your customers?
  • What are some of the challenges that business face in either generating or collecting useful information about their customer interactions?
  • How is the technical platform of Dreamdata implemented and how has it evolved since you first began working on it?
  • What are some of the ways that you approach entity resolution across the different channels and data sources?
  • How do you reconcile the information collected from different sources that might use disparate data formats and representations?
  • What is the onboarding process for your customers to identify and integrate with all of their systems?
  • How do you approach the definition of the schema model for the database that your customers implement for storing their footprint?
    • Do you allow for customization by the customer?
    • Do you rely on a tool such as DBT for populating the table definitions and transformations from the source data?
  • How do you approach representation of the analysis and actionable insights to your customers so that they are able to accurately intepret the results?
  • How have your own experiences at Dreamdata influenced the areas that you invest in for the product?
  • What are some of the most interesting or surprising insights that you have been able to gain as a result of the unified view that you are building?
  • What are some of the most challenging, interesting, or unexpected lessons that you have learned from building and growing the technical and business elements of Dreamdata?
  • When might a user be better served by building their own pipelines or analysis for tracking their customer interactions?
  • What do you have planned for the future of Dreamdata?
  • What are some of the industry trends that you are keeping an eye on and what potential impacts to your business do you anticipate?

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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