Next DPLC Live Webinar & Discussion @ 1pm ET on Feb 27, 2024
The Data Product Leadership Community is excited to host Karen Meppen of Hakkoda and DPLC founding member to discuss Immature Data, and Immature Clients: Are Data Products the Right Approach? We'll hear from Karen and then participate in an open dialog. Like all DPLC live sessions, it's fully recorded and transcribed too, and you can keep the chat going in our 24-7 Slack. For members only.
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My reactions to the Chief Data Officer, Fall 2017 conference summary

I ran into a an article about the Chief Data & Analytics Officer, Fall conference that summarized some of the key takeaways at the previous year's conference. One paragraph in the article stuck out to me:

...
The Great Dilemma – Product vs Project vs Capability Analytics Approaches
Although not one of these approaches will provide a universal solution, organisation’s must be clear on which avenue they’d like to take when employing enterprise analytics. Many speakers discussed the notion of analytics as a product/service, and the importance in marketing that product/service to maximise buy-in and adoption. However, analytics executives may look to take a capability-based approach, but one cannot simply build an arsenal of analytics capabilities without a clearly defined purpose and value generated for the business...

(Bolding added by me)

For companies pursuing internal analytics solutions, or creating externally-facing data products or solutions, the situation is basically the same: you cannot start with a bunch of data and metrics, visualize it, and then hope that you have a product/solution somebody cares about. The data isn't what is interesting: it is the actions or strategic planning one can take from the data that holds the value. You have to design the data into information, in order to get it to the point customers can grok this value.

I have found engineering-lead organizations that tend to operate in the "build first, find problem second" method, looking at design as something you bring in at the end to "make it look all pretty and nice." A good UX strategy is a good product strategy is a good analytics strategy: by spending time to understand the latent needs people have for your analytics/data up front, you're much more likely to generate a solution that solves for a need on the other side.

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