One of the conversations I love most is around organizations' data strategies:
A Product Management team wants to strengthen their backlog grooming inputs by leveraging data from other departmental tools like ZenDesk (Customer Support), Hubspot & Google Analytics (Marketing), SalesForce (Sales), as well as Product Management tools like Pendo.
One company is hiring no less than 4 Business Analysts across 3 departments but worries that the new hires will be spending most of their time on data discovery and data prep, costing months’ worth of delays (and the associated lost salary/financial overhead) in finding insights.
Another organization is locked in a stalemate with users over a recent SAP migration to the cloud—users don’t “believe” that all their data and reports are going to be trustworthy after the migration, meaning that they’re paying for two redundant (and very expensive) systems.
We find that three things commonly contribute to the inertia and gridlock in these situations:
Lack of data-centric terminology and vocabulary;
Lack of insight into what’s possible;
Lack of user visibility and examples about what a data tools and solutions look like.
We will be posting more articles about these Data Basics over the coming weeks, with the goal of enabling companies to identify issues like the ones above and begin to overcome the inertia that we often see before solutions become mainstream.