When driving data projects, you will encounter business stakeholder challenges that often go unspoken. This is not always because people hold back but because they don't fully know how to vocalize their constraints.
If they can't directly address their requirement, chances are we can't either. To hear others' speech, we start by asking questions from different perspectives.
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Since most people don’t know a lot about IT organizations or data teams it’s important to understand why moving from ad hoc efforts to a mature approach to driving data projects makes sense. The timing might not be right (now). Becoming data-driven through data as a service requires a serious investment of resources, finances, staff, equipment, services, etc.; scaling efforts will only increase those topline demands. It’s a serious ongoing commitment many organizations find themselves surprised by—even today.
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Data teams should be regarded as intentional business partners because they provide the underlying technology that enables business strategy and maintains data as a corporate asset. They can help educate business partners on the upstream and downstream impacts of poor data quality, and they can help cultivate more effective ambassadors for data governance across the organization.
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Most data teams cover WHAT and HOW with standard reports and KPIs. They will optimize processes and analyze business domains impacting the company's bottom line from a data perspective.
But how many data teams truly understand the WHY behind the reports they generate? How many actively consult with the business as a true partner to understand the underlying business concerns behind the numbers? Without the WHY, delivering true value in the WHAT and HOW is ten times harder.
With tell-me features and AI Tools, we forget the importance of and lose ambidexterity skills like managing details and thinking strategically. Additionally, the emphasis used to be on being able to talk to the box and not the people; now, we must reason with the box (and the people). Skills cultivating engaged stakeholders and executive sponsors weren’t emphasized as much but are now increasingly important. Those are radically different skills!
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Data analytics is filled with complexity. Anyone saying otherwise is selling products. Knowing the data sources, data sets, general lineage, and behavior of the numbers are table stakes for the average data consumer. We must know where our data comes from. Much like we need to know where our food comes from and how it's processed. Is it safe to consume?
Lately, I’ve heard many stories about early career folks with data analyst titles turning to ChatGBT for help because they don't know where to go with questions. ChatGBT should only be used when the output can be rigorously challenged, which can only happen if you have the foundational knowledge of how the output was generated. Here are some handy Do’s and Don’ts to remember before turning to ChatGBT.
After teaching informatics for seven years, I’ve got a new book coming out! Driving Data Projects.
It’s a love letter to my students and a guide to my fellow colleagues. Many employees seek out or are thrust into a series of responsibilities in data management for which there is little formal training. How they engage with data in those roles impacts the privacy and security of consumer data and overall risk to the company's bottom line. The problem?
They aren’t quite sure how data works or how to drive data projects, not really. Today, almost all projects involve data to some degree, yet the data aspect is not adequately addressed.