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

Experiencing Data w/ Brian T. O’Neill - Data Products, Product Management, & UX Design

Το περιεχόμενο παρέχεται από το Brian T. O’Neill from Designing for Analytics. Όλο το περιεχόμενο podcast, συμπεριλαμβανομένων των επεισοδίων, των γραφικών και των περιγραφών podcast, μεταφορτώνεται και παρέχεται απευθείας από τον Brian T. O’Neill from Designing for Analytics ή τον συνεργάτη της πλατφόρμας podcast. Εάν πιστεύετε ότι κάποιος χρησιμοποιεί το έργο σας που προστατεύεται από πνευματικά δικαιώματα χωρίς την άδειά σας, μπορείτε να ακολουθήσετε τη διαδικασία που περιγράφεται εδώ https://el.player.fm/legal.

7M ago 36:57

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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:

15 Ways to Increase Adoption of Data Products: https://designingforanalytics.com/resources/15-ways-to-increase-adoption-of-data-products-using-techniques-from-ux-design-product-management-and-beyond/
Company website: https://designingforanalytics.com
Episode 54: https://designingforanalytics.com/resources/episodes/054-jared-spool-on-designing-innovative-ml-ai-and-analytics-user-experiences/
Episode 106: https://designingforanalytics.com/resources/episodes/106-ideaflow-applying-the-practice-of-design-and-innovation-to-internal-data-products-w-jeremy-utley/
Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-Metric-Matters/dp/0593420586/
Podcast website: https://designingforanalytics.com/podcast

113 επεισόδια

#Tech #Business #MBA #Brian T. O’Neill from Designing for Analytics #Arts #Graphic Design #Data Products