Artwork

Brian T. O’Neill from Designing for Analytics에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Brian T. O’Neill from Designing for Analytics 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Player FM -팟 캐스트 앱
Player FM 앱으로 오프라인으로 전환하세요!

137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen

44:50
 
공유
 

Manage episode 401894504 series 2527129
Brian T. O’Neill from Designing for Analytics에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Brian T. O’Neill from Designing for Analytics 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

This week, I'm chatting with Karen Meppen, a founding member of the Data Product Leadership Community and a Data Product Architect and Client Services Director at Hakkoda. Today, we're tackling the difficult topic of developing data products in situations where a product-oriented culture and data infrastructures may still be emerging or “at odds” with a human-centered approach. Karen brings extensive experience and a strong belief in how to effectively negotiate the early stages of data maturity. Together we look at the major hurdles that businesses encounter when trying to properly exploit data products, as well as the necessity of leadership support and strategy alignment in these initiatives. Karen's insights offer a roadmap for those seeking to adopt a product and UX-driven methodology when significant tech or cultural hurdles may exist.

Highlights/ Skip to:

  • I Introduce Karen Meppen and the challenges of dealing with data products in places where the data and tech aren't quite there yet (00:00)
  • Karen shares her thoughts on what it's like working with "immature data" (02:27)
  • Karen breaks down what a data product actually is (04:20)
  • Karen and I discuss why having executive buy-in is crucial for moving forward with data products (07:48)
  • The sometimes fuzzy definition of "data products." (12:09)
  • Karen defines “shadow data teams” and explains how they sometimes conflict with tech teams (17:35)
  • How Karen identifies the nature of each team to overcome common hurdles of connecting tech teams with business units (18:47)
  • How she navigates conversations with tech leaders who think they already understand the requirements of business users (22:48)
  • Using design prototypes and design reviews with different teams to make sure everyone is on the same page about UX (24:00)
  • Karen shares stories from earlier in her career that led her to embrace human-centered design to ensure data products actually meet user needs (28:29)
  • We reflect on our chat about UX, data products, and the “producty” approach to ML and analytics solutions (42:11)
Quotes from Today’s Episode
  • "It’s not really fair to get really excited about what we hear about or see on LinkedIn, at conferences, etc. We get excited about the shiny things, and then want to go straight to it when [our] organization [may not be ] ready to do that, for a lot of reasons." - Karen Meppen (03:00)
  • "If you do not have support from leadership and this is not something [they are] passionate about, you probably aren’t a great candidate for pursuing data products as a way of working." - Karen Meppen (08:30)
  • "Requirements are just friendly lies." - Karen, quoting Brian about how data teams need to interpret stakeholder requests (13:27)
  • "The greatest challenge that we have in technology is not technology, it’s the people, and understanding how we’re using the technology to meet our needs." - Karen Meppen (24:04)
  • "You can’t automate something that you haven’t defined. For example, if you don’t have clarity on your tagging approach for your PII, or just the nature of all the metadata that you’re capturing for your data assets and what it means or how it’s handled—to make it good, then how could you possibly automate any of this that hasn’t been defined?" - Karen Meppen (38:35)
  • "Nothing upsets an end-user more than lifting-and-shifting an existing report with the same problems it had in a new solution that now they’ve never used before." - Karen Meppen (40:13)
  • “Early maturity may look different in many ways depending upon the nature of business you’re doing, the structure of your data team, and how it interacts with folks.” (42:46)
Links
  continue reading

113 에피소드

Artwork
icon공유
 
Manage episode 401894504 series 2527129
Brian T. O’Neill from Designing for Analytics에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Brian T. O’Neill from Designing for Analytics 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

This week, I'm chatting with Karen Meppen, a founding member of the Data Product Leadership Community and a Data Product Architect and Client Services Director at Hakkoda. Today, we're tackling the difficult topic of developing data products in situations where a product-oriented culture and data infrastructures may still be emerging or “at odds” with a human-centered approach. Karen brings extensive experience and a strong belief in how to effectively negotiate the early stages of data maturity. Together we look at the major hurdles that businesses encounter when trying to properly exploit data products, as well as the necessity of leadership support and strategy alignment in these initiatives. Karen's insights offer a roadmap for those seeking to adopt a product and UX-driven methodology when significant tech or cultural hurdles may exist.

Highlights/ Skip to:

  • I Introduce Karen Meppen and the challenges of dealing with data products in places where the data and tech aren't quite there yet (00:00)
  • Karen shares her thoughts on what it's like working with "immature data" (02:27)
  • Karen breaks down what a data product actually is (04:20)
  • Karen and I discuss why having executive buy-in is crucial for moving forward with data products (07:48)
  • The sometimes fuzzy definition of "data products." (12:09)
  • Karen defines “shadow data teams” and explains how they sometimes conflict with tech teams (17:35)
  • How Karen identifies the nature of each team to overcome common hurdles of connecting tech teams with business units (18:47)
  • How she navigates conversations with tech leaders who think they already understand the requirements of business users (22:48)
  • Using design prototypes and design reviews with different teams to make sure everyone is on the same page about UX (24:00)
  • Karen shares stories from earlier in her career that led her to embrace human-centered design to ensure data products actually meet user needs (28:29)
  • We reflect on our chat about UX, data products, and the “producty” approach to ML and analytics solutions (42:11)
Quotes from Today’s Episode
  • "It’s not really fair to get really excited about what we hear about or see on LinkedIn, at conferences, etc. We get excited about the shiny things, and then want to go straight to it when [our] organization [may not be ] ready to do that, for a lot of reasons." - Karen Meppen (03:00)
  • "If you do not have support from leadership and this is not something [they are] passionate about, you probably aren’t a great candidate for pursuing data products as a way of working." - Karen Meppen (08:30)
  • "Requirements are just friendly lies." - Karen, quoting Brian about how data teams need to interpret stakeholder requests (13:27)
  • "The greatest challenge that we have in technology is not technology, it’s the people, and understanding how we’re using the technology to meet our needs." - Karen Meppen (24:04)
  • "You can’t automate something that you haven’t defined. For example, if you don’t have clarity on your tagging approach for your PII, or just the nature of all the metadata that you’re capturing for your data assets and what it means or how it’s handled—to make it good, then how could you possibly automate any of this that hasn’t been defined?" - Karen Meppen (38:35)
  • "Nothing upsets an end-user more than lifting-and-shifting an existing report with the same problems it had in a new solution that now they’ve never used before." - Karen Meppen (40:13)
  • “Early maturity may look different in many ways depending upon the nature of business you’re doing, the structure of your data team, and how it interacts with folks.” (42:46)
Links
  continue reading

113 에피소드

모든 에피소드

×
 
Loading …

플레이어 FM에 오신것을 환영합니다!

플레이어 FM은 웹에서 고품질 팟캐스트를 검색하여 지금 바로 즐길 수 있도록 합니다. 최고의 팟캐스트 앱이며 Android, iPhone 및 웹에서도 작동합니다. 장치 간 구독 동기화를 위해 가입하세요.

 

빠른 참조 가이드