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131 - 15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) with Brian T. O’Neill

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Content provided by Brian T. O’Neill from Designing for Analytics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Brian T. O’Neill from Designing for Analytics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This week I’m covering Part 1 of the 15 Ways to Increase User Adoption of Data Products, which is based on an article I wrote for subscribers of my mailing list. Throughout this episode, I describe why focusing on empathy, outcomes, and user experience leads to not only better data products, but also better business outcomes. The focus of this episode is to show you that it’s completely possible to take a human-centered approach to data product development without mandating behavioral changes, and to show how this approach benefits not just end users, but also the businesses and employees creating these data products.

Highlights/ Skip to:

  • Design behavior change into the data product. (05:34)
  • Establish a weekly habit of exposing technical and non-technical members of the data team directly to end users of solutions - no gatekeepers allowed. (08:12)
  • Change funding models to fund problems, not specific solutions, so that your data product teams are invested in solving real problems. (13:30)
  • Hold teams accountable for writing down and agreeing to the intended benefits and outcomes for both users and business stakeholders. Reject projects that have vague outcomes defined. (16:49)
  • Approach the creation of data products as “user experiences” instead of a “thing” that is being built that has different quality attributes. (20:16)
  • If the team is tasked with being “innovative,” leaders need to understand the innoficiency problem, shortened iterations, and the importance of generating a volume of ideas (bad and good) before committing to a final direction. (23:08)
  • Co-design solutions with [not for!] end users in low, throw-away fidelity, refining success criteria for usability and utility as the solution evolves. Embrace the idea that research/design/build/test is not a linear process. (28:13)
  • Test (validate) solutions with users early, before committing to releasing them, but with a pre-commitment to react to the insights you get back from the test. (31:50)

Links:

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103 episodes

Artwork
iconShare
 
Manage episode 386248204 series 2938687
Content provided by Brian T. O’Neill from Designing for Analytics. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Brian T. O’Neill from Designing for Analytics or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This week I’m covering Part 1 of the 15 Ways to Increase User Adoption of Data Products, which is based on an article I wrote for subscribers of my mailing list. Throughout this episode, I describe why focusing on empathy, outcomes, and user experience leads to not only better data products, but also better business outcomes. The focus of this episode is to show you that it’s completely possible to take a human-centered approach to data product development without mandating behavioral changes, and to show how this approach benefits not just end users, but also the businesses and employees creating these data products.

Highlights/ Skip to:

  • Design behavior change into the data product. (05:34)
  • Establish a weekly habit of exposing technical and non-technical members of the data team directly to end users of solutions - no gatekeepers allowed. (08:12)
  • Change funding models to fund problems, not specific solutions, so that your data product teams are invested in solving real problems. (13:30)
  • Hold teams accountable for writing down and agreeing to the intended benefits and outcomes for both users and business stakeholders. Reject projects that have vague outcomes defined. (16:49)
  • Approach the creation of data products as “user experiences” instead of a “thing” that is being built that has different quality attributes. (20:16)
  • If the team is tasked with being “innovative,” leaders need to understand the innoficiency problem, shortened iterations, and the importance of generating a volume of ideas (bad and good) before committing to a final direction. (23:08)
  • Co-design solutions with [not for!] end users in low, throw-away fidelity, refining success criteria for usability and utility as the solution evolves. Embrace the idea that research/design/build/test is not a linear process. (28:13)
  • Test (validate) solutions with users early, before committing to releasing them, but with a pre-commitment to react to the insights you get back from the test. (31:50)

Links:

  continue reading

103 episodes

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