To succeed, marketers must stop passively accepting the data they're given. Instead, they must proactively partner with IT and privacy teams to advocate for the specific data collection and governance required to power their growth and personalization initiatives.

Data governance is often seen as a cost center. Reframe it as an enabler of revenue by showing how trusted, standardized data reduces the "idea to insight" cycle. This allows executives to make faster, more confident decisions that drive growth and secure buy-in.

Data has become a primary means of production alongside capital and labor. Following historical parallels with agricultural co-ops and labor unions, communities will likely form "data unions" to pool their data, enabling collective bargaining with large corporations and restoring individual power.

Many leaders focus on data for backward-looking reporting, treating it like infrastructure. The real value comes from using data strategically for prediction and prescription. This requires foundational investment in technology, architecture, and machine learning capabilities to forecast what will happen and what actions to take.

Treat government programs as experiments. Define success metrics upfront and set a firm deadline. If the program fails to achieve its stated goals by that date, it should be automatically disbanded rather than being given more funding. This enforces accountability.

In siloed government environments, pushing for change fails. The effective strategy is to involve agency leaders directly in the process. By presenting data, establishing a common goal (serving the citizen), and giving them a voice in what gets built, they transition from roadblocks to champions.

Instead of starting with available data, marketers should first identify and rank key business decisions by their potential financial impact. This decision-first approach ensures data collection and analysis efforts are focused on what truly drives business value, preventing 'analysis paralysis' and resource waste.

By first helping government agencies craft regulations, a startup gains deep expertise and credibility. This naturally leads to high-value inbound interest from private sector firms needing help complying with those same regulations, creating a powerful two-sided market flywheel with built-in demand.

The Data Nutrition Project discovered that the act of preparing a 'nutrition label' forces data creators to scrutinize their own methods. This anticipatory accountability leads them to make better decisions and improve the dataset's quality, not just document its existing flaws.