The Twin Cities Data Fluency Group had its second meeting in May. This month involved an engaging discussion on “How the business can better work with analytics and tech partners.” Tricia Duncan and myself (Dave Mathias) moderated three great panelists – Nate Hallquist from Syngenta, Serena Roberts from Capella, and Jack Vishneski from ThreeBridge and consulting with Cargill.
There was a lively discussion on several fronts, but key takeaways were as follows:
Building relationships is key. Most information work takes teams and that means working with people. The more you build relationships the better chance to succeed as Nate mentioned.
Bring everything back to problem being solved. Data and analytics only serve a purpose if they solve problems. As Jack succinctly mentioned it is all about solving problems and bringing conversations back to those problems will help ensure success.
Trust is key. As Serena mentioned being a trusted advisor as an analyst and business partner alike is a must. Serena has the unique experience playing both roles in sales and sales enablement and building trust with both these hats has been essential to her success.
Rapid prototyping should be norm. Rapid prototyping is a must for dashboards and both to help ensure customer satisfaction and efficiency. These rapid prototypes can be done in a dashboard tool if a similar dataset available but just as nice it can be hand drawn on a whiteboard or paper.
In addition to these takeaways, there was a good discussion on the role of self-service business intelligence (BI) and how much autonomy the business should have and how much of it stays in the analyst, data science, or technology hands. There was mixed feeling here both on panel and in audience. Some companies have shown more success than others in distributing data fluency and technology into the business. However, there was agreement that tools are making it more able for end users to do more challenging problems.
One metaphor that seemed to resonate is treating self-service BI as a grocery store and not a treasure chest can help. As Nate described this the analyst, technology, or data science groups ensure that often used data has been made available with appropriate cleaning, integrity, and trust to business users. However, organizations need to ensure end users have proper training, tools, and help available so they can focus on conversations and insights while reducing the risk of invalid data models or technical debt.
There was a lot of overall agreement that data fluency is critical for organizations broadly and the language of data will be more easily picked up by some than others. But, to have a data-driven or data-informed culture at an organization requires your people to be data fluent.
This is a short summary of the great discussion that occurred, and all are welcome to attend the next TC Data Fluency MeetUp will be in July (date TBD). If you are an analyst or data scientist, then this is a great opportunity to bring one or more of your business partners to help further your relationship.
Thank you to Nate, Serena, Jack, and everyone that attended, and Tricia, Nate, and I hope to see you in July.
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