Analytical Depth & Power
There is a ton of important data work that simply cannot be done inside of BI tools. Predictive modeling, complex statistical analyses, geospatial work, unstructured data, machine learning— The list of important analysis that can’t be done in a SQL based BI tool is actually pretty long (View Highlight)
As a data team ventures more and more into complex data projects, they’ll have to expand into using Python, R, and other tools like notebooks and SQL IDEs. The data chaos that a BI tool was intended to extinguish will begin to rear its head again, with analysis being done in local environments, csv files being emailed back and forth, and nobody ever sure which version is the final one that should be used in the board meeting. (View Highlight)
The truth is that if you’re trying to quantify your impact by yourself, you have already lost. Instead, the best way to tell the ROI story is for other people to tell it.
If your Data team is truly providing value, the leaders of other functions should be lining up to sing your song. Limitations or reductions in Data team headcount should elicit howls from functional stakeholders (View Highlight)
If your partners aren’t willing to go to bat for you like this, then it’s time to take a step back to rethink how you’re operating. Are other teams actually benefiting from your work, or are you detached from business outcomes? Is your team in the trenches with other functions, or only providing input from afar? (View Highlight)
Too many data teams operate in a centralized, siloed manner. “Ivory Tower” teams may be doing brilliant, insightful work, but they’re too far from the business to make a tangible impact. (View Highlight)
Next, integrate your planning process. If your organization uses a system like OKRs, explicitly tie the Data objectives to support the goals of your stakeholders. This makes it clear exactly how your team is impacting functional outcomes. (View Highlight)
Infrastructure-level objectives — like implementing a new data warehouse — can live separately, but should still have explicit callouts for how those investments are supporting the higher-level objectives. (View Highlight)
Data Leaders should also push for their teams to be involved with other teams’ granular planning cycles. If the Marketing team has a weekly planning meeting or daily stand-ups, the Data analysts supporting that team should be in the room (View Highlight)
