Vol. 1: Real-Time Crime Trends and Multinational Giants

Welcome to Gini Data’s first newsletter, where we dive deep into some of the most interesting datasets we’ve found and (hopefully!) spark creativity with the insights you receive. Every dataset tells a story, and this week, we’re pulling back the curtain on real-time crime data and multinational enterprise structures.

Dataset #1: Real-Time Crime Trends

🤔 Why It’s Interesting 
Did you know the FBI’s crime reports, often used for policy making and more, are up to 33 months behind? That’s a long time to wait for actionable data.

Enter the Real-Time Crime Index (RTCI), which fills the gap by offering monthly updates on crime data from hundreds of U.S. cities, with records going back to January 2018.

While it’s just a sampling (we know how inconsistent government reporting can be), it already covers 349 cities—more than enough to start identifying trends and making data-driven decisions today.

📊 Data Sample

^{Source: Real-Time Crime Index}

💡 What could you do with this data?

• Example #1: Location strategy: Assess crime trends when choosing where to expand or open a new office.

• Example #2: Consumer behavior: See how local crime patterns might impact your customers' habits and brand perception.

• Example #3: Real estate decisions: Use crime data to inform investment decisions, identify up-and-coming areas, or assess operational risks.

Dataset #2: Multinational Enterprise Structures

🤔 Why It’s Interesting
Did you know some of the world’s largest companies, like Alphabet and Nike, are composed of hundreds of subsidiaries?

The Organisation for Economic Co-operation and Development (OECD) and the United Nations Statistics Division (UNSD) partnered to build the Multinational Enterprise Information Platform, giving us a rare glimpse into the structures of these massive entities.

For example, did you know Amazon has 192 subsidiaries? Or that Pepsi owns Quaker Oats (a match made in heaven...)?

Their platform provides detailed insights into the top 500 multinational enterprises, covering headquarters, subsidiaries, ownership structures, and even website rankings.

This data isn’t just interesting — it shows the true scale and complexity of some of these companies’ operations and their interconnected business networks.

📊 Data Sample

^{Source: OECD-UNSD Multinational Enterprise Information Platform}

💡 What could you do with this data?

• Example #1: Market and Competitive Research: Map out competitors' organizational structures and understand their global footprint.

• Example #2: Supply Chain Analysis: Gain insights into subsidiaries and regional operations to better assess supply chain risks or partnerships.

• Example #3: Tax Strategy: Use the data to optimize international tax strategies, identifying tax-efficient jurisdictions where certain MNE subsidiaries are located.

That’s it for this week! Loved this data? Let us know how you’re using it, or feel free to give us feedback. Have a dataset you’d love to see us explore? Reply to this email and let us know!

More coming your way soon,
Jessica & Gus