As I considered how to promote my new book on driving data projects, I wanted to include myths and misconceptions that reinforce their value. I have experienced many of these in teams I’ve worked on or with. Data projects are not a static set of routines. It's a constantly evolving, open-to-innovation process.

Only 54 percent of organizations fully understand the value of project management, according to PMI's Pulse of the Profession™ report. That might explain, in part, why project success rates are so low: Less than two-thirds meet their original business intents.

I read somewhere that over 80% of adults want to write a book, but only 3% ever get to 'The End' of a draft. That means that 97% of people who want to write a book never finish.

Stats like that make accomplishments like getting my “author’s box” of books all the more rewarding.

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.

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.

Employees have been encouraged to ‘automate their roles’ to demonstrate self-direction and continuous learning. In the past, an employee's skills, motivation, and business interests determined the pace of change. Soon, the pace may be beyond their control, risking job loss before they can adapt to consider the next set of problems. If they can’t find problems faster than the pace of automation, they are not adequately prepared for transition.

We often consider digital technologies like data platforms, AI, and copilot features as tools. But if we're rethinking the future of work and the future of careers and companies, it's helpful to think of these things as augmenting our efforts. For a copilot in particular, it becomes a junior coworker or maybe a more senior co-worker as the AI skills get better.

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.

People who work in data management are particularly dedicated problem solvers. They are committed to the mission in a way that makes them want to make the initiative successful. Most examples featured in the book reflect what happens in a specific type of data project -- a team-based project with stakeholders recruited from across the organization, including outside partnerships.

Change management wouldn’t be so hard if it weren’t for…the people. Open issues or objections left unresolved today cost time down the road. Suppose work starts before these concerns are mitigated. Stakeholders might get frustrated or begin to hold back their participation. Work produced might have difficulty getting implemented. Buy-in realizes impact.