As we conclude our exploration of the evolving data culture in corporate America, we find ourselves at a critical juncture. The pendulum swings we've observed—from the data-driven efficiency focus of the 1990s to the purpose-driven revolution of the 2010s—have set the stage for a new era of complexity. The advent of artificial intelligence (AI) is not just another technological advancement; it represents a fundamental shift in how organizations must approach skill development and organizational design for data, purpose, and ethics.
Read MorePart 4: The Trilemma of Modern Business: Navigating Data, Purpose, and Ethics in the AI Era
As we stand at the precipice of a new era in corporate evolution, the landscape before us is far more complex and nuanced than we could have imagined even a decade ago. The simple dichotomies of the past—efficiency versus humanity, data versus intuition—have given way to a trilemma that threatens to reshape the very foundations of organizational structure and leadership. This piece aims to unravel the intricate web of challenges facing modern businesses as they attempt to balance data-driven decision making, purpose-driven cultures, and the looming ethical considerations of the AI age.
Read MoreParadox of Purpose: How the Quest for Meaning Reshaped Data Culture and Leadership
As we entered the 2010s, corporate America underwent a seismic shift. The relentless pursuit of efficiency that characterized the 1990s and early 2000s gave way to a new paradigm—one that prioritized purpose and profit. While addressing crucial issues of employee burnout and societal expectations, this transformation inadvertently set in motion a chain of events that would profoundly impact data culture and leadership across organizations.
Read MoreChange Management in Data Projects: Why We Ignored It and Why We Can't Afford to Anymore
For decades, we've heard the same refrain: “Change management is crucial for project success.” Yet leaders have nodded politely and ignored this advice, particularly in data and technology initiatives. The result? According to McKinsey, a staggering 70% of change programs fail to achieve their goals. So why do we keep making the same mistake, and more importantly, why should we care now?
Read MoreBusiness and Technology Strategy Must Learn to Harmonize
The buzz around data and artificial intelligence (AI) often overshadows a fundamental truth: the core of any successful endeavor remains distinctly human. As businesses navigate the complexities of the digital age, the importance of human insight, empathy, and value-driven strategies becomes increasingly evident.
Read MoreTransparency and Explainability Don't Equal Trust
Trust is transitioning from institutional to "distributed," shifting authority from leaders to peers, which is often overlooked and perpetuates trust issues. If trust is predictable, it isn’t needed – is it? If the inner workings of AI, government, and the media were just more transparent, if we knew how they worked, we think we wouldn’t really need to “trust” so much. It would be more predictable.
Read More6 Myths and Misconceptions about Data Projects
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.
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Data 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.
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3 elements of effective sponsorship
A popular misconception of senior leadership is that effective executive sponsorship is a clearly understood skill. Many assume executives receive developmental feedback about becoming effective sponsors. Sadly, there is little training on sponsorship from middle management on up.
Leaders often accept sponsorship of an activity, not knowing what it entails. Some think it means sending a few enthusiastic emails about an initiative, propping up delegates in meetings, and moving on to the next thing. Some organizational cultures tolerate those actions as enough.
Linking projects to strategy
It can be challenging when stakeholders cannot translate business questions into technical requirements or do not provide enough context for data teams to do so. From there, the data team is often left to maintain the status of a series of ad hoc projects rather than connect these business questions to a larger more defined data strategy.
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