Just as beverage companies once managed their packaging waste, tech companies were once more straightforward about their data practices. But now, we’re seeing a troubling pattern of companies quietly expanding their data collection through obscure settings and opt-out mechanisms buried in lengthy terms of service.
Read MoreA student declaration of rights: Protecting Education in the Age of AI
Today, I'm sharing news of an initiative occupying my thoughts and research: the Global Student & AI Rights Pledge & Declaration. These are two initiatives I architected as part of presentations I gave this year at ICTE 2024. But before I detail these efforts, I want to emphasize something important: this isn't just another policy document destined to gather digital dust in some institutional repository.
Read MoreCultivating a Data Ecosystem: A Fresh Approach to Organizational Data Management
Many organizations have recognized the need to enhance their data management capabilities. The traditional response has been to centralize these efforts, often by appointing a Chief Data Officer (CDO). However, this well-intentioned approach often leads to challenges and resistance within the organization.
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Anticipating Resistance: A Proactive Approach to Data Project Success
In data leadership, resistance to change is often viewed as an inevitable hurdle to overcome. Successful data leaders should reframe that paradigm to planning for resistance before it occurs. This proactive approach not only smooths the path for project implementation but also fosters a culture of open communication and mutual understanding among their stakeholders.
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.
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