The symbiotic relationship between technical analytics development and business utilization underscores the heightened emphasis on data literacy skills. Recognizing that literacy demands effort from technical and business domains, analysts must simplify and convey insights while business teams must effectively apply them.
Read MoreAll Technology Projects are Data Projects
One of the biggest ideas in Driving Data Projects (the book) is that "all technology projects are data projects." Yet data is still an afterthought in many organizations—even with AI on the horizon (or now, in many firms' backyards).
Author of Data Quality: The Field Guide, Tom Redman, popularized the idea that the most important moments in a piece of data's lifetime are the moment it is created and the moment it is used. These moments often occur outside of IT. The business consumes vast amounts of data, emphasizing the importance of business involvement in data quality management. Those who have provisioned and consumed data know from experience that bad data dies hard. It will get rid of you if you don't get rid of it.
Read MoreIs it ARTIFICIAL intelligence or AUGMENTED intelligence?
Is it ARTIFICIAL intelligence or AUGMENTED intelligence?
The truth? It depends on the design's purpose. An organization’s purpose is informed by its values and profit motivation. Artificial intelligence aims to create autonomous systems that can perform tasks without human intervention, while augmented intelligence seeks to enhance human capabilities by providing AI-powered tools and assistance.
Read More4 Perspectives to drive effective data translation
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.
Read MoreData Trend: From Spreadsheets to Algorithms
The transition from traditional spreadsheets to sophisticated data management and analysis algorithms represents a significant evolution that has revolutionized how businesses process and leverage information. Algorithms have reshaped the landscape of data-driven decision-making. Facebook's filter bubble is an early example of a machine learning system individualizing the user experience based on user patterns.
Read MoreData Projects: Tips and Challenges
As we continue to drive data projects, familiar challenges begin to present themselves. By observing, we can become better diagnosticians of systemic issues. Learn what to avoid and how to navigate them better.
Read MoreWriting a book is like getting a tattoo
Writing a book is a lot like getting a tattoo. It’s permanent. It marks a life transition. It's also 95% pain, 2.5% novelty, and 2.5% talking that novelty to others. It's proof I was able to push past the anxiety and deal with the pain to do something creative. Finishing is a wonderful feeling of accomplishment, proof that I made it past all the barriers.
Driving Data Projects is, first and foremost, a love letter to my students. The book highlights two main stumbling blocks they hit: understanding the data supply chain and their role in it and integrating key change management activities like working with executive sponsorship. I also observed these issues outside of the classroom--in almost every organization I've served. Everyone skins their knees on these issues--because they are hard.
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