January 10, 2022
What...is the Deal...with THAT?!?!
A light-hearted list of things that happen in the real world when building a data-driven culture.
3 min read
A Seinfeld-esque list of real-world ironies that occur while you build a data-led organization.
A little late on the drop, I was beyond excited when I learned that the entirety of Seinfeld was added to my Netflix lineup (in October ‘21). Throughout December—while cooking or decompressing for a quick 20 minutes before a non-blue light bedtime routine—I dug in. Despite every episode being a repeat to me, it has not disappointed. A show “about nothing” is actually a bizarre-but-accurate interpretation of everyday life. Its “what is the deal with” humor of the 90s got me thinking: what is the equivalent in the “everyday” life of organizations as they try to shift to a data-driven culture?
If you play any role in such an organization, I hope one or more things on this light-hearted list resonate for you…and you work to erase it from the list!
The team or org has made significant investments in analytics but the favorite mode of sharing findings is…a screenshot in an email. Oof.
Your organization commits to being data-led and each business unit leader gets to invest in new SaaS products tailored specifically for sales, marketing, HR, etc., complete with “stock” analytics. Later they realize they are each stuck using siloed dashboards without API extraction by their BI team.
Like above, but the company opts to use existing systems for data. They have decentralized analysts in each department start building dashboards in different software. Business units are “analytics rich” but still have a hard time sharing/integrating insights to connect the dots.
Your most important meetings are driven by analytics and metrics…that you still share by having your team create a Powerpoint or PDF for distribution.
Your company goes full throttle into being a data-driven culture. So much, that you end up with too many dashboards and stop looking at any of them. Doh!
There’s an intense focus on data quality, but visible, usable quality control analytics for end-users are rare “in the wild”. Teams have no confidence in what they are using. They actually move slower because most stakeholders independently spend time validating the data.
For you cloud Tech Ops folks: when data and analytics are growing (good!) so much in your company that data storage/processing takes a backseat and you actually spend more money on powerful servers to handle the load. (In our team this is known as “cranking it up to 11”)
You know that you have access to the metric you want…you just have no clue where it is or how to find it.
As a decision-maker and leader you were less than thrilled when the one-note analytics strategy was to deploy “self-service” analytics software. You spend far more time now figuring out how to use visualization software than making valuable decisions.
For the analytics builders: the powerful, integrated analytics you have been building has just needed “2 more features” before it can be used by company leadership…for the last 3.5 years.
To all of these I ask in my most cynical Seinfeld voice: “What, is the deal, with THAT?!”
I hope you had a laugh with me (and a nervous twitch, as I’ve experienced all of these and more) while reading these. But, if any hit too close to home, I do hope you and your teams consider how you can revisit these topics. Try to reduce friction so your organization can make progress—not perfection—on your journey to be effectively data-driven. I would love to hear your own stories and experiences – reach out anytime!
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